Citations

Abstract Background Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL‐based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy. Purpose To design a physics‐aware and generalizable AI‐based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases. Methods This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo‐based dose engine as the reference standard, we compared three methods: the region of interest (ROI)‐based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low‐statistics dose via uniformly weighted spots on an expanded spot‐placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cases with uncommon beam angles or prescription doses were used to assess their performance, which was evaluated using dose‐volume histogram (DVH) indices, 3D Gamma passing rates (3%/2 mm/10%), and Dice coefficients for dose agreement, while prediction times were measured to gauge model efficiency. Results The proposed noisy probing dose method consistently outperformed the ROI‐based and beam mask baselines across all evaluated metrics, with more accurate dose agreement and superior generalizability. For DVH indices, the noisy probing dose method achieved the smallest deviation in clinical target volume (CTV) dose coverage: in prostate cancer, CTV D98 deviation was reduced by 45% (from 0.53 ± 0.22 Gy [RBE] for ROI‐based) and 29% (from 0.41 ± 0.28 Gy [RBE] for beam mask) to 0.29 ± 0.06 Gy [RBE]; in lung cancer, similar improvements were observed, with CTV D98 deviations reduced to 0.34 ± 0.12 Gy [RBE]. The 3D Gamma passing rates improved to 99.65% ± 1.15% for prostate targets and 97.04% ± 1.17% for lung targets. The dice coefficients of the 90% iso‐dose lines were also the highest with the noisy probing dose method (prostate: 0.983 ± 0.005; lung: 0.967 ± 0.01). For the 12 outlier cases, the noisy probing dose method maintained superior generalizability, yielding higher 3D Gamma passing rates (prostate targets: 96.79% ± 0.83%, OARs: 94.29% ± 1.01%; lung targets: 93.38% ± 1.34%, OARs: 93.95% ± 1.32%), demonstrating robust generalizability to rare clinical scenarios. The dose predictions for all testing cases were completed within 0.3 seconds. Conclusions A novel noisy probing dose method was proposed for PBSPT dose prediction in prostate and lung cancer patients. By embedding more proton‐specific physics, this method demonstrated an improvement in the generalizability of dose prediction.

Biological heterogeneity among different breast cancer (BC) subtypes results in markedly varying clinical outcomes. Identification and analysis of key gene biomarkers that are differentially regulated during radiation therapy (RT) may pose multiple clinical challenges for BC treatment. The purpose of the study is to identify and analyze the expression of key gene biomarkers and their networks that are differentially regulated after hypofractionated RT. Patients with BC (cT0-T2, N0, M0) were treated with hypofractionated whole breast RT 25 Gy in five fractions, 4 to 8 weeks before breast conservation surgery (BCS). Biopsy (pre-RT; n = 5) and surgical (post-RT; n = 14 or 15) BC tumor samples were used for NanoString targeted sequencing. We identified 165 and 244 differentially expressed genes (DEGs; p < 0.05) in BC tumor samples from BC patients post-RT using the nCounter BC360 and IO360 panels, respectively. Gene networks and pathway analysis revealed that RT increases the gene signature of tumor inflammation (TIS), cytotoxicity, and apoptosis, while downregulating the gene signatures of tumor cell proliferation, differentiation, and cell adhesion, and increases the claudin-low gene score. RT-induced mammary stemness and enhanced infiltration of stroma, mast, and macrophage cells in the BC tumor microenvironment (TME). Further, the nCounter IO360 (immuno-oncology) panel analysis validated the findings of BC360 and demonstrated that RT increased the myeloid inflammation signature and chemokine expression, modulated B, T, NK, and DC cell activities, and enhanced residual cancer burden (RCB) in BC tumors, thus creating an immunosuppressive TME. Collectively, RT sensitized BC tumors by increasing the gene signature of TIS, cytotoxicity, apoptosis, and mammary stemness. RT facilitated an immunosuppressive environment and increased RCB, suggesting that the therapeutic potential of RT is highly individualized for each patient based on their unique tumor biology, genetic makeup, and TME.

