Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer

BackgroundIntensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) are standard physical technologies of stereotactic body radiotherapy (SBRT) that are used for patients with non-small-cell lung cancer (NSCLC). The treatment plan quality depends on the experience of the planner and is limited by planning time. An automated planning process can save time and ensure a high-quality plan. This study aimed to introduce and demonstrate an automated planning procedure for SBRT for patients with NSCLC based on machine-learning algorithms. The automated planning was conducted in two steps: (1) determining patient-specific optimized beam orientations; (2) calculating the organs at risk (OAR) dose achievable for a given patient and setting these dosimetric parameters as optimization objectives. A model was developed using data of historical expertise plans based on support vector regression. The study cohort comprised patients with NSCLC who were treated using SBRT. A training cohort (N = 125) was used to calculate the beam orientations and dosimetric parameters for the lung as functions of the geometrical feature of each case. These plan–geometry relationships were used in a validation cohort (N = 30) to automatically establish the SBRT plan. The automatically generated plans were compared with clinical plans established by an experienced planner.ResultsAll 30 automated plans (100%) fulfilled the dose criteria for OARs and planning target volume (PTV) coverage, and were deemed acceptable according to evaluation by experienced radiation oncologists. An automated plan increased the mean maximum dose for ribs (31.6 ± 19.9 Gy vs. 36.6 ± 18.1 Gy, P < 0.05). The minimum, maximum, and mean dose; homogeneity index; conformation index to PTV; doses to other organs; and the total monitor units showed no significant differences between manual plans established by experts and automated plans (P > 0.05). The hands-on planning time was reduced from 40–60 min to 10–15 min.ConclusionAn automated planning method using machine learning was proposed for NSCLC SBRT. Validation results showed that the proposed method decreased planning time without compromising plan quality. Plans generated by this method were acceptable for clinical use.

Related papers:

title: Is high-dose stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC) overkill? A systematic review.
abstract: For stereotactic body radiotherapy (SBRT), typically a scheme of 60 Gy in 3-8 fractions is applied, producing local tumour control rates around 90%. The dose specification is in one point only and ignores possible underdosages at the edge of the planning target volume (PTV). We investigated the doses at the edge of the PTV and correlated this with local tumour control with the aim to shed light on the radiation dose needed to eradicate stage I NSCLC.



Published data on the freedom from local progression (FFLP) data from SBRT and accelerated high-dose conventional radiotherapy series for stage I NSCLC with a follow up of at least 30 months were included. The EQD(2,T) was calculated from the dose at the periphery of the PTV.



Fifteen studies for SBRT (1076 patients) showed a median FFLP of 88.0±10.4% with a median EQD(2,T) of 76.9±17.4 Gy. The median FFLP was 87.6±6.0% for the accelerated schedules with an EQD(2,T) of 86.9±39.1 Gy, respectively. No significant relation was found between FFLP and the EQD(2,T) (p=0.23).



Several fractionated and accelerated schedules with equal biological doses achieve the same tumour control rates as SBRT. Lower, but more uniform doses to the whole PTV may be sufficient to achieve similar control rates, with the possibility to deliver SBRT in adapted schedules, beneficial to centrally located tumours in the vicinity of critical structures like the oesophagus and great vessels.

title: Stereotactic ablative radiotherapy versus lobectomy for operable stage I non-small-cell lung cancer: a pooled analysis of two randomised trials.
abstract: The standard of care for operable, stage I, non-small-cell lung cancer (NSCLC) is lobectomy with mediastinal lymph node dissection or sampling. Stereotactic ablative radiotherapy (SABR) for inoperable stage I NSCLC has shown promising results, but two independent, randomised, phase 3 trials of SABR in patients with operable stage I NSCLC (STARS and ROSEL) closed early due to slow accrual. We aimed to assess overall survival for SABR versus surgery by pooling data from these trials.



Eligible patients in the STARS and ROSEL studies were those with clinical T1-2a (<4 cm), N0M0, operable NSCLC. Patients were randomly assigned in a 1:1 ratio to SABR or lobectomy with mediastinal lymph node dissection or sampling. We did a pooled analysis in the intention-to-treat population using overall survival as the primary endpoint. Both trials are registered with ClinicalTrials.gov (STARS: NCT00840749; 
58 patients were enrolled and randomly assigned (31 to SABR and 27 to surgery). Median follow-up was 40·2 months (IQR 23·0-47·3) for the SABR group and 35·4 months (18·9-40·7) for the surgery group. Six patients in the surgery group died compared with one patient in the SABR group. Estimated overall survival at 3 years was 95% (95% CI 85-100) in the SABR group compared with 79% (64-97) in the surgery group (hazard ratio [HR] 0·14 [95% CI 0·017-1·190], log-rank p=0·037). Recurrence-free survival at 3 years was 86% (95% CI 74-100) in the SABR group and 80% (65-97) in the surgery group (HR 0·69 [95% CI 0·21-2·29], log-rank p=0·54). In the surgery group, one patient had regional nodal recurrence and two had distant metastases; in the SABR group, one patient had local recurrence, four had regional nodal recurrence, and one had distant metastases. Three (10%) patients in the SABR group had grade 3 treatment-related adverse events (three [10%] chest wall pain, two [6%] dyspnoea or cough, and one [3%] fatigue and rib fracture). No patients given SABR had grade 4 events or treatment-related death. In the surgery group, one (4%) patient died of surgical complications and 12 (44%) patients had grade 3-4 treatment-related adverse events. Grade 3 events occurring in more than one patient in the surgery group were dyspnoea (four [15%] patients), chest pain (four [15%] patients), and lung infections (two [7%]).



SABR could be an option for treating operable stage I NSCLC. Because of the small patient sample size and short follow-up, additional randomised studies comparing SABR with surgery in operable patients are warranted.

title: A dosimetric retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC)
abstract: A retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC). 
 
