bluuebunny/crossref_metadata_2025 huggingface dataset:
WE‐AB‐202‐01: Evaluating the Toxicity Reduction with CT‐Ventilation Functional Avoidance Radiation Therapy
Authors: Vinogradskiy Y , Miyasaka Y , Kadoya N , Castillo R , Castillo E , Guerrero T , Yamamoto T
Published: July 2016

Purpose:

CT‐ventilation is an exciting new imaging modality that uses 4DCTs to calculate lung ventilation. Studies have proposed to use 4DCT‐ventilation imaging for functional avoidance radiotherapy which implies designing treatment plans to spare functional portions of the lung. Although retrospective studies have been performed to evaluate the dosimetric gains to functional lung; no work has been done to translate the dosimetric gains to an improvement in pulmonary toxicity. The purpose of our work was to evaluate the potential reduction in toxicity for 4DCT‐ventilation based functional avoidance.

Methods:

70 lung cancer patients with 4DCT imaging were used for the study. CT‐ventilation maps were calculated using the patient's 4DCT, deformable image registrations, and a density‐change‐based algorithm. Radiation pneumonitis was graded using imaging and clinical information. Log‐likelihood methods were used to fit a normal‐tissue‐complication‐probability (NTCP) model predicting grade 2+ radiation pneumonitis as a function of doses (mean and V20) to functional lung (>15% ventilation). For 20 patients a functional plan was generated that reduced dose to functional lung while meeting RTOG 0617‐based constraints. The NTCP model was applied to the functional plan to determine the reduction in toxicity with functional planning

Results:

The mean dose to functional lung was 16.8 and 17.7 Gy with the functional and clinical plans respectively. The corresponding grade 2+ pneumonitis probability was 26.9% with the clinically‐used plan and 24.6% with the functional plan (8.5% reduction). The V20‐based grade 2+ pneumonitis probability was 23.7% with the clinically‐used plan and reduced to 19.6% with the functional plan (20.9% reduction).

Conclusion:

Our results revealed a reduction of 9–20% in complication probability with functional planning. To our knowledge this is the first study to apply complication probability to convert dosimetric results to toxicity improvement. The results presented in the current work provide seminal data for prospective clinical trials in functional avoidance.

YV discloses funding from State of Colorado. TY discloses National Lung Cancer Partnership; Young Investigator Research grant
WE‐AB‐202‐03: Quantifying Ventilation Change Due to Radiation Therapy Using 4DCT Jacobian Calculations
Authors: Patton T , Du K , Christensen G , Reinhardt J , Bayouth J
Published: July 2016

Purpose:

Four‐dimensional computed tomography (4DCT) and image registration can be used to determine regional lung ventilation changes after radiation therapy (RT). This study aimed to determine if lung ventilation change following radiation therapy was affected by the pre‐RT ventilation of the lung.

Methods:

13 subjects had three 4DCT scans: two repeat scans acquired before RT and one three months after RT. Regional ventilation was computed using Jacobian determinant calculations on the registered 4DCT images. The post‐RT ventilation map was divided by the pre‐RT ventilation map to get a voxel‐by‐voxel Jacobian ratio map depicting ventilation change over the course of RT. Jacobian ratio change was compared over the range of delivered doses. The first pre‐RT ventilation image was divided by the second to establish a control for Jacobian ratio change without radiation delivered. The functional change between scans was assessed using histograms of the Jacobian ratios.

Results:

There were significantly (p < 0.05) more voxels that had a large decrease in Jacobian ratio in the post‐RT divided by pre‐RT map (15.6%) than the control (13.2%). There were also significantly (p < .01) more voxels that had a large increase in Jacobian ratio (16.2%) when compared to control (13.3%). Lung regions with low function (<10% expansion by Jacobian) showed a slight linear reduction in expansion (0.2%/10 Gy delivered), while high function regions (>10% expansion) showed a greater response (1.2% reduction/10 Gy). Contiguous high function regions > 1 liter occurred in 11 of 13 subjects.

Conclusion:

There is a significant change in regional ventilation following a course of radiation therapy. The change in Jacobian following RT is dependent both on the delivered dose and the initial ventilation of the lung tissue: high functioning lung has greater ventilation loss for equivalent radiation doses. Substantial regions of high function lung tissue are prevalent.

Research support from NIH grants CA166119 and CA166703, a gift from Roger Koch, and a Pilot Grant from University of Iowa Carver College of Medicine
WE‐AB‐202‐06: Correlating Lung CT HU with Transformation‐Based and Xe‐CT Derived Ventilation
Authors: Du K , Patton T , Reinhardt J , Christensen G , Bayouth J
Published: July 2016

Purpose:

Regional lung ventilation is useful to reduce radiation‐induced function damage during lung cancer radiation therapy. Recently a new direct HU (Hounsfield unit)‐based method was proposed to estimate the ventilation potential without image registration. The purpose of this study is to examine if there is a functional dependence between HU values and transformation‐based or Xe‐CT derived ventilation.

