Prostate cancer is among the most common cancers in men, and radiotherapy is a standard treatment option. Accurate dose delivery requires accounting for daily anatomical changes in the prostate and nearby organs at risk, particularly the bladder and rectum. To enable this, image-guided radiotherapy (IGRT) protocols typically acquire a cone-beam CT (CBCT) at each treatment fraction and register it to the pre-treatment planning CT. Deformable image registration (DIR) between the planning CT and daily cone-beam CT (CBCT) is commonly used for this purpose. However, DIR often fails on CBCT due to its low soft-tissue contrast and imaging artifacts. To address these limitations, recent work has explored the use of deep learning models such as CycleGANs to generate synthetic CT (sCT) from CBCT, improving image quality for registration. Building on this idea, we developed an improved CycleGAN architecture to reduce CBCT distortions and hallucinations. Beyond intensity information, registration accuracy can also be enhanced with point-to-distance map (PD) constraints derived from organ contours. These constraints may come from manual expert segmentations or from fully automated approaches such as TotalSegmentator, which generates multi-organ segmentations directly from CT volumes. Our project is structured in two phases: (i) refining sCT generation with our improved CycleGAN, and (ii) developing an automated pipeline to benchmark sCT performance in downstream tasks including organ segmentation and deformable registration. The overall aim is to provide clinicians with a risk-aware framework that allows them to choose between manual, semi-automated, or fully AI-driven workflows for pelvic image-guided radiotherapy.

[This corrects the article DOI: 10.1016/j.phro.2025.100865.].

BACKGROUND AND PURPOSE:Delineating the clinical target volume (CTV) is an essential part of a radiation oncologist's practice. However, this process is subject to variability due to factors such as clinical experience, institutional standards, and contouring guidelines being followed. We propose a parameterized CTV delineation model for glioma tumors that can tailor to CTV delineation practices. The model is validated based on its ability to replicate different physician-delineated CTVs within our institution. MATERIAL AND METHODS:Five delineation parameters define the model-based CTV for glioma: CTV margin expansion, reduction of CTV margins in the brainstem and optic structures for reduced likelihood of tumor infiltration or preferential sparing, and increase of CTV margins at the ventricles and falx cerebri to account for positional uncertainties. Parameters are fitted by matching the model CTV to the manually-delineated CTVs for patients, maximizing the Dice score. The model is developed using 93 retrospectively collected glioma cases treated by three physicians in our institution. The parameter values for different physicians provide CTV delineation starting points, which was tested by training a classification model using fitted CTV parameters. RESULTS:Physician quality assessments showed no statistically significant difference in quality ratings between model-generated and physician-delineated CTVs (p > 0.05), with both groups achieving an average quality rating of 2.3 (0: unusable, 1: major edits, 2: minor edits, 3: acceptable). The distribution of the fitted delineation parameters varied systematically across physicians, especially in the CTV contours near the brainstem and ventricles. The best-performing classification model identified physician-specific CTV delineation starting points with 80% accuracy and Cohen's Kappa score of 0.68. CONCLUSION:Our parametric CTV delineation system effectively captures and quantifies physician contouring practices. The model can be used in a contour quality assurance program to promote consensus-building and CTV standardization across physician practices.

Objective. The delineation of the clinical target volume (CTV) in radiotherapy is fundamentally uncertain due to the invisibility of microscopic disease on medical images. The ICRU 83 report acknowledges this by proposing a probabilistic interpretation of the CTV, but it does not define how to compute the probability of microscopic tumor presence (MTP) in tissue. This work addresses this gap by introducing a novel stochastic model that estimates the probability of MTP at the voxel level based on local spatial correlations in the voxels’ neighborhood. Approach. We developed two first-principles stochastic models to simulate MTP under different assumptions, incorporating spatial correlation between neighboring voxels. The constant marginal probability (CMP) model assumes spatially uniform MTP and is suited for tumors without radial dependence on the distance from the gross tumor volume (GTV). The variable marginal probability (VMP) model introduces radial dependence, modeling decreasing MTP with distance from the GTV. The CMP model was evaluated on prostate cancer data, while the VMP model was assessed using breast and lung cancer data. Results. Both models accurately reproduced the fraction of times that MTP is present. In the prostate case, the CMP model estimated a marginal probability of MTP of 0.03, consistent with a literature report that indicates an average total microscopic tumor volume of approximately 583 mm 3 across patients. The VMP model successfully replicated the radial distribution of tumor islets, achieving mean absolute errors of 0.01 mm and 0.011 mm for breast and lung cancer distance distributions, respectively. However, not all MTP characteristics could be fully captured by the models, and in some cases discrepancies with population based tumor characteristics remain. Significance. This work introduces a statistically consistent framework that enables a probabilistic definition of the CTV. The proposed models provide a new way to capture key aspects of microscopic disease spread by introducing local voxel correlations.