Abstract Background The van Herk margin formula, derived for 3‐D conformal radiotherapy with a uniform 3.2 mm dose penumbra and no intrinsic dose buffer, remains widely used to design CTV‐to‐PTV expansions in contemporary image‐guided prostate radiotherapy. However, coplanar VMAT techniques often feature broader penumbrae and explicit PTV–isodose clearances, potentially violating the assumptions underlying the original formulation and leading to overly conservative margins. Purpose We investigated whether the widely adopted van Herk margin formula overestimates clinical target volume (CTV)‐to‐planning target volume (PTV) expansions for contemporary coplanar volumetric modulated arc therapy (VMAT) prostate treatments. Methods Fifty consecutive intact‐prostate VMAT radiotherapy plans (two coplanar arcs; clinical margins 3 mm, except for 2 mm posterior) were exported. Direction‐specific 90% isodose–to‐PTV gaps and penumbra widths were measured. Candidate anisotropic margins were tested by eroding the PTV to create CTV_eval. Monte‐Carlo simulations combined systematic (Σ) shifts with Gaussian random (σ′) blurring kernels of 0–2 mm were performed. Acceptability criteria of (i) CTV Dmin0.03 cc ≥ 90% Rx in ≥ 90% of simulated scenarios or (ii) population tumor‐control probability (TCP) loss < 1 % were used. Results VMAT plans exhibited intrinsic 90% isodose clearances of 3–5 mm laterally/anteriorly and 1–2 mm superior–inferiorly, while axial‐plane penumbras were up to fivefold broader than van Herk's assumption. With a [0, 0, 2] mm (LR/AP/SI) margin, ≥ 90% of patients maintained Dmin0.03 cc ≥ 90% Rx provided Σ lay within an ellipsoid of [2.0, 1.5, 1.8] mm and σ′ ≤ [1.5, 2.0, 1.5] mm. Under TCP criteria the safe Σ ellipsoid for high‐risk disease was [2.5, 1.9, 2.2] mm, while low‐intermediate risk was even less sensitive. Conclusions For image‐guided coplanar VMAT prostate radiotherapy, an anisotropic [0, 0, 2] mm CTV‐to‐PTV margin is sufficient for target coverage. A modified margin expression that subtracts the measured isodose–to‐PTV gap and uses reduced random‐error coefficients better reflects modern practice.

Abstract Background Partial breast irradiation (PBI) is an effective treatment for early-stage breast cancer. However, evidence comparing different PBI modalities is limited. Methods This trial emulation uses electronic health records from a multi-state large tertiary health system. Three PBI modalities were compared: proton radiation therapy, photon radiation therapy, and applicator-based brachytherapy. Inverse probability weighting (IPW) was used to balance confounders. The primary outcome was ipsilateral breast recurrence (IBR). Results Between 2013 and 2023, 1041 women with early-stage breast cancer were treated with PBI. Of these, 1026 patients were included in the analyses. The median age was 66 years; 22.51% had ductal carcinoma in situ (DCIS), and 97.56% were estrogen receptor positive. With median follow-up of 38.2 months, the rate of IBR in the overall group was 3.05% (95% confidence interval [CI] = 1.99% to 4.65%) at 3 years. After IPW, compared with photon PBI, the hazard ratio for IBR for proton PBI was 2.11 (95% CI = 0.70 to 6.33) and for brachytherapy, 3.86 (95% CI = 1.39 to 10.69). Compared with proton PBI, the hazard ratio for IBR for brachytherapy was 1.83 (95% CI = 0.88 to 3.81). IBR risk was similar across PBI modalities for patients with tumor size ≤ 10 mm and in patients treated for DCIS. Among patients with tumor size &gt;10 mm, the hazard ratio for IBR for brachytherapy was 7.64 (95% CI = 1.64 to 35.58) and 3.59 (95% CI = 1.22 to 10.56) relative to photons and protons, respectively. Conclusion PBI with applicator-based brachytherapy was associated with higher IBR than photon or proton PBI in patients with tumor size &gt; 10 mm, suggesting the need for more careful PBI patient selection for this modality.

Abstract Background The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose This study aims to investigate whether fine‐tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD‐10 code prediction in radiation oncology. Methods Data for 15 724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD‐10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD‐10 code, respectively) for the supervised fine‐tuning of these three tasks. Open source LLaMA2‐7B and Mistral‐7B models were utilized for the fine‐tuning with the Low‐Rank Approximations method. Accuracy and ROUGE‐1 score were reported for the fine‐tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F‐1 score were evaluated for Task (2) and (3). One‐sided Wilcoxon signed‐rank tests were used to statistically analyze the results. Results Fine‐tuned LLMs outperformed original LLMs across all tasks with p value ≤ 0.001. Clinical evaluation demonstrated that over 60% of the fine‐tuned LLMs‐generated treatment regimens were clinically acceptable. Precision, recall, and F1‐score showed improved performance of fine‐tuned LLMs. Conclusion Fine‐tuned LLMs demonstrated statistically significant improvements over original LLMs upon three clinically important tasks in radiation oncology. This study explored the feasibility of applying fine‐tuned LLMs in radiation oncology, inspiring further development of utilizing LLMs to assist with radiation oncology tasks.