Methods:

4DCT images acquired from 13 patients prior to radiation therapy and 4 mechanically ventilated sheep subjects which also have associated Xe‐CT images were used for this analysis. Transformation‐based ventilation was computed using Jacobian determinant of the transformation field between peak‐exhale and peak‐inhale 4DCT images. Both transformation and Xe‐CT derived ventilation was computed for each HU bin. Color scatter plot and cumulative histogram were used to compare and validate the direct HU‐based method.

Results:

There was little change of the center and shape of the HU histograms between free breathing CT and 4DCT average, with or without smoothing, and between the repeated 4DCT scans. HU of −750 and −630 were found to have the greatest transformation‐based ventilation for human and sheep subjects, respectively. Maximum Xe‐CT derived ventilation was found to locate at HU of −600 in sheep subjects. The curve between Xe‐CT ventilation and HU was noisy for tissue above HU −400, possibly due to less intensity change of Xe gas during wash‐out and wash‐in phases.

Conclusion:

Both transformation‐based and Xe‐CT ventilation demonstrated that lung tissues with HU values in the range of (‐750, −600) HU have the maximum ventilation potential. The correlation between HU and ventilation suggests that HU might be used to help guide the ventilation calculation and make it more robust to noise and image registration errors.

Research support from NIH grants CA166703 and CA166119 and a gift from Roger Koch.
WE‐AB‐202‐07: Ventilation CT: Voxel‐Level Comparison with Hyperpolarized Helium‐3 & Xenon‐129 MRI
Authors: Tahir B , Marshall H , Hughes P , Stewart N , Horn F , Collier G , Norquay G , Hart K , Swinscoe J , Hatton M , Wild J , Ireland R
Published: July 2016

Purpose:

To compare the spatial correlation of ventilation surrogates computed from inspiratory and expiratory breath‐hold CT with hyperpolarized Helium‐3 & Xenon‐129 MRI in a cohort of lung cancer patients.

Methods:

5 patients underwent expiration & inspiration breath‐hold CT. Xenon‐129 & 1H MRI were also acquired at the same inflation state as inspiratory CT. This was followed immediately by acquisition of Helium‐3 & 1H MRI in the same breath and at the same inflation state as inspiratory CT. Expiration CT was deformably registered to inspiration CT for calculation of ventilation CT from voxel‐wise differences in Hounsfield units. Inspiration CT and the Xenon‐129's corresponding anatomical 1H MRI were registered to Helium‐3 MRI via the same‐breath anatomical 1H MRI. This enabled direct comparison of CT ventilation with Helium‐3 MRI & Xenon‐129 MRI for the median values in corresponding regions of interest, ranging from finer to coarser in‐plane dimensions of 10 by 10, 20 by 20, 30 by 30 and 40 by 40, located within the lungs as defined by the same‐breath 1H MRI lung mask. Spearman coefficients were used to assess voxel‐level correlation.

Results:

The median Spearman's coefficients of ventilation CT with Helium‐3 & Xenon‐129 MRI for ROIs of 10 by 10, 20 by 20, 30 by 30 and 40 by 40 were 0.52, 0.56, 0.60 and 0.68 and 0.40, 0.42, 0.52 and 0.70, respectively.

Conclusion:

This work demonstrates a method of acquiring CT & hyperpolarized gas MRI (Helium‐3 & Xenon‐129 MRI) in similar breath‐holds to enable direct spatial comparison of ventilation maps. Initial results show moderate correlation between ventilation CT & hyperpolarized gas MRI, improving for coarser regions which could be attributable to the inherent noise in CT intensity, non‐ventilatory effects and registration errors at the voxel‐level. Thus, it may be more beneficial to quantify ventilation at a more regional level.
WE‐AB‐202‐10: Modelling Individual Tumor‐Specific Control Probability for Hypoxia in Rectal Cancer
Authors: Warren S , Warren DR , Wilson JM , Muirhead R , Hawkins MA , Maughan T , Partridge M
Published: July 2016

Purpose:

To investigate hypoxia‐guided dose‐boosting for increased tumour control and improved normal tissue sparing using FMISO‐PET images

Methods:

Individual tumor‐specific control probability (iTSCP) was calculated using a modified linear‐quadratic model with rectal‐specific radiosensitivity parameters for three limiting‐case assumptions of the hypoxia / FMISO uptake relationship. 18FMISO‐PET images from 2 patients (T3N0M0) from the RHYTHM trial (Investigating Hypoxia in Rectal Tumours NCT02157246) were chosen to delineate a hypoxic region (GTV_MISO defined as tumor‐to‐muscle ratio > 1.3) within the anatomical GTV. Three VMAT treatment plans were created in Eclipse (Varian): STANDARD (45Gy / 25 fractions to PTV4500); BOOST_GTV (simultaneous integrated boost of 60Gy / 25fr to GTV +0.5cm) and BOOST_MISO (60Gy / 25fr to GTV_MISO+0.5cm). GTV mean dose (in EQD2), iTSCP and normal tissue dose‐volume metrics (small bowel, bladder, anus, and femoral heads) were recorded.