Five randomly selected early stage lung cancer patients were included in the study. For each patient, four plans were created: the SBRT plan and three VMAT plans using different optimisation methodologies. A total of 20 different plans were evaluated. The dose parameters of dose conformity results and the target dose constraints results were compared for these plans. 
 
The mean planning target volume (PTV) for all the plans (SBRT and VMAT) was 18·3 cm3, with a range from 15·6 to 20·1 cm3. The maximum dose tolerance to 1 cc of all the plans was within 140% (84 Gy) of the prescribed dose, and 95% of the PTV of all the plans received 100% of the prescribed dose (60 Gy). In all the plans, 99% of the PTV received a dose >90% of the prescribed dose, and the mean dose in all the plans ranged from 67 to 72 Gy. The planning target dose conformity for the SBRT and the VMAT (0°, 15° collimator single arc plans and dual arc) plans showed the tightness of the prescription isodose conformity to the target. 
 
Conclusions 
 
SBRT and VMAT are radiotherapy approaches that increase doses to small tumour targets without increasing doses to the organs at risk. Although VMAT offers an alternative to SBRT for NSCLC and the potential advantage of VMAT is the reduced treatment times over SBRT, the statistical results show that there was no significant difference between the SBRT and VMAT optimised plans in terms of dose conformity and organ-at-risk sparing.

title: IMRT: a review and preview.
abstract: The very first cornerstone paper on intensity-modulated radiation therapy (IMRT) was published in Physics in Medicine and Biology, and many seminal IMRT works have since appeared in this journal. Today IMRT is a widely used clinical treatment modality in many countries. This contribution to the 50th anniversary issue reviews the physical, mathematical, and technological milestones that have facilitated the clinical implementation and success of IMRT. In particular, the basic concepts and developments of both IMRT treatment planning ('inverse planning') and the delivery of cone-beam IMRT with a multileaf collimator from a fixed number of static beam directions are discussed. An outlook into the future of IMRT concludes the paper.

title: Treatment planning for volumetric modulated arc therapy.
abstract: Volumetric modulated arc therapy (VMAT) is a specific type of intensity-modulated radiation therapy (IMRT) in which the gantry speed, multileaf collimator (MLC) leaf position, and dose rate vary continuously during delivery. A treatment planning system for VMAT is presented.



Arc control points are created uniformly throughout one or more arcs. An iterative least-squares algorithm is used to generate a fluence profile at every control point. The control points are then grouped and all of the control points in a given group are used to approximate the fluence profiles. A direct-aperture optimization is then used to improve the solution, taking into account the allowed range of leaf motion of the MLC. Dose is calculated using a fast convolution algorithm and the motion between control points is approximated by 100 interpolated dose calculation points. The method has been applied to five cases, consisting of lung, rectum, prostate and seminal vesicles, prostate and pelvic lymph nodes, and head and neck. The resulting plans have been compared with segmental (step-and-shoot) IMRT and delivered and verified on an Elekta Synergy to ensure practicality.



For the lung, prostate and seminal vesicles, and rectum cases, VMAT provides a plan of similar quality to segmental IMRT but with faster delivery by up to a factor of 4. For the prostate and pelvic nodes and head-and-neck cases, the critical structure doses are reduced with VMAT, both of these cases having a longer delivery time than IMRT. The plans in general verify successfully, although the agreement between planned and measured doses is not very close for the more complex cases, particularly the head-and-neck case.



Depending upon the emphasis in the treatment planning, VMAT provides treatment plans which are higher in quality and/or faster to deliver than IMRT. The scheme described has been successfully introduced into clinical use.

title: Day-to-day reproducibility of prostate intrafraction motion assessed by multiple kV and MV imaging of implanted markers during treatment.
abstract: When one is performing online setup correction for prostate positioning displacements prior to daily dose delivery, intrafraction motion can become a limiting factor to prostate targeting accuracy. The aim of this study was to quantify and characterize prostate intrafraction motion assessed by multiple kilovoltage (kV) and megavoltage (MV) imaging of implanted markers during treatment in a large patient group.



Intrafraction motion in the sagittal plane was studied by retrospective analysis of displacements of implanted gold markers on (nearly) lateral kV and MV images obtained at various time points during the treatment fractions (mean, 27 per patient) in 108 consecutive patients. The effective prostate motion in a fraction was defined as the time-weighted mean displacement.



Prostate displacements in the sagittal plane increased during the fraction (mean, 0.2 ± 0.2 mm/min). Forty percent of patients had a systematic (i.e., appearing in all fractions) effective displacement in the sagittal plane greater than 2 mm. Observed effective population mean-of-means (μeff) +/- systematic (Σeff) intrafraction motion (μ(eff) ± Σ(eff)) was 0.9 ± 1.1 mm and 0.6 ± 1.0 mm for the anterior-posterior and superior inferior directions, respectively. Corresponding random motion (σ(eff)) was 1.2 mm and 1.1 mm. Mean effective prostate motion in the first 5 fractions was predictive for mean effective displacement in the remaining fractions (p < 0.001).



For a large subgroup of patients, the systematic component of intrafraction prostate motion was substantial. Intrafraction motion correction prior to each beam delivery or offline corrections could likely be beneficial for the subgroup of patients with significant motion. The systematic component is well predicted by measurements in the initial fractions.

