bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐C‐BRE‐02: BED Vs. Local Control: Radiobiological Effect of Tumor Volume in Monte Carlo (MC) Lung SBRT Planning
Authors: Pokhrel D , Badkul R , Jiang H , Estes C , Park J , Kumar P , Wang F 
Published: July 2014

Purpose:SBRT with hypofractionated dose schemata has emerged a compelling treatment modality for medically inoperable early stage lung cancer patients. It requires more accurate dose calculation and treatment delivery technique. This report presents the relationship between tumor control probability(TCP) and size‐adjusted biological effective dose(sBED) of tumor volume for MC lung SBRT patients.Methods:Fifteen patients who were treated with MC‐based lung SBRT to 50Gy in 5 fractions to PTVV100%=95% were studied. ITVs were delineated on MIP images of 4DCT‐scans. PTVs diameter(ITV+5mm margins) ranged from 2.7–4.9cm (mean 3.7cm). Plans were generated using non‐coplanar conformal arcs/beams using iPlan XVMC algorithm (BrainLABiPlan ver.4.1.2) for Novalis‐TX with HD‐MLCs and 6MVSRS(1000MU/min) mode, following RTOG‐0813 dosimetric guidelines. To understand the known uncertainties of conventional heterogeneities‐corrected/uncorrected pencil beam (PBhete/ PB‐homo) algorithms, dose distributions were re‐calculated with PBhete/ PB‐homo using same beam configurations, MLCs and monitor units. Biologically effective dose(BED10) was computed using LQ‐model with α/β=10Gy for meanPTV and meanITV. BED10‐c*L, gave size‐adjusted BED(sBED), where c=10Gy/cm and L=PTV diameter in centimeter. The TCP model was adopted from Ohri et al.(IJROBP, 2012): TCP = exp[sBEDTCD50]/ k /(1.0 + exp[sBED‐TCD50]/k), where k=31Gy corresponding to TCD50=0Gy; and more realistic MC‐based TCP was computed for PTV(V99%).Results:Mean PTV PB‐hete TCP value was 6% higher, but, mean PTV PB‐homo TCP value was 4% lower compared to mean PTV MC TCP. Mean ITV PB‐hete/PB‐homo TCP values were comparable (within ±3.0%) to mean ITV MC TCP. The mean PTV(V99%)had BED10=90.9±3.7%(median=92.2%),sBED=54.1±8.2%(median=53.5%) corresponding to mean MC TCP value of 84.8±3.3%(median=84.9%) at 2‐ year local control.Conclusion:The TCP model which incorporates BED10 and tumor diameter indicates that radiobiological effect of target volume and dose calculation algorithm significantly affects TCP for lung SBRT patients. Dose calculation using MC‐based algorithm is more realistic with tissue heterogeneities and is routinely performed in our clinic. Patients will be followed up to determine whether TCP prediction correlate clinical outcomes.
SU‐C‐BRE‐03: Dual Compartment Mathematical Modeling of Glioblastoma Multiforme (GBM)
Authors: Yu V , Nguyen D , Kupelian P , Kaprealian T , Selch M , Low D , Pajonk F , Sheng K 
Published: July 2014

Purpose:To explore the aggressive recurrence and radioresistence of GBM with a dual compartment tumor survival mathematical model based on intrinsic tumor heterogeneity, cancer stem cells (CSC) and differentiated cancer cells (DCC).Methods:The repopulation and differentiation responses to radiotherapy of a solid tumor were simulated using an Ordinary Differential Equation (ODE). To obtain the tumor radiobiological parameters, we assumed that a tumor consists of two subpopulations, each with its distinctive linear quadratic parameters. The dual compartment cell survival model was constructed as SF(D)=F × exp(‐α1 D‐β1D2) + (1‐F) × exp(‐α2D‐β2D2) for a single fraction of treatment, with F as the fraction of CSC, and α and β describing the radiological properties of each population. Robust least square fitting was performed on clonogenic survival data from one GBM (U373MG) and one NSCLC (H460) cell line. The fit parameters were then used in the ODE model to predict treatment outcome of various treatment schemes.Results:The fit parameters from GBM cell survival data were (F, α1, β1, α2, β2)=(0.0396, 0.0801, 0.0006, 0.1363, 0.0279), exhibiting two populations with distinctive radiological properties, CSC more radioresistant than DCC. The GBM cell line exhibited significantly poorer tumor control than its single compartment model prediction and NSCLC, which responded well to hypofrationation. The increased radioresistance was due to rapid regrowth of the DCC compartment triggered by its depletion while maintaining a viable CSC population. The rapid regrowth can be reduced by treating dose fractions ≤ 2 Gy with a prolonged treatment period.Conclusion:The interaction between a radioresistant CSC compartment and DCC compartment can explain the poor clinical outcome of GBM after radiotherapy despite dose escalation and hypofractionation attempts. Lower dose fractions result in better treatment outcome but still eventually recurs. Dose escalation beyond 100 Gy and/or differentiation therapy will be vital in achieving GBM tumor control.
SU‐C‐BRD‐02: A Team Focused Clinical Implementation and Failure Mode and Effects Analysis of HDR Skin Brachytherapy Using Valencia and Leipzig Surface Applicators
Authors: Sayler E , Harrison A , Eldredge‐Hindy H , Dinome J , Munro S , Anne R , Comber E , Lockamy V 
Published: July 2014