Objective. Efficient image guidance and online adaptive treatment are essential for the success of gantry-less proton therapy (PT). Low-field magnetic resonance imaging (MRI) is a viable option for image guidance, but scan time can limit the quality of low-field MRI images. This study aims to investigate the impact of MRI image quality on deformable image registration (DIR) performance. Approach. We propose a Chebyshev-polynomial-based DIR method, which calculates a mapping between the voxels of a high-quality source image and a lower-quality target image. We prepared a longitudinal breast MRI dataset and synthesized lower-quality target images with four image resolutions and noise levels. For evaluation, we assumed the registration between a pair of high-quality images as the reference registration. We calculated the root-mean-square error (RMSE) between the warped image and the reference target image, as well as between the warped images aligned with high- and lower-quality target images. Deformable vector field (DVF) errors were calculated based on the reference DVF. We obtained binary masks for glandular tissue and calculated Dice coefficients after DIR. The method was further validated with a volunteer breast MRI study with intentional movements between two scan sets and a longitudinal pelvic MRI dataset that includes two contours. Comparison studies with commercial software and open-source software were performed. Main results. Although the quantitative metrics worsened with higher levels of undersampling or increased noise, the RMSE between the warped and target images was substantially reduced compared to the RMSE between the source and target images before registration, even when the target images were severely degraded. Dice coefficients were also considerably increased under various image degradation scenarios. Significance. We have developed a Chebyshev-polynomial-based DIR method and demonstrated its performance with high-quality source and lower-quality target images. This study could help optimize MRI for adaptive gantry-less PT.

Background and purpose:Diffusion tensor imaging (DTI) has been proposed to guide the anisotropic expansion from gross tumor volume to clinical target volume (CTV), aiming to integrate known tumor spread patterns into the CTV. This study investigate the potential of using a DTI atlas as an alternative to patient-specific DTI for generating anisotropic CTVs. Materials and Methods:The dataset consisted of twenty-eight newly diagnosed glioblastoma patients from a Danish national DTI protocol with post-operative T1-contrast and DTI imaging. Three different DTI atlases, spatially aligned to the patient images using deformable image registration, were considered as alternatives. Anisotropic CTVs were constructed to match the volume of a 15 mm isotropic expansion by generating 3D distance maps using either patient- or atlas-DTI as input to the shortest path solver. The degree of CTV anisotropy was controlled by the migration ratio, modeling tumor cell migration along the dominant white matter fiber direction extracted from DTI. The similarity between patient- and atlas-DTI CTVs was analyzed using the Dice Similarity Coefficient (DSC), with significance testing according to a Wilcoxon test. Results:The median (range) DSC between anisotropic CTVs generated using patient-specific and atlas-based DTI was 0.96 (0.93-0.97), 0.96 (0.93-0.97), and 0.95 (0.93-0.97) for the three atlases, respectively (p > 0.01), for a migration ratio of 10. The results remained consistent over the range of studied migration ratios (2 to 100). Conclusion:The high degree of similarity between all anisotropic CTVs indicates that atlas-DTI is a viable replacement for patient-specific DTI for incorporating fiber direction into the CTV.