title: Intensity-Modulated Radiation Therapy Dose Prescription, Recording, and Delivery: Patterns of Variability Among Institutions and Treatment Planning Systems
abstract: Of the 803 patients, 12% were treated for brain cancer, 26% for head and neck cancer, and 62% for prostate cancer. The recorded dose variability from prescription was widespread for the minimum, maximum, and isocenter doses. A total of 46% of the patients received a maximum dose that was more than 10% higher than the prescribed dose, and 63% of the patients received a dose that was more than 10% lower than the prescribed dose. At all five institutions, the prostate cancer cases had the smallest dosimetric variation and the head and neck cancer cases had the largest variation. The median dose to the target varied from the prescribed dose by ±2% in 68% of the patients, by ±5% in 88% of the patients, and by ±10% in 96% of the patients. The recorded isocenter dose varied from prescription for all disease sites and treatment planning systems.  Substantial variation in the prescribed and delivered doses exists among medical institutions, raising concerns about the validity of comparing clinical outcomes for IMRT. The isocenter dose in IMRT is simply a point dose and often does not reflect the prescription dose that is specified by a selected isodose line encompassing the target volume. This study suggests the need for national and/or international guidelines for dose prescription, planning, and reporting for a meaningful clinical trial in IMRT. J Natl Cancer Inst 2008;100: 300 – 307 An improvement in radiation therapy outcomes could be achieved by periodic comparisons of clinical practices through outcome evaluations from clinical trials and studies. For a multicenter study, a meaningful comparison of clinical outcomes in response to radiation treatment requires a standardized process for dose specifi cation. Treatment outcome can be interpreted meaningfully only with accurate knowledge of the reference dose and the dose distribution. National guidelines for clinical reference dosimetry, such as those put forth by Task Groups 21 and 51 of the Radiation Therapy Committee of the American Association of Physicists in Medicine ( 1 , 2 ), recommend that the reference dose (machine output) should not vary by more than ±2% among centers. For patient treatment, the combined dosimetric uncertainty in the target volume (which includes differences in patient setup and

title: Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems.
abstract: This study quantifies variation in radiation treatment plan quality for plans generated by a population of treatment planners given very specific plan objectives.



A "Plan Quality Metric" (PQM) with 14 submetrics, each with a unique value function, was defined for a prostate treatment plan, serving as specific goals of a hypothetical "virtual physician." The exact PQM logic was distributed to a population of treatment planners (to remove ambiguity of plan goals or plan assessment methodology) as was a predefined computed tomographic image set and anatomic structure set (to remove anatomy delineation as a variable). Treatment planners used their clinical treatment planning system (TPS) to generate their best plan based on the specified goals and submitted their results for analysis.



One hundred forty datasets were received and 125 plans accepted and analyzed. There was wide variability in treatment plan quality (defined as the ability of the planners and plans to meet the specified goals) quantified by the PQM. Despite the variability, the resulting PQM distributions showed no statistically significant difference between TPS employed, modality (intensity modulated radiation therapy versus arc), or education and certification status of the planner. The PQM results showed negligible correlation to number of beam angles, total monitor units, years of experience of the planner, or planner confidence.



The ability of the treatment planners to meet the specified plan objectives (as quantified by the PQM) exhibited no statistical dependence on technologic parameters (TPS, modality, plan complexity), nor was the plan quality statistically different based on planner demographics (years of experience, confidence, certification, and education). Therefore, the wide variation in plan quality could be attributed to a general "planner skill" category that would lend itself to processes of continual improvement where best practices could be derived and disseminated to improve the mean quality and minimize the variation in any population of treatment planners.

title: A sensitivity-guided algorithm for automated determination of IMRT objective function parameters.
abstract: Optimizing intensity-modulated radiotherapy (IMRT) plans involves tradeoffs that balance normal-tissue objectives against each other and against tumor objectives. Adjusting the parameters that determine the appropriate contributions of individual anatomic structures to the objective functions through trial and error is time consuming and may not produce the best achievable plans. We have developed a sensitivity-guided parameter optimization (SGPO) method to assist in the automatic determination of parameters to drive the IMRT optimization to better achieve, or even exceed, specified planning goals. The method is based on the trade-off relationships among multiple objectives: In a globally optimal plan (or within a convex subspace of the plan objectives), any attempt to improve the achievement of goals for a structure will result in sacrificing the goals for at least one other structure. However, different objectives may have different sensitivities to the overall goal of an IMRT plan. For instance, changes in dose distribution, hence the subscore corresponding to an objective for a given normal structure, may minimally impact the target dose distribution. Stated differently, the target coverage is insensitive to the changes in dose distribution of the specific normal structure. A lung cancer treatment plan designed with the SGPO method was used to demonstrate that IMRT plans could be designed to favor a structure with the highest target sensitivity and spare the structures with the least target sensitivity without compromising the target coverage. Using one case each of prostate and paranasal sinus cancers, we also demonstrated that several alternative optimal solutions could be designed with the SGPO algorithm favoring different structures. Finally, we applied the method to eight oropharyngeal cancer cases to obtain objective function parameters that satisfied the Radiation Therapy Oncology Group RTOG-H-0022 protocol. The eight plans optimized using the computer-generated objective function parameters met the protocol's scoring criteria with no or only minor protocol violations. Our preliminary study indicates that the SGPO method may be an effective and practical way to improve IMRT planning.

title: Automatic interactive optimization for volumetric modulated arc therapy planning
abstract: BackgroundIntensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.MethodsOur current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times.ResultsAverage V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%.ConclusionsUsing the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans.

title: Fully automatic volumetric modulated arc therapy plan generation for rectal cancer.
abstract: To develop and evaluate a fully automatic rectal planning optimizer (ARPO) for volumetric modulated arc therapy (VMAT) treatment planning without human interaction.



The ARPO was developed using inherent Pinnacle(3) script language; it was designed to perform the whole planning process including planning structure generation, beam placement, doseline setting and treatment planning. The automatic scheme adjusts the objectives of the objective function simulating the operation of dosimetrists based on our clinical experience. A total of 29 planned rectal cancer patients were retrospectively replanned using the ARPO (VMATauto) under the same constraints.



With the ARPO, the hands-on time required for the whole planning process was significantly reduced to <1min. All VMATauto plans were recognized as clinically acceptable and 69% as clinically improved; 3% of VMATauto plans were marked equal and 28% inferior to manually generated VMATman plans when reviewed in a single-blind study by one experienced radiation oncologist. Without any planning workload the VMATauto plans had similar planning target volume dose coverage to the VMATman plans and statistically better organ-at-risk sparing, especially regarding lower small intestine irradiation.