Purpose:and Leipzig applicators (VLAs) are single‐channel brachytherapy surface applicators used to treat skin lesions up to 2cm diameter. Source dwell times can be calculated and entered manually after clinical set‐up or ultrasound. This procedure differs dramatically from CT‐based planning; the novelty and unfamiliarity could lead to severe errors. To build layers of safety and ensure quality, a multidisciplinary team created a protocol and applied Failure Modes and Effects Analysis (FMEA) to the clinical procedure for HDR VLA skin treatments.Methods:team including physicists, physicians, nurses, therapists, residents, and administration developed a clinical procedure for VLA treatment. The procedure was evaluated using FMEA. Failure modes were identified and scored by severity, occurrence, and detection. The clinical procedure was revised to address high‐scoring process nodes.Results:Several key components were added to the clinical procedure to minimize risk probability numbers (RPN): ‐Treatments are reviewed at weekly QA rounds, where physicians discuss diagnosis, prescription, applicator selection, and set‐up. Peer review reduces the likelihood of an inappropriate treatment regime. ‐A template for HDR skin treatments was established in the clinical EMR system to standardize treatment instructions. This reduces the chances of miscommunication between the physician and planning physicist, and increases the detectability of an error during the physics second check. ‐A screen check was implemented during the second check to increase detectability of an error. ‐To reduce error probability, the treatment plan worksheet was designed to display plan parameters in a format visually similar to the treatment console display. This facilitates data entry and verification. ‐VLAs are color‐coded and labeled to match the EMR prescriptions, which simplifies in‐room selection and verification.Conclusion:Multidisciplinary planning and FMEA increased delectability and reduced error probability during VLA HDR Brachytherapy. This clinical model may be useful to institutions implementing similar procedures.
SU‐C‐BRD‐04: Automatic Detection of Patient Identification and Patient Positioning Errors Using 3D Setup Images
Authors: Jani S , O' Connell D , Chow P , Agazaryan N , Low D , Lamb J 
Published: July 2014

Purpose:To develop an automated system to detect patient identification and patient positioning errors using algorithmic comparison between megavoltage CT (MVCT) and kilovoltage CT (kVCT) planning images.Methods:MVCT images from 35 head and neck (H&N) patients and 19 pelvis patients were collected from a Tomotherapy machine, along with the corresponding planning kVCTs. MVCTs and kVCTs were manually aligned according to clinical protocols at our institution. Patient identification errors were simulated by aligning MVCTs and kVCTs from different patients. Positioning errors were simulated by misaligning MVCTs and kVCTs by 1cm to 5cm in the each of the six anatomical directions. For each image pair, a pixel‐by‐pixel cross‐correlation metric was computed within the MVCT image body contour. To eliminate the effect of daily variations in bowel gas, the metric was limited to voxels with HU>‐700. The kVCT voxel intensities were remapped to the MVCT scale using a third‐order polynomial from a publicly available software package.Results:A threshold pixel‐by‐pixel cross‐correlation value was found that distinguished between correct and incorrect patient setup with a high degree of accuracy. A stratified 10‐fold cross‐validation analysis yielded average misclassification probabilities of 0.0030 for H&N and 0.00 for pelvis. For misaligned image pairs, cross‐validation analysis yielded average misclassification probabilities of 0.00 and 0.0013 for H&N shifts ≥20mm and ≥10mm across all six anatomical directions, respectively. Misclassification probabilities were 0.00, 0.011, and 0.10 for pelvic shifts ≥30mm, ≥20mm, and ≥10mm, respectively. Receiver operator characteristic analysis for misaligned patients yielded areas under the curve ranging from 0.99 to 1.0 for H&N and 0.86 to 1.0 for pelvis.Conclusion:This proof‐ofconcept study shows that pixel‐by‐pixel cross‐correlation of MVCT setup images with their corresponding planning CT images can be used to detect wrong‐patient errors as well as incorrect patient shifts in the pelvis and H&N regions.
SU‐C‐BRD‐07: The Radiological Physics Center (RPC): 45 Years of Improving Radiotherapy Dosimetry
Authors: Followill D , Lowenstein J , Molineu A , Alvarez P , Summers P , Kry S 
Published: July 2014