Results:

Patient A showed small hypoxic volume (15.8% of GTV) and Patient B moderate hypoxic volume (40.2% of GTV). Dose escalation to 60Gy was achievable, and doses to femoral heads and small bowel in BOOST plans were comparable to STANDARD plans. For patient A, a reduced maximum bladder dose was observed in BOOST_MISO compared to BOOST_GTV (D0.1cc 49.2Gy vs 54.0Gy). For patient B, a smaller high dose volume was observed for the anus region in BOOST_MISO compared to BOOST_GTV (V55Gy 19.9% vs 100%), which could potentially reduce symptoms of fecal incontinence. For BOOST_MISO, the largest iTSCPs (A: 95.5% / B: 90.0%) assumed local correlation between FMISO uptake and hypoxia, and approached iTSCP values seen for BOOST_GTV (A: 96.1% / B: 90.5%).

Conclusion:

Hypoxia‐guided dose‐boosting is predicted to improve local control in rectal tumors when FMISO is spatially correlated to hypoxia, and to reduce dose to organs‐at‐risk compared to boosting the whole GTV. This could lead to organ‐preserving treatment strategies for locally‐advanced rectal cancer, thereby improving quality of life.

Oxford Cancer Imaging Centre (OCIC); Cancer Research UK (CRUK); Medical Research Council (MRC)
WE‐AB‐202‐11: Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre‐Treatment Dynamic PET Imaging Data
Authors: Crispin‐Ortuzar M , Grkovski M , Beattie B , Lee N , Riaz N , Humm J , Jeong J , Fontanella A , Deasy J
Published: July 2016

Purpose:

To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid‐treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with [18‐F]FMISO dynamic PET and glucose metabolism with [18‐F]FDG PET.

Methods:

A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra‐tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid‐therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel‐level FDG uptake, perfusion (FMISO K1) and hypoxia (FMISO k3). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion's median hypoxia level (k3[med,sim]) which in turn was compared to the FMISO k3[med] measured at mid‐therapy.

Results:

The average k3[med] of the tumors in pre‐treatment scans was 0.0035 min−1, with an inter‐tumor standard deviation of σ[pre]=0.0034 min−1. The initial simulated k3[med,sim] of each tumor agreed with the corresponding measurements within 0.1σ[pre]. In 7 out of 10 lesions, the mid‐treatment k3[med,sim] prediction agreed with the data within 0.3σ[pre]. The remaining cases corresponded to the most extreme relative changes in k3[med].

Conclusion:

This work presents a method to personalize the prediction of a TCP model using pre‐treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia values during treatment. Variations from predicted response may be a useful biomarker, which should be further explored.

Partially supported by NIH grant #1 R01 CA157770‐01A1 and a grant from Varian Corporation.
WE‐AB‐207A‐01: BEST IN PHYSICS (IMAGING): High‐Resolution Cone‐Beam CT of the Extremities and Cancellous Bone Architecture with a CMOS Detector
Authors: Cao Q , Brehler M , Sisniega A , Marinetto E , Zyazin A , Peters I , Stayman J , Yorkston J , Siewerdsen J , Zbijewski W
Published: July 2016

Purpose:

Extremity cone‐beam CT (CBCT) with an amorphous silicon (aSi) flat‐panel detector (FPD) provides low‐dose volumetric imaging with high spatial resolution. We investigate the performance of the newer complementary metal‐oxide semiconductor (CMOS) detectors to enhance resolution of extremities CBCT to ∼0.1 mm, enabling morphological analysis of trabecular bone. Quantitative in‐vivo imaging of bone microarchitecture could present an important advance for osteoporosis and osteoarthritis diagnosis and therapy assessment.

Methods:

Cascaded systems models of CMOS‐ and FPD‐based extremities CBCT were implemented. Performance was compared for a range of pixel sizes (0.05–0.4 mm), focal spot sizes (0.3–0.6 FS), and x‐ray techniques (0.05–0.8 mAs/projection) using detectability of high‐, low‐, and all‐frequency tasks for a nonprewhitening observer. Test‐bench implementation of CMOS‐based extremity CBCT involved a Teledyne DALSA Xineos3030HR detector with 0.099 mm pixels and a compact rotating anode x‐ray source with 0.3 FS (IMD RTM37). Metrics of bone morphology obtained using CMOS‐based CBCT were compared in cadaveric specimens to FPD‐based system using a Varian PaxScan4030 (0.194 mm pixels).