The ARPO is robust and dramatically efficient in clinical application and provides improved planning quality.

title: iCycle: Integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans.
abstract: To introduce iCycle, a novel algorithm for integrated, multicriterial optimization of beam angles, and intensity modulated radiotherapy (IMRT) profiles.



A multicriterial plan optimization with iCycle is based on a prescription called wish-list, containing hard constraints and objectives with ascribed priorities. Priorities are ordinal parameters used for relative importance ranking of the objectives. The higher an objective priority is, the higher the probability that the corresponding objective will be met. Beam directions are selected from an input set of candidate directions. Input sets can be restricted, e.g., to allow only generation of coplanar plans, or to avoid collisions between patient/couch and the gantry in a noncoplanar setup. Obtaining clinically feasible calculation times was an important design criterium for development of iCycle. This could be realized by sequentially adding beams to the treatment plan in an iterative procedure. Each iteration loop starts with selection of the optimal direction to be added. Then, a Pareto-optimal IMRT plan is generated for the (fixed) beam setup that includes all so far selected directions, using a previously published algorithm for multicriterial optimization of fluence profiles for a fixed beam arrangement Breedveld et al. . To select the next direction, each not yet selected candidate direction is temporarily added to the plan and an optimization problem, derived from the Lagrangian obtained from the just performed optimization for establishing the Pareto-optimal plan, is solved. For each patient, a single one-beam, two-beam, three-beam, etc. Pareto-optimal plan is generated until addition of beams does no longer result in significant plan quality improvement. Plan generation with iCycle is fully automated.



Performance and characteristics of iCycle are demonstrated by generating plans for a maxillary sinus case, a cervical cancer patient, and a liver patient treated with SBRT. Plans generated with beam angle optimization did better meet the clinical goals than equiangular or manually selected configurations. For the maxillary sinus and liver cases, significant improvements for noncoplanar setups were seen. The cervix case showed that also in IMRT with coplanar setups, beam angle optimization with iCycle may improve plan quality. Computation times for coplanar plans were around 1-2 h and for noncoplanar plans 4-7 h, depending on the number of beams and the complexity of the site.



Integrated beam angle and profile optimization with iCycle may result in significant improvements in treatment plan quality. Due to automation, the plan generation workload is minimal. Clinical application has started.

title: An approach to multiobjective optimization of rotational therapy.
abstract: Multiobjective optimization is used in radiotherapy, especially IMRT, to generate treatment plans which meet different objectives to varying extents. Trade-off surfaces can be constructed representing the gains and losses of different objectives when switching from one plan to another, and the planner can interactively explore different treatment possibilities without the need for reoptimization. In this work a method for the multiobjective optimization of rotational therapy is introduced. The proposed method is applied slice per slice and uses the geometry of the slice directly to construct several arcs, each conformally irradiating the tumor and blocking a number (0,1,2,...) of different organs at risk present in the treatment. The blocked arc dose distributions so obtained are quite inhomogeneous in the target. An algorithm, based on the iterative reconstruction of images from projections, has been developed to compensate for this inhomogeneity, leading to compensated blocked arcs which deliver more uniform target doses but still block critical structures. Different treatments can be obtained as linear combinations of these arcs, each involving different trade-offs among the objectives involved. The compensatory algorithm substantially improves the target dose uniformity of blocked arcs at the cost of slightly increasing the dose to the rest of the body, allowing delivery of good uniform dose distributions to the target without significantly irradiating the blocked organ(s). Trade-off surfaces are presented for slices containing a target and one or two critical structures. The method is directly implementable using axial or helical tomotherapy. Implementation for conventional linear accelerators will be more difficult because the number of arcs needed to deliver such treatments can be large, an issue to be explored in future work.

title: An approach to multiobjective optimization of rotational therapy. II. Pareto optimal surfaces and linear combinations of modulated blocked arcs for a prostate geometry.
abstract: The purpose of this work is twofold: To further develop an approach to multiobjective optimization of rotational therapy treatments recently introduced by the authors [An approach to multiobjective optimization of rotational therapy," Med. Phys. 36, 3292-3303 (2009)], especially regarding its application to realistic geometries, and to study the quality (Pareto optimality) of plans obtained using such an approach by comparing them with Pareto optimal plans obtained through inverse planning.



In the previous work of the authors, a methodology is proposed for constructing a large number of plans, with different compromises between the objectives involved, from a small number of geometrically based arcs, each arc prioritizing different objectives. Here, this method has been further developed and studied. Two different techniques for constructing these arcs are investigated, one based on image-reconstruction algorithms and the other based on more common gradient-descent algorithms. The difficulty of dealing with organs abutting the target, briefly reported in previous work of the authors, has been investigated using partial OAR unblocking. Optimality of the solutions has been investigated by comparison with a Pareto front obtained from inverse planning. A relative Euclidean distance has been used to measure the distance of these plans to the Pareto front, and dose volume histogram comparisons have been used to gauge the clinical impact of these distances. A prostate geometry has been used for the study.



For geometries where a blocked OAR abuts the target, moderate OAR unblocking can substantially improve target dose distribution and minimize hot spots while not overly compromising dose sparing of the organ. Image-reconstruction type and gradient-descent blocked-arc computations generate similar results. The Pareto front for the prostate geometry, reconstructed using a large number of inverse plans, presents a hockey-stick shape comprising two regions: One where the dose to the target is close to prescription and trade-offs can be made between doses to the organs at risk and (small) changes in target dose, and one where very substantial rectal sparing is achieved at the cost of large target underdosage. Plans computed following the approach using a conformal arc and four blocked arcs generally lie close to the Pareto front, although distances of some plans from high gradient regions of the Pareto front can be greater. Only around 12% of plans lie a relative Euclidean distance of 0.15 or greater from the Pareto front. Using the alternative distance measure of Craft ["Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization," Phys. Medica (to be published)], around 2/5 of plans lie more than 0.05 from the front. Computation of blocked arcs is quite fast, the algorithms requiring 35%-80% of the running time per iteration needed for conventional inverse plan computation.