Purpose:The RPC, established in 1968 has contributed to the development, conduct, and QA of NCI funded multi‐institutional cooperative group clinical trials and institutions, primarily in the USA/Canada and 242 other countries, participating in trials.Methods:The RPC QA program components were designed to audit the radiation dose calculation chain from the NIST traceable reference beam calibration, to inclusion of dosimetry parameters used to calculate tumor doses, to the delivery of the radiation dose. The QA program included: 1) remote TLD/OSLD audit of machine output, 2) on‐site dosimetry review visits, 3) credentialing for advanced technologies, and 4) review of patient treatment records. The RPC presented and published their findings to the radiation oncology community.Results:The number of institutions monitored by the RPC increased from around 1200 in the late 90s, to ∼2000 in 2013. There were over 4000 megavoltage therapy machines and ∼28,000 therapy beams in the 1991 institutions monitored by the RPC by the end of 2013. Within the 14,000 photon, electron and proton beam outputs remotely monitored with TLD/OSLD annually, between 10‐20% of the institutions have one or more beams outside the RPC 5% criterion. Dosimetry site visits to photon and proton centers continue to result in 2‐4 recommendations affecting key dosimetry parameters that impact patient treatment times. One in four patient treatment records reviewed by the RPC have their dose data corrected by >5% before trial groups use them for outcomes analysis. Twelve of fourteen clinically active proton centers are approved to participate in NCI funded clinical trials. The RPC published 222 peer reviewed articles since 1972.Conclusion:Findings from the RPC suggest that human errors continue to play a role in radiotherapy discrepancies and without the RPC independent QA program, the number of undetected errors and time elapsed before their discovery would have been greater.Work supported by MGH C06 CA059267 and grants CA10953, CA081647 awarded by NCI, DHHS.
SU‐C‐BRF‐01: Correlation of DIBH Breath Hold Amplitude with Dosimetric Sparing of Heart and Left Anterior Descending Artery in Left Breast Radiotherapy
Authors: Kim Taeho , Reardon Kelli , Sukovich Kaitlyn , Crandley Edwin , Read Paul , Krishni Wijesooriya 
Published: July 2014

Purpose:A 7.4% increase in major coronary events per 1 Gy increase in mean heart dose has been reported from the population‐based analysis of radiation‐induced cardiac toxicity following treatment of left sided breast cancer. Deep inhalation breath‐hold (DIBH) is clinically utilized to reduce radiation dose to heart and left anterior descending artery (LAD). We investigated the correlation of dose sparing in heart and LAD with internal DIBH amplitude to develop a quantitative predictive model for expected dose to heart and LAD based on internal breath hold amplitude.Methods:A treatment planning study (Prescription Dose = 50 Gy) was performed on 50 left breast cancer patients underwent DIBH whole breast radiotherapy. Two CT datasets, free breathing (FB) and DIBH, were utilized for treatment planning and for determination of the internal anatomy DIBH amplitude (difference between sternum position at FB and DIBH). The heart and LAD dose between FB and DIBH plans was compared and dose to the heart and LAD as a function of breath hold amplitude was determined.Results:Average DIBH amplitude using internal anatomy was 13.9±4.2 mm. The DIBH amplitude‐mean dose reduction correlation is 20%/5mm (0.3 Gy/5mm) for the heart and 18%/5mm (1.1 Gy/5mm) for LAD. The correlation with max dose reduction is 12%/5mm (3.8 Gy/5mm) for the heart and 16%/5mm (3.2 Gy/5mm) for LAD. We found that average dose reductions to LAD from 6.0±6.5 Gy to 2.0±1.6 Gy with DIBH (4.0 Gy reduction: ‐67%, p < 0.001) and average dose reduction to the heart from 1.3±0.7 Gy to 0.7±0.2 Gy with DIBH (0.6 Gy reduction: ‐46%, p < 0.001). That suggests using DIBH may reduce the risk of the major coronary event for left sided breast cancer patients.Conclusion:The correlation between breath hold amplitude and dosimetric sparing suggests that dose sparing linearly increases with internal DIBH amplitude.
SU‐C‐BRF‐02: Surface Imaging Based Analysis of Intra‐Fraction Motion for Breast Radiotherapy Patients
Authors: Wiant D , Wentworth S , Maurer J , Vanderstraeten C , Terrell J , Sintay B 
Published: July 2014