Results:

Finer pixel size and reduced electronic noise for CMOS (136 e compared to 2000 e for FPD) resulted in ∼1.9× increase in detectability for high‐frequency tasks and ∼1.1× increase for all‐frequency tasks. Incorporation of the new x‐ray source with reduced focal spot size (0.3 FS vs. 0.5 FS used on current extremities CBCT) improved detectability for CMOS‐based CBCT by ∼1.7× for high‐frequency tasks. Compared to FPD CBCT, the CMOS detector yielded improved agreement with micro‐CT in measurements of trabecular thickness (∼1.7× reduction in relative error), bone volume (∼1.5× reduction), and trabecular spacing (∼3.5× reduction).

Conclusion:

Imaging performance modelling and experimentation indicate substantial improvements for high‐frequency imaging tasks through adoption of the CMOS detector and small FS x‐ray source, motivating the use of these components in a new system for quantitative in‐vivo imaging of trabecular bone.

Financial Support: US NIH grant R01EB018896. Qian Cao is a Howard Hughes Medical Institute International Student Research Fellow.

Disclosures: W Zbijewski, J Siewerdsen and A Sisniega receive research funding from Carestream Health.
WE‐AB‐207A‐04: Random Undersampled Cone Beam CT: Theoretical Analysis and a Novel Reconstruction Method
Authors: Shen C , Lou Y , Chen L , Jia X
Published: July 2016

Purpose:

Reducing x‐ray exposure and speeding up data acquisition motived studies on projection data undersampling. It is an important question that for a given undersampling ratio, what the optimal undersampling approach is. In this study, we propose a new undersampling scheme: random‐ray undersampling. We will mathematically analyze its projection matrix properties and demonstrate its advantages. We will also propose a new reconstruction method that simultaneously performs CT image reconstruction and projection domain data restoration.

Methods:

By representing projection operator under the basis of singular vectors of full projection operator, matrix representations for an undersampling case can be generated and numerical singular value decomposition can be performed. We compared properties of matrices among three undersampling approaches: regular‐view undersampling, regular‐ray undersampling, and the proposed random‐ray undersampling. To accomplish CT reconstruction for random undersampling, we developed a novel method that iteratively performs CT reconstruction and missing projection data restoration via regularization approaches.

Results:

For a given undersampling ratio, random‐ray undersampling preserved mathematical properties of full projection operator better than the other two approaches. This translates to advantages of reconstructing CT images at lower errors. Different types of image artifacts were observed depending on undersampling strategies, which were ascribed to the unique singular vectors of the sampling operators in the image domain. We tested the proposed reconstruction algorithm on a Forbid phantom with only 30% of the projection data randomly acquired. Reconstructed image error was reduced from 9.4% in a TV method to 7.6% in the proposed method.

Conclusion:

The proposed random‐ray undersampling is mathematically advantageous over other typical undersampling approaches. It may permit better image reconstruction at the same undersampling ratio. The novel algorithm suitable for this random‐ray undersampling was able to reconstruct high‐quality images.
WE‐AB‐207A‐11: Respiratory Motion Guided 4DCBCT ‐ A Step Towards Controlling 4DCBCT Image Quality
Authors: O'Brien R , Stankovic U , Keall P , Sonke J
Published: July 2016

Purpose:

We have developed a method, called respiratory motion guided 4DCBCT (RMG‐4DCBCT), in which the gantry speed and projection frequency are varied in response to the patient's real‐time respiratory signal to eliminate streaking artifacts and to suppress duplicate projections in 4DCBCT images. In 2015, we realized RMG‐4DCBCT on an Elekta Synergy linear accelerator with a mechanical relay to suppress projections and a potentiometer to adjust the gantry speed in response to the patient's real‐time respiratory signal. The aim of this study was to analyse the image quality to determine what can and cannot be controlled.

Methods:

Using RMG‐4DCBCT, we acquired 40 (RMG‐4DCBCT_40) and 60 (RMG‐4DCBCT_60) equally spaced projections per respiratory phase of the CIRS dynamic thorax phantom with breathing periods from 2s to 8s and two breathing traces from lung cancer patients. The contrast to noise ratio (CNR) and edge response width (ERW) were used to compare image quality between RMG‐4DCBCT and conventional 4DCBCT.