The geometry-based arc approach to multicriteria optimization of rotational therapy allows solutions to be obtained that lie close to the Pareto front. Both the image-reconstruction type and gradient-descent algorithms produce similar modulated arcs, the latter one perhaps being preferred because it is more easily implementable in standard treatment planning systems. Moderate unblocking provides a good way of dealing with OARs which abut the PTV. Optimization of geometry-based arcs is faster than usual inverse optimization of treatment plans, making this approach more rapid than an inverse-based Pareto front reconstruction.

title: Experience-based quality control of clinical intensity-modulated radiotherapy planning.
abstract: To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality.



A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 - exp(-3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script's effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model.



All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose <30 Gy). Before script implementation, an average of 30.1 Gy was delivered to the salvageable cases, with an average predicted dose of 20.3 Gy. After implementation, an average of 18.7 Gy was delivered to salvageable cases, with an average predicted dose of 17.2 Gy. In the prostate cases, the rectum model excess was reduced from δ = 0.28 ± 0.20 to δ = 0.07 ± 0.15. On surveying dosimetrists at the end of the study, most reported that the script both improved their IMRT planning (8 of 10) and increased their efficiency (6 of 10).



This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.

title: Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning.
abstract: To propose a method of intensity-modulated radiotherapy (IMRT) planning that generates achievable dose-volume histogram (DVH) objectives using a database containing geometric and dosimetric information of previous patients.



The overlap volume histogram (OVH) is used to compare the spatial relationships between the organs at risk and targets of a new patient with those of previous patients in a database. From the OVH analysis, the DVH objectives of the new patient were generated from the database and used as the initial planning goals. In a retrospective OVH-assisted planning demonstration, 15 patients were randomly selected from a database containing clinical plans (CPs) of 91 previous head-and-neck patients treated by a three-level IMRT-simultaneous integrated boost technique. OVH-assisted plans (OPs) were planned in a leave-one-out manner by a planner who had no knowledge of CPs. Thus, DVH objectives of an OP were generated from a subdatabase containing the information of the other 90 patients. Those DVH objectives were then used as the initial planning goals in IMRT optimization. Planning efficiency was evaluated by the number of clicks of the "Start Optimization" button in the course of planning. Although the Pinnacle(3) treatment planning system allows planners to interactively adjust the DVH parameters during optimization, planners in our institution have never used this function in planning.



The average clicks required for completing the CP and OP was 27.6 and 1.9, respectively (p <.00001); three OPs were finished within a single click. Ten more patient's cord + 4 mm reached the sparing goal D(0.1cc) <44 Gy (p <.0001), where D(0.1cc) represents the dose corresponding to 0.1 cc. For planning target volume uniformity, conformity, and other organ at risk sparing, the OPs were at least comparable with the CPs. Additionally, the averages of D(0.1cc) to the cord + 4 mm decreased by 6.9 Gy (p <.0001); averages of D(0.1cc) to the brainstem decreased by 7.7 Gy (p <.005). The averages of V(30 Gy) to the contralateral parotid decreased by 8.7% (p <.0001), where V(30 Gy) represents the percentage volume corresponding to 30 Gy.



The method heralds the possibility of automated IMRT planning.

title: A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.
abstract: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation.



Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information.



DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy approximately 80% in prediction and effectiveness in improving ART planning quality.



An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

title: Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma.
abstract: To develop a model to assess the quality of an IMRT treatment plan using data of prior patients with pancreatic adenocarcinoma.



The dose to an organ at risk (OAR) depends in large part on its orientation and distance to the planning target volume (PTV). A database of 33 previously treated patients with pancreatic cancer was queried to find patients with less favorable PTV-OAR configuration than a new case. The minimal achieved dose among the selected patients should also be achievable for the OAR of the new case. This way the achievable doses to the OARs of 25 randomly selected pancreas cancer patients were predicted. The patients were replanned to verify if the predicted dose could be achieved. The new plans were compared to their original clinical plans.



The predicted doses were achieved within 1 and 2 Gy for more than 82% and 94% of the patients, respectively, and were a good approximation of the minimal achievable doses. The improvement after replanning was 1.4 Gy (range 0-4.6 Gy) and 1.7 Gy (range 0-6.3 Gy) for the mean dose to the liver and the kidneys, respectively, without compromising target coverage or increasing radiation dose to the bowel, cord or stomach.



The model could accurately predict the achievable doses, leading to a considerable decrease in dose to the OARs and an increase in treatment planning efficiency.

title: Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study.
abstract: To investigate whether an overlap volume histogram (OVH)-driven planning application using an intensity-modulated radiation therapy (IMRT) database can guide and automate volumetric-modulated arc therapy (VMAT) planning for head-and-neck cancer.



Based on comparable head-and-neck dosimetric results between planner-generated VMAT and IMRT plans, an inhouse developed, OVH-driven automated planning application containing a database of prior clinical head-and-neck IMRT plans is built into Pinnacle(3) SmartArc for VMAT planning. Double-arc VMAT plans of four oropharynx, four nasopharynx, and four larynx patients are generated and compared with corresponding clinical IMRT plans.



Each VMAT plan is automatically generated in two optimization rounds, while the average number of optimization rounds in generating a clinical IMRT plan is 43. In VMAT plans, statistical superiority (p < 0.01) in sparing of the cord+4 mm, brainstem, brachial plexus, larynx, and inner ear is observed with a slight degradation in low-dose-level planning target volume (PTV) coverage. On average, D(0.1 cc) to the cord+4 mm, brainstem and brachial plexus is reduced by 3.7, 4.9, and 1.6 Gy, respectively; V(50 Gy) to the larynx is reduced by 5.3%; mean dose to the inner ear is reduced by 4.4 Gy; V(95) of low-dose-level PTV coverage is reduced by 0.3% with p = 0.25.