Purpose:To quantify and characterize intrafraction motion for breast radiotherapy treatments using 3D surface imaging.Methods:Thirty free breathing breast patients were monitored with 3D surface imaging over a total of 831 monitoring sessions. Mean translations and rotations were calculated over each minute, each session, and over all sessions combined. The percentage of each session that the root mean square (RMS) of the linear translations were outside of 2 mm, 3 mm, 4 mm, and 5 mm were determined for each patient. Correlations between mean translations per minute and time and between standard deviation per minute and time were evaluated using Pearson's r value.Results:The mean RMS translation averaged over all patients was 2.63 mm +/‐ 1.63 mm. The patients spent an average of 52%, 24%, 11%, and 5% of the monitoring time outside of 2 mm, 3 mm, 4 mm, and 5 mm RMS tolerances, respectively. The RMS values averaged over all patients were 2.95 mm +/‐ 1.67 mm, 3.21 +/‐ 2.11, and 3.57 mm +/‐ 2.23 mm over the 5th, 10th, and 15th minutes of monitoring, respectively. The RMS values (r = 0.95, p = 0) and the standard deviations of the RMS values (r = 0.97, p = 0) both showed strong significant correlations with time.Conclusion:The majority of patients treatment time is spent within 5 mm of the isocenter. Patient position tends to drift with increasing time in the session. Treatment length should be considered when determining planning margins. An 8 mm margin on a target volume would account for two standard deviations of motion for a treatment up to 15 minutes in length.
SU‐C‐BRF‐03: PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy
Authors: Chetvertkov M , Kim J , Siddiqui F , Kumarasiri A , Chetty I , Gordon J 
Published: July 2014

Purpose:To develop principal component analysis (PCA) models from daily cone beam CTs (CBCTs) of head and neck (H&N) patients that could be used prospectively in adaptive radiation therapy (ART).Methods:: For 7 H&N patients, Pinnacle Treatment Planning System (Philips Healthcare) was used to retrospectively deformably register daily CBCTs to the planning CT. The number N of CBCTs per treatment course ranged from 14 to 22. For each patient a PCA model was built from the deformation vector fields (DVFs), after first subtracting the mean DVF, producing N eigen‐DVFs (EDVFs). It was hypothesized that EDVFs with large eigenvalues represent the major anatomical deformations during the course of treatment, and that it is feasible to relate each EDVF to a clinically meaningful systematic or random change in anatomy, such as weight loss, neck flexion, etc.Results:DVFs contained on the order of 3×87×87×58=1.3 million scalar values (3 times the number of voxels in the registered volume). The top 3 eigenvalues accounted for ∼90% of variance. Anatomical changes corresponding to an EDVF were evaluated by generating a synthetic DVF, and applying that DVF to the CT to produce a synthetic CBCT. For all patients, the EDVF for the largest eigenvalue was interpreted to model weight loss. The EDVF for other eigenvalues appeared to represented quasi‐random fraction‐to‐fraction changes.Conclusion:The leading EDVFs from single‐patient PCA models have tentatively been identified with weight loss changes during treatment. Other EDVFs are tentatively identified as quasi‐random inter‐fraction changes. Clean separation of systematic and random components may require further work. This work is expected to facilitate development of population‐based PCA models that can be used to prospectively identify significant anatomical changes, such as weight loss, early in treatment, triggering replanning where beneficial.
SU‐C‐BRF‐04: Delivered 4D Dose Calculation for Lung‐VMAT Patients Using In‐ Treatment CBCT and LINAC Log Data
Authors: Saotome N , Haga A , Imae T , Kida S , Nakano M , Yamashita H , Nakagawa K , Ohtomo K 
Published: July 2014