Results:

Regardless of the breathing period, for RMG‐4DCBCT, the CNR is approximately 7 and 9 with RMG‐4DCBCT_40 and RMG‐4DCBCT_60 respectively. Conventional 4DCBCT has a CNR dropping from 20 down to 6 as the breathing period drops from 2s to 8s. With RMG‐4DCBCT, the ERW, in the direction of phantom motion, ranges from 2.1mm to 2.5mm as the breathing period drops from 2s to 8s which compares to a higher range of 2.0mm to 2.5mm with conventional 4DCBCT. Images with similar quality to conventional 4DCBCT can be acquired with RMG‐4DCBCT_40 which has a 70% reduction in imaging dose.

Conclusion:

The image contrast can be controlled with RMG‐4DCBCT regardless of the patients breathing rate. However, although the image sharpness is better with RMG‐4DCBCT, image sharpness has a small dependence on the breathing period; the accuracy of registration and segmentation will therefore vary with the patient's breathing period.

This project was supported by a National Health and Medical Research Council (NHMRC) project grant 1034060 and Cancer Australia grant number 1084566.
WE‐AB‐207A‐12: HLCC Based Quantitative Evaluation Method of Image Artifact in Dental CBCT
Authors: Chen Y , Wu S , Qi H , Xu Y , Zhou L
Published: July 2016

Purpose:

Image artifacts are usually evaluated qualitatively via visual observation of the reconstructed images, which is susceptible to subjective factors due to the lack of an objective evaluation criterion. In this work, we propose a Helgason‐Ludwig consistency condition (HLCC) based evaluation method to quantify the severity level of different image artifacts in dental CBCT.

Methods:

Our evaluation method consists of four step: 1) Acquire Cone beam CT(CBCT) projection; 2) Convert 3D CBCT projection to fan‐beam projection by extracting its central plane projection; 3) Convert fan‐beam projection to parallel‐beam projection utilizing sinogram‐based rebinning algorithm or detail‐based rebinning algorithm; 4) Obtain HLCC profile by integrating parallel‐beam projection per view and calculate wave percentage and variance of the HLCC profile, which can be used to describe the severity level of image artifacts.

Results:

Several sets of dental CBCT projections containing only one type of artifact (i.e. geometry, scatter, beam hardening, lag and noise artifact), were simulated using gDRR, a GPU tool developed for efficient, accurate, and realistic simulation of CBCT Projections. These simulated CBCT projections were used to test our proposed method. HLCC profile wave percentage and variance induced by geometry distortion are about 3∼21 times and 16∼393 times as large as that of the artifact‐free projection, respectively. The increase factor of wave percentage and variance are 6 and133 times for beam hardening, 19 and 1184 times for scatter, and 4 and16 times for lag artifacts, respectively. In contrast, for noisy projection the wave percentage, variance and inconsistency level are almost the same with those of the noise‐free one.

Conclusion:

We have proposed a quantitative evaluation method of image artifact based on HLCC theory. According to our simulation results, the severity of different artifact types is found to be in a following order: Scatter>Geometry>Beam hardening>Lag>Noise>Artifact‐free in dental CBCT
WE‐AB‐209‐08: Novel Beam‐Specific Adaptive Margins for Reducing Organ‐At‐Risk Doses
Authors: Tsang H , Kamerling CP , Ziegenhein P , Nill S , Oelfke U
Published: July 2016

Purpose:

Current practice of using 3D margins in radiotherapy with high‐energy photon beams provides larger‐than‐required target coverage. According to the photon depth‐dose curve, target displacements in beam direction result in minute changes in dose delivered. We exploit this behavior by generating margins on a per‐beam basis which simultaneously account for the relative distance of the target and adjacent organs‐at‐risk (OARs).

Methods:

For each beam, we consider only geometrical uncertainties of the target location perpendicular to beam direction. By weighting voxels based on its proximity to an OAR, we generate adaptive margins that yield similar overall target coverage probability and reduced OAR dose‐burden, at the expense of increased target volume. Three IMRT plans, using 3D margins and 2D per‐beam margins with and without adaptation, were generated for five prostate patients with a prescription dose Dpres of 78Gy in 2Gy fractions using identical optimisation constraints. Systematic uncertainties of 1.1, 1.1, 1.5mm in the LR, SI, and AP directions, respectively, and 0.9, 1.1, 1.0mm for the random uncertainties, were assumed. A verification tool was employed to simulate the effects of systematic and random errors using a population size of 50,000. The fraction of the population that satisfies or violates a given DVH constraint was used for comparison.

Results:

We observe similar target coverage across all plans, with at least 97.5% of the population meeting the D98%>95%Dpres constraint. When looking at the probability of the population receiving D5<70Gy for the rectum, we observed median absolute increases of 23.61% (range, 2.15%–27.85%) and 6.97% (range, 0.65%–17.76%) using per‐beam margins with and without adaptation, respectively, relative to using 3D margins.