Abstract Introduction: Radiotherapy (RT)-induced acute esophagitis (AE) is a common side effect in lung cancer patients receiving RT, which can significantly impact their quality of life. This highlights the need for a predictive model that can estimate AE risk in advance using pretreatment imaging data. However, collecting sufficient data for model development is often resource-intensive and costly. Additionally, acquiring a homogeneous training dataset (e.g., from a single center or modality) is not always feasible. This study aimed to develop an automated artificial intelligence-based 3D vision foundation model (VFM) combining standard-of-care planning computed tomography (pCT) and planned radiation dose maps to predict grade II or higher AE. Materials and methods: This study included 246 patients with non-small cell lung cancer who underwent either image-guided radiation treatment (IMRT) or proton-beam radiation therapy. The endpoint was grade two or higher AE (33% positive and 67% negative). The IMRT group consisted of 182 patients from a single center, whereas the proton therapy group included 64 patients from the Proton Collaborative Group trial from 11 different institutions. For each patient, pCT, dose maps, and radiotherapy segmentations were available. The VFM was created using a transformer pretrained using a large number of unlabeled volumetric 3D CTs from patients with varied diseases through a self-supervised learning approach, which extracts useful features directly from images. Next, the pretrained encoder was combined with fully connected classification layers and fine-tuned using stratified 5-fold cross-validation with 20 patients set aside for testing. We developed two models: a CT only and CT + dose model. Model performance was assessed using the area under the curve (AUC), specificity, and sensitivity. Results: In cross-validation, adding dose information to CT (CT+Dose) improved model performance, increasing AUC from 0.72 ± 0.08 to 0.76 ± 0.10 and specificity from 0.82 ± 0.09 to 0.87 ± 0.04, while sensitivity remained the same. On the independent test set, CT+Dose showed a marked improvement over CT only (AUC: 0.82 vs. 0.59; sensitivity: 0.57 vs. 0.14), with a slight decrease in specificity (0.77 vs. 1.00). Conclusion: A VFM model combining CT and radiation dose showed the capability to predict AE more accurately with higher specificity. Further studies on larger cohorts of testing patients are planned to assess model generalization. Citation Format: Chloe Min Seo Choi, Jue Jiang, Nikhil Mankuzhy, John Chang, Jing Zeng, Carlos Vargas, James Urbanic, Isabella Choi, Mark Mcdonald, James Gray, Joseph Deasy, Maria Thor, Charles Simone, Harini Veeraraghavan. Prediction of radiotherapy-Induced esophagitis in non-small cell lung cancer using a 3D vision foundation model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B022.

Abstract Background Inter‐fractional and intra‐fractional positioning and motion monitoring based on implanted fiducials through kilovoltage (kV) imaging is a cost‐effective approach to enhance intact prostate radiotherapy treatment accuracy. However, comprehensive studies for three‐dimensional (3D) position extraction and the corrective action threshold are lacking. Purpose To develop and verify a fiducial motion monitoring system with 3D position information based on triggered kV images taken during treatment delivery, and to comprehensively evaluate the recommended thresholds for corrective action during treatment. Methods An in‐house fiducial triangulation algorithm was developed to monitor fiducial positions using subsequent kV triggered images. The precision of fiducial triangulation and motion detection was validated on a pelvis phantom with four fiducial inserts. A retrospective study was conducted on prostate cancer patients who received either moderately hypofractionated radiotherapy (MHRT, n = 16) or stereotactic body radiotherapy (SBRT, n = 12), categorized by endo‐rectal balloon (ERB) use. Intra‐fractional positions relative to isocenter were computed, and these positions were analyzed in comparison to the original marker locations of the patient's treatment plan. A linear regression fit was used per fraction to determine correlation coefficients between motions in the left–right (LR), superior–inferior (SI), and anterior–posterior (AP) directions. Average fraction time (AFT) of treatment was reported based on the observed average time spent on steps in the workflow and the percentage of fiducials caught outside of the current two‐dimensional (2D) tolerance threshold of 5 mm. The observed 3D offsets were used to estimate AFT for various 3D tolerance thresholds. Results Overall, our triangulation method proved to be very precise for static cases, where phantom measurements revealed a fiducial position triangulation precision of mm for stationary targets, but had a spread of mm for targets with 1 mm of motion. Employing triangulation, true motion exceeding 5 mm was detected above 4.0 mm in magnitude 90.9% of the time, a noticeable improvement in comparison to the 28%–65% successful detection rate reported by 2D projection methods. Most detection errors were attributed to depth disparity. Similar to other reports, correlation coefficients for intra‐fractional motion generally indicated no LR/SI, no LR/AP, and weak positive SI/AP correlations for MHRT and SBRT patients. Offsets beyond a 3D tolerance threshold of 5 mm were observed at a rate of 4.25%–5.25%, while the 5 mm 2D out‐of‐tolerance catch‐rate was 1.6%. The AFT was 8.1–8.2 min using the 2D tolerance threshold of 5 mm. In comparison, the estimated AFT for the proposed 3D tolerance monitoring of offsets beyond 2–5 mm was slightly higher at 8.2–10.9 min due to the higher amount of out‐of‐tolerance instances for the higher precision intra‐fractional motion management. Conclusions Our study showcases a promising subsequent kV‐based triangulation method for intra‐fractional prostate motion monitoring. Acquiring 3D motion information results in higher out‐of‐tolerance catch‐rates particularly in the depth dimension of the kV images, which is perpendicular to the treatment beam. Failure to properly observe and catch these offsets would result in sub‐optimal conformality and accuracy of the dose delivery.