Background and purpose:Delineating clinical target volumes (CTVs) for glioma is challenging as consistency with the neuroanatomy needs to be carefully verified. We developed an automated approach that incorporates tumor infiltration pathways and anatomic barriers to improve the neuroanatomical consistency and efficiency of CTV delineation. Materials and methods:A deep learning model for brain structure segmentation was developed based on manual delineations of hemispheres, brainstem, cerebellum, optic chiasm, optic nerves, ventricles, and midline on CT images of ninety-nine glioma patients. Brain structures predictions are integrated into a constrained distance transform that defines the CTV as a 15-mm expansion of the gross tumor volume. Connecting structures with white matter tracts allow for expansions across different structure boundaries, e.g., cerebellum and brainstem connecting at the cerebellar peduncles. Results:Mean (±std) Dice Similarity Coefficient (DSC) for the hemispheres, brainstem, cerebel-lum, chiasm, optic nerves, midline and ventricles were (98.5 ± 0.8)%, (92.5 ± 2.8)%, (96.7 ± 2.2)% (63.9 ± 12.2)%, (83.8 ± 9.0)%, (81.2 ± 7.0) and (91.5 ± 3.9)%. Mean (±std) 95 % Hausdorff distance (HD95) were, in mm, 1.9 ± 2.5, 7.0 ± 5.4, 1.8 ± 1.2, 7.2 ± 3.2, 2.3 ± 1.0, 9.5 ± 10.5, and 3.8 ± 3.1, respectively. Auto-generated CTVs are compared against reference CTVs (15-mm expansion constrained by manually-contoured brain structures). The automatic CTVs showed excellent similarity to the reference CTVs with mean (±std) Surface DSC with 2 mm tolerance and HD95 scores of (95.6 ± 3.4)% and (1.4 ± 1.2) mm, respectively. A physician's quality assessment reported that the automated method would result in a substantial amount of time saved in 85 % of CTV delineations. Conclusion:We have successfully incorporated expert knowledge to improve the neuroanatom-ical consistency of automatically-generated CTVs for glioma.

Objective. A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles. Approach. To compensate for the poor soft-tissue contrast of CT images, muscle fiber orientation is derived from cryo-images of two cadavers from the human visible project (VHP). The approach consists of (a) detecting image gradients in the cadaver images representative of muscle fibers, (b) mapping this information onto the patient image, and (c) embedding the muscle fiber orientation into an expansion method to generate patient-specific clinical target volumes (CTV). The validation tested the consistency of image gradient orientation across VHP subjects for the piriformis, gluteus maximus, paraspinal, gluteus medius, and gluteus minimus muscles. The model robustness was analyzed by comparing CTVs generated using different VHP subjects. The difference in shape between the new CTVs and standard CTV was analyzed for clinical impact. Main results. Good agreement was found between the image gradient orientation across VHP subjects, as the voxel-wise median cosine similarity was at least 0.86 (for the gluteus minimus) and up to 0.98 for the piriformis. The volume and surface similarity between the CTVs generating from different VHP subjects was on average at least 0.95 and 5.13 mm for the Dice Similarity Coefficient and the Hausdorff 95% Percentile Index, showing excellent robustness. Finally, compared to the standard CTV with different margins in muscle and non-muscle tissue, the new CTV margins are reduced in muscle tissue depending on the chosen clinical margins. Significance. This study implements a method to integrate muscle fiber orientation into the target volume without the need for additional imaging.