Purpose:To verify the delivered four‐dimensional (4D) dose distribution for the lung VMAT using in‐treatment 4D cone‐beam CT (CBCT) and LINAC log data.Methods:Three patients for lung stereotactic radiotherapy (SRT) were treated by single‐arc volumetric modulated arc therapy (VMAT) created by SmartArc in Pinnacle v9.2 (Philips, USA).Kilovoltage (kV) projection images were acquired during VMAT delivery using the x‐ray volumetric imager, XVI (Elekta, UK). LINAC log data were also recorded via iCom interface (Elekta, UK). Subsequently, the iCom data format was converted to Pinnacle data format, thereby allowing Pinnacle to read actual MLC positions, jaw positions, gantry angles and MUs. These parameters were categorized into four groups according to patient's respiratory phase. The patient respiratory phase was determined from kV‐projection images by in‐house phase recognizing software. By exporting these data back to Pinnacle, in‐treatment dose distributions for each respiratory phase were obtained by combining calculated dose having a particular phase. The 4D CBCT were reconstructed by in‐house program. The density in the structures on the CBCT was overridden by the average density in the corresponding structures on the planning CT for the dose calculation. The dose indexes, such as maximum, minimum, mean dose in the target were compared among the plan, in‐treatment 3D calculation, and in‐treatment 4D calculation.Results:The day‐to‐day variation of the delivered dose was observed for all three patients. The maximum difference was 5.9% for the minimum dose in the target. Difference for the mean dose was found to be small (1.1% at maximum).Conclusion:We have obtained in‐treatment dose distribution for each of the different respiratory phases using in‐treatment 4D CBCT and LINAC log data during lung VMAT delivery. Our result indicate a small but significant dose difference between the 3D and 4D calculations for the case of large target traveling.
SU‐C‐BRF‐05: Design and Geometric Validation of An Externally and Internally Deformable, Programmable Lung Motion Phantom
Authors: Cheung Y , Sawant A 
Published: July 2014

Purpose:Most clinically‐deployed strategies for respiratory motion management in lung radiotherapy (e.g., gating, tracking) use external markers that serve as surrogates for tumor motion. However, typical lung phantoms used to validate these strategies are rigid‐exterior+rigid‐interior or rigid‐exterior+deformable‐interior. Neither class adequately represents the human anatomy, which is deformable internally as well as externally. We describe the construction and experimental validation of a more realistic, externally‐ and internally‐deformable, programmable lung phantom.Methods:The outer shell of a commercially‐available lung phantom (RS‐ 1500, RSD Inc.) was used. The shell consists of a chest cavity with a flexible anterior surface, and embedded vertebrae, rib‐cage and sternum. A 3‐axis platform was programmed with sinusoidal and six patient‐recorded lung tumor trajectories. The platform was used to drive a rigid foam ‘diaphragm’ that compressed/decompressed the phantom interior. Experimental characterization comprised of mapping the superior‐inferior (SI) and anterior‐posterior (AP) trajectories of external and internal radioopaque markers with kV x‐ray fluoroscopy and correlating these with optical surface monitoring using the in‐room VisionRT system.Results:The phantom correctly reproduced the programmed motion as well as realistic effects such as hysteresis. The reproducibility of marker trajectories over multiple runs for sinusoidal as well as patient traces, as characterized by fluoroscopy, was within 0.4 mm RMS error for internal as well as external markers. The motion trajectories of internal and external markers as measured by fluoroscopy were found to be highly correlated (R=0.97). Furthermore, motion trajectories of arbitrary points on the deforming phantom surface, as recorded by the VisionRT system also showed a high correlation with respect to the fluoroscopically‐measured trajectories of internal markers (R=0.92).Conclusion:We have developed a realistic externally‐ and internally‐deformable lung phantom that will serve as a valuable tool for clinical QA and motion management research. This work was supported through funding from the NIH and VisionRT Ltd.Amit Sawant has research funding from Varian Medical Systems, VisionRT and Elekta