IMRT-data-driven VMAT planning offers a potential method for generating VMAT plans that are comparable to IMRT plans in terms of dosimetric quality.

title: An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection.
abstract: Hydrogel injected between the rectum and prostate prior to radiotherapy provides a possible means of increased dose sparing to the rectum. Here the authors evaluate the overlap volume histogram (OVH) metric as a means to predict the rectal dose following hydrogel injection. Whether OVH predicted dose can serve as the dose objective or constraint for automated treatment planning was also investigated.



Treatment planning was performed on 21 prostate cancer patients both pre- and posthydrogel injection, with five-field IMRT delivering 78 Gy to the planning target volume (PTV). The authors quantify the geometrical relationship between the rectum and the prostate PTV using an OVH metric which determines the fractional volume of the rectum that is within a specified distance of the PTV. For an OVH distance the authors selected, L(20), the PTV expansion distance at which 20% of the rectum overlaps. The authors calculated the rectal dose, D(20), received by 20% of the rectum volume on the dose volume histogram. Linear regression was used to examine the correlation between the L(20) and D(20), and between ΔL(20) and ΔD(20) (i.e., the change of L(20) and D(20) posthydrogel injection). Additionally, rectal dose D(15), D(25), D(35), D(50), and bladder dose D(15) were predicted from the OVH (L(15), L(25), L(35), L(50), for rectum and L(15) for bladder) by the L(x)-D(x) linear regression. The predicted doses were applied to the objectives for automated treatment planning of ten plans from five patients. Automatically generated plans were compared with plans manually generated on trial-and-error basis.



The rectal L(20) was increased and dose D(20) decreased due to the enlarged separation of rectum caused by the hydrogel injection. Linear regression showed an inverse linear correlation between L(20) and D(20), and between ΔL(20) and ΔD(20) (r(2) = 0.77, 0.60, respectively; p < 0.0001). The increase in rectal sparing (ΔD(20)) is only weakly correlated with the volume of injected hydrogel (r(2) = 0.17; p = 0.07), indicating OVH is a more predictive indicator of rectal sparing than the volume of hydrogel itself. Application of the predicted rectum and bladder doses to automated planning produced acceptable treatment plans, with rectal dose reduced for eight of ten plans.



The OVH metric can predict the rectal dose in the external beam prostate radiotherapy for patients with hydrogel injection. The predicted doses can be applied to the objectives of optimization in automated treatment planning to produce acceptable treatment plans.

title: A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites).
abstract: The quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans.



One hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test.



A voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set.



We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

title: Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy.
abstract: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients.



Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model.



With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis.



It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

title: Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
abstract: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time‐consuming and prone to inter‐observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)‐based method for fast and consistent auto‐segmentation of these structures.  Our DDCNN method was an end‐to‐end architecture enabling fast training and testing. Specifically, it employed a novel multiple‐scale convolutional architecture to extract multiple‐scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto‐segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel‐wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy.  Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U‐Net. The proposed DDCNN method outperformed the U‐Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U‐Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior performance and faster speed.  These data suggest that DDCNN can be used to segment the CTV and OARs accurately and efficiently. It was invariant to the body size, body shape, and age of the patients. DDCNN could improve the consistency of contouring and streamline radiotherapy workflows.

title: Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study
abstract: Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients.

title: Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.
abstract: Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality's information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.

title: Breast mass lesion classification in mammograms by transfer learning
abstract: Automatic classification of breast mass lesions in mammographic images remains an unsolved problem. This paper explored the technique of transfer learning to tackle this problem. It utilized the convolutional neural network (CNN) of GoogLeNet and AlexNet pre-trained on a large-scale visual database. The performance was evaluated a new dataset in terms of the area under the receiver operating characteristic curves (AUC). Results demonstrate that GoogLeNet (AUC=0.88) outperforms AlexNet (AUC=0.83) and other state-of-the-art traditional approaches in breast cancer diagnosis. The technique of transfer learning not only overcomes the unsatisfactory performance of traditional approaches, but also breaks the obstacle of limited samples for building deep CNNs.

title: Knowledge-based treatment planning and its potential role in the transition between treatment planning systems.
abstract: Commissioning a new treatment planning system (TPS) involves many time-consuming tasks. We investigated the role that knowledge-based planning (KBP) can play in aiding a clinic's transition to a new TPS. Sixty clinically treated prostate/prostate bed intensity-modulated radiation therapy (IMRT) plans were exported from an in-house TPS and were used to create a KBP model in a newly implemented commercial application. To determine the benefit that KBP may have in a TPS transition, the model was tested on 2 groups of patients. Group 1 consisted of the first 10 prostate/prostate bed patients treated in the commercial TPS after the transition from the in-house TPS. Group 2 consisted of 10 patients planned in the commercial TPS after 8 months of clinical use. The KBP-generated plan was compared with the clinically used plan in terms of plan quality (ability to meet planning objectives and overall dose metrics) and planning efficiency (time required to generate clinically acceptable plans). The KBP-generated plans provided a significantly improved target coverage (p = 0.01) compared with the clinically used plans for Group 1, but yielded plans of comparable target coverage to the clinically used plans for Group 2. For the organs at risk, the KBP-generated plans produced lower doses, on average, for every normal-tissue objective except for the maximum dose to 0.1 cc of rectum. The time needed for the KBP-generated plans ranged from 6 to 15 minutes compared to 30 to 150 and 15 to 60 minutes for manual planning in Groups 1 and 2, respectively. KBP is a promising tool to aid in the transition to a new TPS. Our study indicates that high-quality treatment plans could have been generated in the newly implemented TPS more efficiently compared with not using KBP. Even after 8 months of the clinical use, KBP still showed an increase in plan quality and planning efficiency compared with manual planning.

title: Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer.
abstract: Helical Tomotherapy (HT) plans were used to create two RapidPlan knowledge-based (KB) models to generate plans with different techniques and to guide the optimization in a different treatment planning system for prostate plans. Feasibility and performance of these models were evaluated.



two sets of 35 low risk (LR) and 30 intermediate risk (IR) prostate cancer cases who underwent HT treatments were selected to train RapidPlan models. The KB predicted constraints were used to perform new 20KB plans using RapidArc technique (KB-RA) (inter-technique validation), and to optimise 20 new HT (KB-HT) plans in the Tomoplan (inter-system validation). For each validation modality, KB plans were benchmarked with the manual plans created by an expert planner (EP).