Conclusion:

We observed sufficient and similar target coverage using per‐beam margins. By adapting each per‐beam margin away from an OAR, we can further reduce OAR dose without significantly lowering target coverage probability by irradiating more less‐important tissues.

This work is supported by Cancer Research UK under Programme C33589/A19908. Research at ICR is also supported by Cancer Research UK under Programme C33589/A19727 and NHS funding to the NIHR Biomedical Research Centre at RMH and ICR.
WE‐AB‐207B‐01: Dose Tolerance for SBRT/SABR
Authors: Grimm J
Published: July 2016

Purpose:

Stereotactic body radiation therapy (SBRT) / stereotactic ablative body radiotherapy (SABR) is gaining popularity, but quantitative dose tolerance has still been lacking. To improve this, the April 2016 issue of Seminars in Radiation Oncology will have normal tissue complication probability (NTCP) models for 10 critical structures: optic pathway, cochlea, oral mucosa, esophagus, chestwall, aorta, bronchi, duodenum, small bowel, and spinal cord.

Methods:

The project included more than 1500 treatments in 1–5 fractions using CyberKnife, Gamma Knife, or LINAC, with 60 authors from 15 institutions. NTCP models were constructed from the 97 grade 2–3 complications, predominantly scored using the common terminology criteria for adverse events (CTCAEv4). Dose volume histogram (DVH) data from each institutional dataset was loaded into the DVH Evaluator software (DiversiLabs, LLC, Huntingdon Valley, Pa) for modeling. The current state of the literature for the critical structures was depicted using DVH Risk Maps: comparative graphs of dose tolerance limits that can include estimated risk levels, reported complications, DVH data for study patients, as well as high‐ and low‐risk dose tolerance limits.

Results:

For relatively acceptable toxicity like grade 1–3 rib fractures and chestwall pain, the high‐risk limits have 50% risk and the low‐risk limits have 5% risk. Emami et al (IJROBP 1991 May 15;21(1):109–22) used 50% and 5% risk levels for all structures, whereas this effort used clinically acceptable ranges for each: in structures like aorta or spinal cord where complications must be avoided, the high‐ and low‐risk limits have about 3% and 1% risk, respectively, in this issue of Seminars. These statistically based guidelines can help ensure plan quality for each patient.

Conclusion:

NTCP for SBRT is now becoming available. Hypofractionated dose tolerance can be dramatically different than extrapolations of conventional fractionation so NTCP analysis of the SBRT/SBRT data is important to ensure safe clinical practice.

Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and was used to analyze the data.
WE‐AB‐207B‐03: A Computational Methodology for Determination of CTV‐To‐PTV Margins with Inter Fractional Shape Variations Based On a Statistical Point Distribution Model for Prostate Cancer Radiation Therapy
Authors: Shibayama Y , Arimura H , Hirose T , Nakamura K , Sasaki T , Ohga S , Umezu Y , Nakamura Y , Honda H
Published: July 2016

Purpose:

Our assumption was that interfractional shape variations of target volumes could not be negligible for determination of clinical target volume (CTV)‐to‐planning target volume (PTV) margins. The aim of this study was to investigate this assumption as a simulation study by developing a computational framework of CTV‐to‐PTV margins with taking the interfractional shape variations into account based on point distribution model (PDM)

Methods:

The systematic and random errors for interfractional shape variations and translations of target volumes were evaluated for four types of CTV regions (only a prostate, a prostate plus proximal 1‐cm seminal vesicles, a prostate plus proximal 2‐cm seminal vesicles, and a prostate plus whole seminal vesicles). The CTV regions were delineated depending on prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The random and systematic errors for shape variations of CTV regions were derived from PDMs of CTV surfaces for all fractions of each patient. Systematic errors of shape variations of CTV regions were derived by comparing PDMs between planning CTV surfaces and average CTV surfaces. Finally, anisotropic CTV‐to‐PTV margins with shape variations in 6 directions (anterior, posterior, superior, inferior, right, and left) were computed by using a van Herk margin formula.

Results:

Differences between CTV‐to‐PTV margins with and without shape variations ranged from 0.7 to 1.7 mm in anterior direction, 1.0 to 2.8 mm in posterior direction, 0.8 to 2.8 mm in superior direction, 0.6 to 1.6 mm in inferior direction, 1.4 to 4.4 mm in right direction, and 1.3 to 5.2 mm in left direction.