Purpose:Deformable image registration (DIR) plays a critical role in adaptive radiation therapy (ART) to accommodate anatomical changes. However, conventional intensity-based DIR methods face challenges when registering images with unequal image intensities. In these cases, DIR accuracy can be improved using a hybrid image similarity metric which matches both image intensities and the location of known structures. This study aims to assess DIR accuracy using a hybrid similarity metric and leveraging CycleGAN-based intensity correction and auto-segmentation and comparing performance across three DIR workflows. Methods:The proposed approach incorporates a hybrid image similarity metric combining a point-to-distance (PD) score and intensity similarity score. Synthetic CT (sCT) images were generated using a 2D CycleGAN model trained on unpaired CT and CBCT images, improving soft-tissue contrast in CBCT images. The performance of the approach was evaluated by comparing three DIR workflows: (1) traditional intensity-based (No PD), (2) auto-segmented contours on sCT (CycleGAN PD), and (3) expert manual contours (Expert PD). A 3D U-Net model was then trained on two datasets comprising 56 3D images and validated on 14 independent cases to segment the prostate, bladder, and rectum. DIR accuracy was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and fiducial separation metrics. Results:The hybrid similarity metric significantly improved DIR accuracy. For the prostate, DSC increased from 0.61 ± 0.18 (No PD) to 0.82 ± 0.13 (CycleGAN PD) and 0.89 ± 0.05 (Expert PD), with corresponding reductions in 95% HD from 11.75 mm to 4.86 mm and 3.27 mm, respectively. Fiducial separation was also reduced from 8.95 mm to 4.07 mm (CycleGAN PD) and 4.11 mm (Expert PD) (p < 0.05). Improvements in alignment were also observed for the bladder and rectum, highlighting the method's robustness. Conclusion:A hybrid similarity metric that uses CycleGAN-based auto-segmentation presents a promising avenue for advancing DIR accuracy in ART. The study's findings suggest the potential for substantial enhancements in DIR accuracy by combining AI-based image correction and auto-segmentation with classical DIR.

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy. The ICT model allows the mapping of tumor cell densities and tumor fronts (as iso-levels of tumor cell density) from the template image to the patient image for inclusion in radiotherapy treatment planning. The proposed approach transforms the diffusion tensors to simulate tumor growth in locally deformed anatomy and outputs the tumor cell density distribution over time. The ICT model is validated in a cohort of ten brain tumor patients. Comparative analysis with the tumor cell density in the original template image shows that the ICT model accurately simulates tumor cell densities in the deformed image space. By creating radiotherapy target volumes as tumor fronts, this study provides a framework for more personalized radiotherapy treatment planning, without the use of additional imaging.

Objective. This study describes geometry-based and intensity-based tools for quality assurance (QA) of automatically generated structures for online adaptive radiotherapy, and designs an operator-independent traffic light system that identifies erroneous structure sets. Approach. A cohort of eight head and neck (HN) patients with daily CBCTs was selected for test development. Radiotherapy contours were propagated from planning computed tomography (CT) to daily cone beam CT (CBCT) using deformable image registration. These propagated structures were visually verified for acceptability. For each CBCT, several error scenarios were used to generate what were judged unacceptable structures. Ten additional HN patients with daily CBCTs and different error scenarios were selected for validation. A suite of tests based on image intensity, intensity gradient, and structure geometry was developed using acceptable and unacceptable HN planning structures. Combinations of one test applied to one structure, referred to as structure-test combinations, were selected for inclusion in the QA system based on their discriminatory power. A traffic light system was used to aggregate the structure-test combinations, and the system was evaluated on all fractions of the ten validation HN patients. Results. The QA system distinguished between acceptable and unacceptable fractions with high accuracy, labeling 294/324 acceptable fractions as green or yellow and 19/20 unacceptable fractions as yellow or red. Significance. This study demonstrates a system to supplement manual review of radiotherapy planning structures. Automated QA is performed by aggregating results from multiple intensity- and geometry-based tests.

Objective. Current radiotherapy guidelines for glioma target volume definition recommend a uniform margin expansion from the gross tumor volume (GTV) to the clinical target volume (CTV), assuming uniform infiltration in the invaded brain tissue. However, glioma cells migrate preferentially along white matter tracts, suggesting that white matter directionality should be considered in an anisotropic CTV expansion. We investigate two models of anisotropic CTV expansion and evaluate their clinical feasibility. Approach. To incorporate white matter directionality into the CTV, a diffusion tensor imaging (DTI) atlas is used. The DTI atlas consists of water diffusion tensors that are first spatially transformed into local tumor resistance tensors, also known as metric tensors, and secondly fed to a CTV expansion algorithm to generate anisotropic CTVs. Two models of spatial transformation are considered in the first step. The first model assumes that tumor cells experience reduced resistance parallel to the white matter fibers. The second model assumes that the anisotropy of tumor cell resistance is proportional to the anisotropy observed in DTI, with an 'anisotropy weighting parameter' controlling the proportionality. The models are evaluated in a cohort of ten brain tumor patients. Main results. To evaluate the sensitivity of the model, a library of model-generated CTVs was computed by varying the resistance and anisotropy parameters. Our results indicate that the resistance coefficient had the most significant effect on the global shape of the CTV expansion by redistributing the target volume from potentially less involved gray matter to white matter tissue. In addition, the anisotropy weighting parameter proved useful in locally increasing CTV expansion in regions characterized by strong tissue directionality, such as near the corpus callosum. Significance. By incorporating anisotropy into the CTV expansion, this study is a step toward an interactive CTV definition that can assist physicians in incorporating neuroanatomy into a clinically optimized CTV.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