RapidPlan was successfully configured using HT plans. The KB-RA plans fulfilled the clinical dose-volume requirements in 100% and 92% of cases for planning target volumes (PTVs) and organs at risk (OARs), respectively. For KB-HT plans these percentages were found to be a bit lower: 90% for PTVs and 86% for OARs. In comparison to EP plans, the KB-RA plans produced higher bladder doses for both LR and IR, and higher rectum doses for LR. KB-HT and EP plans produced similar results.



RapidPlan can be trained to create models by using plans of a different treatment modality. These models were suitable for generating clinically acceptable plans for inter-technique and inter-system applications. The use of KB models based on plans of consolidated technique could be useful with a new treatment modality.

title: Beam orientation optimization for IMRT by a hybrid method of the genetic algorithm and the simulated dynamics.
abstract: We have developed a new method for beam orientation optimization in intensity-modulated radiation therapy (IMRT). The problem of beam orientation optimization in IMRT is solved by a decoupled two-step iterative process: (1) optimization of the intensity profiles for given beam configurations; (2) selection of optimal beam configurations based on the ranking by an objective function score for the results of the intensity profile optimization. The simulated dynamics algorithm is used for the intensity profile optimization. This algorithm enforces both the hard constraints and dose-volume constraints. A genetic algorithm is used to select beam orientation configurations. The method has been tested for both a simulated and clinical case, and the results show that beam orientation optimization significantly improved IMRT plans within a time period that is clinically acceptable. The results also show the dependence of the optimal orientation configurations on the prescribed constraints. In addition, beam orientation optimization by the method described here can provide multiple plans with similar dose distributions. This degeneracy characteristic can be exploited to our advantage in introducing additional planning objectives, e.g., the smoothness of intensity profiles, for the selection of the optimal plan among the degenerate configurations for treatment delivery.

title: Spherical cluster analysis for beam angle optimization in intensity-modulated radiation therapy treatment planning.
abstract: An intuitive heuristic to establish beam configurations for intensity-modulated radiation therapy is introduced as an extension of beam ensemble selection strategies applying scalar scoring functions. It is validated by treatment plan comparisons for three intra-cranial, pancreas, and prostate cases each. Based on a patient specific matrix listing the radiological quality of candidate beam directions individually for every target voxel, a set of locally ideal beam angles is generated. The spherical distribution of locally ideal beam angles is characteristic for every treatment site and patient: ideal beam angles typically cluster around distinct orientations. We interpret the cluster centroids, which are identified with a spherical K-means algorithm, as irradiation angles of an intensity-modulated radiation therapy treatment plan. The fluence profiles are subsequently optimized during a conventional inverse planning process. The average computation time for the pre-optimization of a beam ensemble is six minutes on a state-of-the-art work station. The treatment planning study demonstrates the potential benefit of the proposed beam angle optimization strategy. For the three prostate cases under investigation, the standard treatment plans applying nine coplanar equi-spaced beams and treatment plans applying an optimized non-coplanar nine-beam ensemble yield clinically comparable dose distributions. For symmetric patient geometries, the dose distribution formed by nine equi-spaced coplanar beams cannot be improved significantly. For the three pancreas and intra-cranial cases under investigation, the optimized non-coplanar beam ensembles enable better sparing of organs at risk while guaranteeing equivalent target coverage. Beam angle optimization by spherical cluster analysis shows the biggest impact for target volumes located asymmetrically within the patient and close to organs at risk.

title: Patient geometry-driven information retrieval for IMRT treatment plan quality control.
abstract: Intensity modulated radiation therapy (IMRT) treatment plan quality depends on the planner's level of experience and the amount of time the planner invests in developing the plan. Planners often unwittingly accept plans when further sparing of the organs at risk (OARs) is possible. The authors propose a method of IMRT treatment plan quality control that helps planners to evaluate the doses of the OARs upon completion of a new plan.



It is achieved by comparing the geometric configurations of the OARs and targets of a new patient with those of prior patients, whose plans are maintained in a database. They introduce the concept of a shape relationship descriptor and, specifically, the overlap volume histogram (OVH) to describe the spatial configuration of an OAR with respect to a target. The OVH provides a way to infer the likely DVHs of the OARs by comparing the relative spatial configurations between patients. A database of prior patients is built to serve as an external reference. At the conclusion of a new plan, planners search through the database and identify related patients by comparing the OAR-target geometric relationships of the new patient with those of prior patients. The treatment plans of these related patients are retrieved from the database and guide planners in determining whether lower doses delivered to the OARs in the new plan are feasible.



Preliminary evaluation is promising. In this evaluation, they applied the analysis to the parotid DVHs of 32 prior head-and-neck patients, whose plans are maintained in a database. Each parotid was queried against the other 63 parotids to determine whether a lower dose was possible. The 17 parotids that promised the greatest reduction in D50 (DVH dose at 50% volume) were flagged. These 17 parotids came from 13 patients. The method also indicated that the doses of the other nine parotids of the 13 patients could not be reduced, so they were included in the replanning process as controls. Replanning with an effort to reduce D50 was conducted on these 26 parotids. After replanning, the average reductions for D50 of the 17 flagged parotids and nine unflagged parotids were 6.6 and 1.9 Gy, respectively. These results demonstrate that the quality control method has accurately identified not only the parotids that require dose reductions but also those for which dose reductions are marginal. Originally, 11 of out the 17 flagged parotids did not meet the Radiation Therapy Oncology Group sparing goal of V(30 Gy) < 50%. Replanning reduced them to three. Additionally, PTV coverage and OAR sparing of the original plans were compared to those of the replans by using pairwise Wilcoxon p test. The statistical comparisons show that replanning compromised neither PTV coverage nor OAR sparing.