Conclusion:

More than 1.0 mm additional margins were needed at least in 3 directions to guarantee CTV coverage due to shape variations. Therefore, shape variations should be taken into account for the determination of CTV‐to‐PTV margins.
WE‐AB‐207B‐06: Dose and Biological Uncertainties in Sarcoma
Authors: Marteinsdottir M , Schuemann J , Paganetti H
Published: July 2016

Purpose:

To understand the clinical impact of key uncertainties in proton therapy potentially affecting the analysis of clinical trials, namely the assumption of using a constant relative biological effectiveness (RBE) of 1.1 compared to variable RBE for proton therapy and the use of analytical dose calculation (ADC) methods.

Methods:

Proton dose distributions were compared for analytical and Monte Carlo (TOPAS) dose calculations. In addition, differences between using a constant RBE of 1.1 (RBE‐constant) were compared with four different RBE models (to assess model variations). 10 patients were selected from an ongoing clinical trial on IMRT versus scanned protons for sarcoma. Comparisons were performed using dosimetric indices based on dose‐volume histogram analyses and γ‐index analyses.

Results:

For three of the RBE‐models the mean dose, D95, D50 and D02 (dose values covering 95%, 50% and 2% of the target volume, respectively) were up to 5% lower than for RBE‐constant. The dosimetric indices for one of the RBE‐models were around 9% lower than for the RBE‐constant model. The differences for V90 (the percentage of the target volume covered by 90% of the prescription dose) were up to 40% for three RBE‐models, whereas for one the difference was around 95%. All ADC dosimetric indices were up to 5% larger than for RBE‐constant. The γ‐index passing rate for the target volume with a 3%/3mm criterion was above 97% for all models except for one, which was below 24%.

Conclusion:

Interpretation of clinical trials on sarcoma may depend on dose calculation uncertainties (as assessed by Monte Carlo). In addition, the biological dose distribution depends notably on which RBE model is utilized. The current practice of using a constant RBE of 1.1 may overestimate the target dose by as much as 5% for biological dose calculations. Performing an RBE uncertainty analysis is recommended for trial analysis.

U19 projects ‐ U19 CA 021239. PI: Delaney
WE‐AB‐207B‐07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research
Authors: Folkerts MM , Long T , Radke RJ , Tian Z , Jia X , Chen M , Lu W , Jiang SB
Published: July 2016

Purpose:

To provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities.

Methods:

Generating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence‐preserving manner. Using PSM‐processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re‐combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape‐based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence‐convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research.

Results:

We used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7‐beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage.

Conclusions:

We successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large‐scale radiation therapy optimization research.
WE‐AB‐207B‐09: Margin Reduction for Planning Target Volume (PTV) in Patients with Localized Prostate Cancer: Impact On Delivered Dose and Quality of Life
Authors: Kumarasiri A , Liu C , Brown S , Glide‐Hurst C , Elshaikh M , Chetty I , Movsas B
Published: July 2016

Purpose:

To estimate the delivered (cumulative) dose to targets and organs at risk for localized prostate cancer patients treated with reduced PTV margins and to evaluate preliminary patient reported quality‐of‐life (QOL).

Methods:

Under an IRB‐approved protocol, 20 prostate cancer patients (including 11 control patients) were treated with reduced planning margins (5 mm uniform with 4 mm at prostate/rectum interface). Control patients had standard margin (10/6 mm)‐based treatments. A parameter‐optimized Elastix algorithm along with energy‐mass mapping was used to deform and resample dose of the day onto the planning CT for each fraction to estimate the delivered dose over all fractions. QOL data were collected via Expanded Prostate cancer Index Composite (EPIC‐26) questionnaires at time points pre‐treatment, post‐treatment, and at 2, 6, 12, 18 month follow‐ups. Standardized QOL scores [range: 0–100] were determined and baseline‐corrected by subtracting pre‐treatment QOL data. Mean QOL differences between the margin reduced group and control group (QOLmr‐QOLcontrol) were calculated for first 18 months.

Results:

The difference between the cumulative mean dose (Dmean) and the planned mean dose (±SD) for PTV, prostate, bladder, and rectum were −2.2±1.0, 0.3±0.5, −0.7±2.6, and −2.1±1.3 Gy respectively for the margin‐reduced group, and −0.8±2.0, 0.9±1.4, ‐ 0.7±3.1 and −1.0±2.4 Gy for the control group. Difference between the two groups was statistically insignificant (p=0.1). Standardized and baseline corrected QOLmr‐QOLcontrol for EPIC domains categorized as “Urinary Incontinence”, “Urinary Irritative/Obstructive”, “Bowel”, “Sexual”, and “Hormonal” were 0.6, 12.1, 9.1, 13.3, and −0.9 for the 18 months following radiation therapy (higher values better). Delivered dose to rectum showed a weak correlation to “Bowel” domain (Pearson's coefficient −0.24, p<0.001), while bladder dose did not correlate to Urinary Incontinence/Irritative/Obstructive QOL domains.