Objective. The goal of this research is to demonstrate proof-of-principle for managing intrafraction motion via feedback control of delivered dose to achieve dosimetry comparable to respiratory gating without compromising delivery efficiency. Approach. We develop a stochastic control approach for step-and-shoot intensity-modulated radiotherapy (IMRT) in which the cumulative delivered dose and future trajectory of intrafraction motion are dynamically estimated by combining pre-treatment four-dimensional computed tomography imaging and intrafraction respiratory-motion surrogates. The IMRT plan is then re-optimized in real time to ensure delivery of the planned dose in the presence of free-breathing motion. We compare the performance of the proposed approach against traditional motion-management techniques, namely, respiratory gating and internal target volume (ITV) planning, using the four-dimensional extended cardiac-torso computational phantom. Main results. We simulate the delivery of treatment plans for a lung tumor in the presence of variable breathing amplitude, tumor size, and location. Results show that the proposed method reduces irradiated tissue volume compared to ITV treatment. Additionally, it significantly reduces treatment time compared to traditional respiratory-gated treatment, without compromising the dosimetric quality. Significance. Respiratory gating is a common technique to manage intrafraction motion. While gating supports reduced treatment volumes, it also prolongs the treatment delivery time. The proposed stochastic control approach can help improve the delivery efficiency of respiratory gating without compromising the dose quality.

Background Adjuvant endocrine therapy (ET) is standard of care in women with hormone receptor-positive (HR+) breast cancer (BC) with the goal to reduce recurrence. However, early discontinuation of ET occurs in 30-40% of women, largely attributable to toxicity, and leads to increased recurrence risk. There is considerable overlap in risk factors that predict toxicity from ET and chemotherapy, including age, co-morbidities, and geriatric conditions. Baseline low skeletal muscle area (SMA) on chest computed tomography (CT) is a surrogate marker for sarcopenia and predicts for significant toxicity and intolerance to chemotherapy in women with BC. No study has assessed the association of sarcopenia with toxicity-related discontinuation of ET in women with early-stage HR+ BC. Methods This single center retrospective cohort study included consecutive women with Stage 0-II HR+ BC who received ET and adjuvant radiotherapy (RT) from 01/2011-12/2017. Inclusion required a minimum of 5-year clinical follow-up after diagnosis. We used a validated deep learning pipeline to quantify SMA (cm2) at the tenth thoracic (T10) vertebral body on existing RT planning CT. The skeletal muscle index (SMI [cm2/m2] = SMA/(patient height (m))2) was calculated to adjust for patient height. Sarcopenia was defined as SMI< 32.3 cm2/m2, based on a previously validated independent cohort of young healthy women. The primary endpoint was toxicity-related discontinuation of ET less than 60 months after initiation of ET. Secondary endpoints included any NCI CTCAE v5.0 Grade 3-5 toxicity from ET and ipsilateral breast tumor recurrence. We assessed associations between ET discontinuation and SMI (continuous), as well as thoracic sarcopenia (dichotomous), using logistic regression adjusting for baseline characteristics. We used cox proportional hazards regression to assess disease-free survival (DFS), defined as ipsilateral breast tumor recurrence, locoregional recurrence, or distant metastasis adjusting for baseline and treatment characteristics. Results A total of 265 women (median age 67 years) met inclusion criteria. The majority of women had a comorbidity index of 0-1 (89%) and were Caucasian (89%). The median follow-up was 82 months, 5-year overall survival was 96% and 5-year DFS was 94%. Diagnoses included DCIS (12%), IDC (76%), or ILC (12%); most were T1 (69%) or T2 (18%) and N0 (85%), ER-positive (100%), PR-positive (85%), or HER2-negative (9%). Most common ET type was anastrozole (63%), letrozole (16%), and tamoxifen (17%). SMI (continuous) was not associated with older age, Charlson Comorbidity Index (CCI), race, or tumor stage. A total of 64 (24%) women experienced toxicity-related early discontinuation of ET. On multivariate analysis (MVA), lower SMI was associated with increased toxicity-related early discontinuation of ET (Odds Ratio [OR] 0.89 per 1 cm2/m2 SMI, p=0.001) independent of age, CCI, ET type, or receipt of adjuvant chemotherapy. Lower SMI was associated with higher risk of grade 3-5 toxicity from ET (OR 0.89 per 1 unit SMI, p=0.001) independent of age, CCI, ET type, or receipt of adjuvant chemotherapy. On MVA, sarcopenia was associated with higher risk of toxicity-related early discontinuation of ET (OR 2.43, p=0.019). DFS was associated with toxicity-related early discontinuation of ET (HR 8.06, p=0.005), grade 3 histology (HR 1.42, p=0.042), and multifocal disease (HR 2.55, p=0.040), but not age, histology, stage, or lymphovascular invasion (p>.05 for all). Conclusion Low baseline thoracic skeletal muscle is associated with toxicity-related early ET discontinuation in women with early-stage HR+ BC. Further studies should attempt to generalize this association to all HR+ BC who are candidates for ET. High-risk patients may be candidates for aggressive symptom management or alternative adjuvant therapies. Citation Format: Anurag Saraf, Ismail Tahir, Bonnie Hu, Anna-Sophia Dietrich, Paul Erik Tonnesen, Greg Sharp, Gayle Tillman, Florian Fintelmann, Rachel Jimenez. Sarcopenia on baseline imaging is associated with toxicity-related discontinuation of endocrine therapy in women with early-stage hormone-positive breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-01.