Abstract BACKGROUND Standard of care treatment for recurrent malignant glioma after prior radiotherapy (RT) is not well established. Proton therapy (PT) is increasingly used for reirradiation (ReRT) at the time of recurrence; however, treatment outcomes, toxicity, and prognostic factors for patients treated with PT-ReRT remain poorly defined. METHODS The prospective, multi-institutional Proton Collaborative Group (PCG) registry was queried for patients with malignant glioma who previously received RT and underwent PT-ReRT between 7/2011-12/2023. Overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method, and Cox proportional hazards regression was used for uni- and multivariate analysis (UVA and MVA). Dose was calculated using equivalent dose in 2-Gy fractions (EQD2). RESULTS 143 consecutive patients were identified. Median follow-up was 11.2 months and median time interval (TI) from prior RT (median 58.5 Gy) to PT-ReRT (median 44.6 Gy) was 42.4 months. Primary histology was glioblastoma (n=74), astrocytoma (n=35), and oligodendroglioma (n=34). Median PFS and OS were 8.1 and 11.2 months, respectively. On UVA, improved OS was associated with oligodendroglioma (HR 0.37) and astrocytoma histology (HR 0.53) compared to glioblastoma, TI &gt;60 months (HR 0.2), CNS WHO Grade 2 compared to 4 (HR 0.42), and ECOG performance status 0 (HR 0.47). On MVA, improved OS remained associated only with oligodendroglioma (HR 0.42) and TI &gt;60 months (HR 0.49). Acute and Late Grade 3 toxicity occurred in 7% and 4% of patients. Acute Grade 3 toxicity was associated with poor performance status (HR 16.15). CONCLUSIONS In the largest series of glioma PT-ReRT reported to date, retreatment appears well tolerated with variable outcomes based on clinical prognostic factors. Toxicity rates were acceptable compared to historical photon-based literature despite a high median prescription dose.

AbstractPurposeTo assess the practicality of employing a commercial knowledge‐based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone‐beam computed tomography (iCBCT) datasets.Methods and materialsIntact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose‐volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan‐generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow.ResultsModel testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed).ConclusionThis study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

PURPOSE Multidisciplinary molecular tumor boards (MTBs) decode complex genomic data into clinical recommendations. Although MTBs are well-established in the oncology practice in developed countries, this strategy needs to be better explored in developing countries. Herein, we describe the possible benefits and limitations of the first MTB established in Colombia. METHODS Demographic, clinical, and genomic information was collected between August 2020 and November 2021. By mid-2020, an MTB strategy was created to discuss clinical cases with one or more genomic alterations identified by next-generation sequencing using an open-access virtual platform. We characterized the patient population as benefiting from the recommended treatment option. We assessed the benefits and access to available targeted therapies that have the potential to change clinical management by making recommendations to treating oncologists on the basis of genomic profiling. However, we did not assess the treatment oncologists' compliance with MTB recommendations because they were not intended to replace clinical judgment/standard of care. RESULTS A total of 146 patients were included in the discussions of the MTB. The median age was 59 years, and 59.6% were women. Genomic results prompting a change in therapeutic decisions were obtained in 53.1% of patients (95% CI, 44.9 to 61.3). The most prevalent malignancy was non–small-cell lung cancer (51%). Other malignancies represented 60%, 50%, and 30% of patients with soft-tissue sarcomas, brain tumors, and breast cancer, respectively. CONCLUSION Using an open-access virtual platform, MTBs were feasible in low- and middle-income countries on the basis of the capability to provide the benefits and access to available targeted therapies that are not standard of care. Furthermore, MTB recommendations were made available to the treating oncologist in different locations across Colombia, providing the option to modify clinical management in most of these patients.