Conclusion:

The margin‐reduced group exhibited clinically meaningful improvement of QOL without compromising the PTV dose. A larger number of patients and greater follow‐up is needed to draw unequivocal conclusions.

This work was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA
WE‐AB‐207B‐11: Optimizing Tumor Control Probability in Radiation Therapy Treatment ‐ Application to HDR Cervical Cancer
Authors: Lee E , Yuan F , Templeton A , Yao R , Chu J
Published: July 2016

Purpose:

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor‐control‐probability(TCP) with an acceptable normal‐tissue‐complication probability(NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. We design treatment plans that optimize TCP directly and contrast them with the clinical dose‐based plans. PET image is incorporated to evaluate gain in TCP for dose escalation.

Methods:

We build a nonlinear mixed integer programming optimization model that maximizes TCP directly while satisfying the dose requirements on the targeted organ and healthy tissues. The solution strategy first fits the TCP function with a piecewise‐linear approximation, then solves the problem that maximizes the piecewise linear approximation of TCP, and finally performs a local neighborhood search to improve the TCP value. To gauge the feasibility, characteristics, and potential benefit of PET‐image guided dose escalation, initial validation consists of fifteen cervical cancer HDR patient cases. These patients have all received prior 45Gy of external radiation dose. For both escalated strategies, we consider 35Gy PTV‐dose, and two variations (37Gy‐boost to BTV vs 40Gy‐boost) to PET‐image‐pockets.

Results:

TCP for standard clinical plans range from 59.4% ‐ 63.6%. TCP for dose‐based PET‐guided escalated‐dose‐plan ranges from 63.8%–98.6% for all patients; whereas TCP‐optimized plans achieves over 91% for all patients. There is marginal difference in TCP among those with 37Gy‐boosted vs 40Gy‐boosted. There is no increase in rectum and bladder dose among all plans.

Conclusion:

Optimizing TCP directly results in highly conformed treatment plans. The TCP‐optimized plan is individualized based on the biological PET‐image of the patients. The TCP‐optimization framework is generalizable and has been applied successfully to other external‐beam delivery modalities. A clinical trial is on‐going to gauge the clinical significance.

Partially supported by the National Science Foundation.
WE‐B‐201‐00: Statistical Failings That Keep Us All in the Dark
Authors: Schlesinger David
Published: July 2016

Evidence is growing to suggest that many published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most if not all base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in the literature. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately too frequent occurrence in clinical papers.

In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer‐reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p‐values and clinical significance, misinterpreting statistical power, not accounting for multiple‐hypothesis testing, ignoring missing data, and faulty survival analysis.

Learning Objectives:

Learn about the presence of statistical problems in published studies

Identify common signs and symptoms of potential problems in various types of statistical tests

Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications
WE‐D‐204‐00: Session in Memory of Franca Kuchnir: Excellence in Medical Physics Residency Education
Authors: Hendrickson Kristi
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:

To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.

To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐D‐204‐02: Errors and Process Improvements in Radiation Therapy
Authors: Fontenla D.
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:

To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.

To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐DE‐201‐02: A Statistical Analysis Tool for Plan Quality Verification in HDR Brachytherapy Forward Planning for Cervix Cancer
Authors: Ma R , Zhu X , Li S , Zheng D , Lei Y , Wang S , Verma V , Bennion N , Wahl A , Zhou S
Published: July 2016

Purpose:

High Dose Rate (HDR) brachytherapy forward planning is principally an iterative process; hence, plan quality is affected by planners’ experiences and limited planning time. Thus, this may lead to sporadic errors and inconsistencies in planning. A statistical tool based on previous approved clinical treatment plans would help to maintain the consistency of planning quality and improve the efficiency of second checking.

Methods:

An independent dose calculation tool was developed from commercial software. Thirty‐three previously approved cervical HDR plans with the same prescription dose (550cGy), applicator type, and treatment protocol were examined, and ICRU defined reference point doses (bladder, vaginal mucosa, rectum, and points A/B) along with dwell times were collected. Dose calculation tool then calculated appropriate range with a 95% confidence interval for each parameter obtained, which would be used as the benchmark for evaluation of those parameters in future HDR treatment plans. Model quality was verified using five randomly selected approved plans from the same dataset.

Results:

Dose variations appears to be larger at the reference point of bladder and mucosa as compared with rectum. Most reference point doses from verification plans fell between the predicted range, except the doses of two points of rectum and two points of reference position A (owing to rectal anatomical variations & clinical adjustment in prescription points, respectively). Similar results were obtained for tandem and ring dwell times despite relatively larger uncertainties.

Conclusion:

This statistical tool provides an insight into clinically acceptable range of cervical HDR plans, which could be useful in plan checking and identifying potential planning errors, thus improving the consistency of plan quality.