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

PURPOSE:Sarcopenia, an age-related decline in muscle mass and physical function, is associated with increased toxicity and worse outcomes in women with breast cancer (BC). Sarcopenia may contribute to toxicity-related early discontinuation of adjuvant endocrine  therapy (aET) in women with hormone receptor-positive (HR+) BC but remains poorly characterized. METHODS AND MATERIALS:This multicenter, retrospective cohort study included consecutive women with stage 0-II HR+ BC who received breast conserving therapy (lumpectomy and radiation therapy) and aET from 2011 to 2017 with a 5-year follow-up. Skeletal muscle index (SMI, cm2/m2) was analyzed using a deep learning model on routine cross-sectional radiation simulation imaging; sarcopenia was dichotomized according to previously validated reports. The primary endpoint was toxicity-related aET discontinuation; logistic regression analysis evaluated associations between SMI/sarcopenia and aET discontinuation. Cox regression analysis evaluated associations with time to aET toxicity, ipsilateral breast tumor recurrence (IBTR), and disease-free survival (DFS). RESULTS:A total of 305 women (median follow-up, 89 months) were included with a median age of 67 years and early-stage BC (12% stage 0, 65% stage I). A total of 60 (20%) women experienced toxicity-related aET discontinuation. Sarcopenia was associated with toxicity-related early discontinuation of aET (odds ratio, 2.18; P = .036) and shorter time to aET toxicity (hazard ratio [HR], 1.62; P = .031). SMI or sarcopenia were not independently associated with IBTR or DFS; toxicity-related aET discontinuation was associated with worse IBTR (HR, 9.47; P = .002) and worse DFS (HR, 4.53; P = .001). CONCLUSIONS:Among women with early-stage HR+ BC who receive adjuvant radiation therapy and hormone therapy, sarcopenia is associated with toxicity-related early discontinuation of aET. Further studies should validate these findings in women who did not receive adjuvant radiation therapy. These high-risk patients may be candidates for aggressive symptom management and/or alternative treatment strategies to improve outcomes.