This method provides an effective quality control mechanism for evaluating the DVHs of the OARs. Adoption of such a method will advance the quality of current IMRT planning, providing better treatment plan consistency.

title: Volumetric-modulated arc therapy for stereotactic body radiotherapy of lung tumors: a comparison with intensity-modulated radiotherapy techniques.
abstract: To demonstrate the potential of volumetric-modulated arc therapy (VMAT) compared with intensity-modulated radiotherapy (IMRT) techniques with a limited number of segments for stereotactic body radiotherapy (SBRT) for early-stage lung cancer.



For a random selection of 27 patients eligible for SBRT, coplanar and noncoplanar IMRT and coplanar VMAT (using SmartArc) treatment plans were generated in Pinnacle(3) and compared. In addition, film measurements were performed using an anthropomorphic phantom to evaluate the skin dose for the different treatment techniques.



Using VMAT, the delivery times could be reduced to an average of 6.6 min compared with 23.7 min with noncoplanar IMRT. The mean dose to the healthy lung was 4.1 Gy for VMAT and noncoplanar IMRT and 4.2 Gy for coplanar IMRT. The volume of healthy lung receiving>5 Gy and >20 Gy was 18.0% and 5.4% for VMAT, 18.5% and 5.0% for noncoplanar IMRT, and 19.4% and 5.7% for coplanar IMRT, respectively. The dose conformity at 100% and 50% of the prescribed dose of 54 Gy was 1.13 and 5.17 for VMAT, 1.11 and 4.80 for noncoplanar IMRT and 1.12 and 5.31 for coplanar IMRT, respectively. The measured skin doses were comparable for VMAT and noncoplanar IMRT and slightly greater for coplanar IMRT.



Coplanar VMAT for SBRT for early-stage lung cancer achieved plan quality and skin dose levels comparable to those using noncoplanar IMRT and slightly better than those with coplanar IMRT. In addition, the delivery time could be reduced by ≤70% with VMAT.

title: Optimising stereotactic body radiotherapy for non-small cell lung cancer with volumetric intensity-modulated arc therapy--a planning study.
abstract: The potential advantages of stereotactic body radiotherapy (SBRT) for early stage non-small cell lung cancer (NSCLC) over conventional fractionated radiotherapy include a higher biological effective dose, a reduction in accelerated repopulation, greater patient convenience and reduced demand on radiotherapy resources. Before introducing SBRT in our department, a review of planning and delivery was undertaken, starting with an assessment of optimum beam number and arrangement.



Radiotherapy planning computed tomography scans for five patients previously treated for T1 peripheral NSCLC were selected. In each the contoured tumour had planning target volume (PTV) margins of 1cm in all directions. Forward-planned three-field coplanar and non-coplanar plans and a seven-field coplanar plan were produced and optimised. In-house inverse-planning software (AutoBeam) was used to generate three-, five-, seven- and nine-field coplanar and non-coplanar plans and two volumetric intensity-modulated arc therapy (VMAT) plans. The resulting V(20), V(11), PTV(90), PTV(95) and mean lung dose were compared.



Analysis of variance showed non-coplanar plans to have lower V(11) and higher PTV(90) and PTV(95) than coplanar plans. VMAT showed equivalent V(20) and target coverage when compared with the best non-coplanar plans, but with a faster delivery time (2min 8s versus 12min 40s).



Inverse-planned five-field non-coplanar plans and VMAT improve target coverage while minimising the higher dose to normal lung tissue for SBRT of NSCLC compared with coplanar beam arrangements. VMAT is preferable because of significantly shorter treatment delivery times.

title: On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion
abstract: We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem.

title: Support Vector Regression
abstract: Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems. As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning approach, SVR trains using a symmetrical loss function, which equally penalizes high and low misestimates. Using Vapnik’s -insensitive approach, a flexible tube of minimal radius is formed symmetrically around the estimated function, such that the absolute values of errors less than a certain threshold are ignored both above and below the estimate. In this manner, points outside the tube are penalized, but those within the tube, either above or below the function, receive no penalty. One of the main advantages of SVR is that its computational complexity does not depend on the dimensionality of the input space. Additionally, it has excellent generalization capability, with high prediction accuracy.

title: Estimation of prediction error by using K-fold cross-validation
abstract: Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction error, but it has a downward bias. On the other hand, K-fold cross-validation has an upward bias. The upward bias may be negligible in leave-one-out cross-validation, but it sometimes cannot be neglected in 5-fold or 10-fold cross-validation, which are favored from a computational standpoint. Since the training error has a downward bias and K-fold cross-validation has an upward bias, there will be an appropriate estimate in a family that connects the two estimates. In this paper, we investigate two families that connect the training error and K-fold cross-validation.

title: Scikit-learn: Machine Learning in Python
abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

title: A simple scoring ratio to index the conformity of radiosurgical treatment plans. Technical note.
abstract: A conformity index is a measure of how well the volume of a radiosurgical dose distribution conforms to the size and shape of a target volume. Because the success of radiosurgery is related to the extremely conformal irradiation of the target, an accurate method for describing this parameter is important. Existing conformity ratios and indices used in radiosurgery are reviewed and criticized. It will be demonstrated that previously proposed measurements of conformity can, under certain conditions, give false perfect scores. A new conformity index is derived that gives an objective score of conformity for a treatment plan and gives no false scores. An analysis of five different treatment plans is made using both the existing scoring methods and the new conformity index.