Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-based Auto Segmentation in Pencil Beam Scanning Proton Therapy: Prostate oAPT in PBSPT.

PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy (PBSPT). Artificial intelligence (AI)-based auto-segmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification CT (vCT) can further improve the re-plan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with prostate patients. METHODS AND MATERIALS: AI-based auto-segmentation tool AccuContourTM (Manteia, Xiamen, China) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for re-optimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected OARs. Eleven prostate cancer patients were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis. RESULTS: The verification plan showed significant degradation of the CTV coverage and rectum and bladder sparing due to the interfractional anatomical changes. Re-optimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in re-plans. 3D Gamma analyses in PSQA confirmed the accuracy of the re-plan doses before delivery (Gamma passing rate = 99.57 ± 0.46%), and after delivery (98.59 ± 1.29%). The robustness of the re-plans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time. CONCLUSION: The AI-facilitated oAPT workflow was demonstrated to be both efficient and effective by generating a re-plan that significantly improved the plan quality in prostate cancer treated with PBSPT.

Benchmarking a Foundation Large Language Model on its Ability to Relabel Structure Names in Accordance With the American Association of Physicists in Medicine Task Group-263 Report.

PURPOSE: To introduce the concept of using large language models (LLMs) to relabel structure names in accordance with the American Association of Physicists in Medicine Task Group-263 standard and to establish a benchmark for future studies to reference. METHODS AND MATERIALS: Generative Pretrained Transformer (GPT)-4 was implemented within a Digital Imaging and Communications in Medicine server. Upon receiving a structure-set Digital Imaging and Communications in Medicine file, the server prompts GPT-4 to relabel the structure names according to the American Association of Physicists in Medicine Task Group-263 report. The results were evaluated for 3 disease sites: prostate, head and neck, and thorax. For each disease site, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50), and 50 patients were randomly selected for evaluation. Structure names considered were those that were most likely to be relevant for studies using structure contours for many patients. RESULTS: The per-patient accuracy was 97.2%, 98.3%, and 97.1% for prostate, head and neck, and thorax disease sites, respectively. On a per-structure basis, the clinical target volume was relabeled correctly in 100%, 95.3%, and 92.9% of cases, respectively. CONCLUSIONS: Given the accuracy of GPT-4 in relabeling structure names as presented in this work, LLMs are poised to become an important method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.

Establishing the minimal clinically important difference of the Brief Fatigue Inventory for brain or CNS cancer patients undergoing radiotherapy.

Background: Minimal clinically important differences (MCIDs) quantify the clinical relevance of quality of life results at the individual patient and group level. The aim of this study was to estimate the MCID for the Brief Fatigue Inventory (BFI) and the Worst and Usual Fatigue items in patients with brain or CNS cancer undergoing curative radiotherapy. Methods: Data from a multi-site prospective registry was used. The MCID was calculated using distribution-based and anchor-based approaches. For the anchor-based approach, the fatigue item from the PROMIS-10 served as the anchor to determine if a patient improved, deteriorated, or had no change from baseline to end of treatment (EOT). We compared the unadjusted means on the BFI for the 3 groups to calculate the MCID. For the distribution-based approaches, we calculated the MCID as 0.5 SD of the scores and as 1.96 times the standard error of measurement. Results: Three-hundred and fifty nine patients with brain or CNS tumors undergoing curative radiotherapy filled out the 9-item BFI at baseline and EOT. The MCID for the BFI was 1.33 (ranging from 0.99 to 1.70 across the approaches), 1.51 (ranging from 1.16 to 2.02) and 1.76 (ranging from 1.38 to 2.14) for the usual and worst fatigue items, respectively. Conclusions: This study provides the MCID ranges for the BFI and Worst and Usual fatigue items, which will allow clinically meaningful conclusions to be drawn from BFI scores. These results can be used to select optimal treatments for patients with brain or CNS cancer or to interpret BFI scores from clinical trials.

Radiotherapy toxicity prediction using knowledge-constrained generalized linear model.

Radiation therapy (RT) is a frontline approach to treating cancer. While the target of radiation dose delivery is the tumor, there is an inevitable spill of dose to nearby normal organs causing complications. This phenomenon is known as radiotherapy toxicity. To predict the outcome of the toxicity, statistical models can be built based on dosimetric variables received by the normal organ at risk (OAR), known as Normal Tissue Complication Probability (NTCP) models. To tackle the challenge of the high dimensionality of dosimetric variables and limited clinical sample sizes, statistical models with variable selection techniques are viable choices. However, existing variable selection techniques are data-driven and do not integrate medical domain knowledge into the model formulation. We propose a knowledge-constrained generalized linear model (KC-GLM). KC-GLM includes a new mathematical formulation to translate three pieces of domain knowledge into non-negativity, monotonicity, and adjacent similarity constraints on the model coefficients. We further propose an equivalent transformation of the KC-GLM formulation, which makes it possible to solve the model coefficients using existing optimization solvers. Furthermore, we compare KC-GLM and several well-known variable selection techniques via a simulation study and on two real datasets of prostate cancer and lung cancer, respectively. These experiments show that KC-GLM selects variables with better interpretability, avoids producing counter-intuitive and misleading results, and has better prediction accuracy.

Pre-operative vs. post-operative stereotactic radiosurgery for operative metastatic brain tumors: study protocol for a phase III clinical trial.

BACKGROUND AND OBJECTIVES: Almost one third of cancer patients in the United States will develop brain metastases on an annual basis. Surgical resection is indicated in the setting of brain metastases for reasons, such as maximizing local control in select patients, decompression of mass effect, and/or tissue diagnosis. The current standard of care following resection of a brain metastasis has shifted from whole brain radiation therapy to post-operative stereotactic radiosurgery (SRS). However, there is a significant rate of local recurrence within one year of postoperative SRS. Emerging retrospective and prospective data suggest pre-operative SRS is a safe and potentially effective treatment paradigm for surgical brain metastases. This trial intends to determine, for patients with an indication for resection of a brain metastasis, whether there is an increase in the time to a composite endpoint of adverse outcomes; including the first occurrence of either: local recurrence, leptomeningeal disease, or symptomatic radiation brain necrosis - in patients who receive pre-operative SRS as compared to patients who receive post-operative SRS. METHODS: This randomized phase III clinical trial compares pre-operative with post-operative SRS for brain metastases. A dynamic random allocation procedure will allocate an equal number of patients to each arm: pre-operative SRS followed by surgery or surgery followed by post-operative SRS. EXPECTED OUTCOMES: If pre-operative SRS improves outcomes relative to post-operative SRS, this will establish pre-operative SRS as superior. If post-operative SRS proves superior to pre-operative SRS, it will remain a standard of care and halt the increasing utilization of pre-operative SRS. If there is no difference in pre- versus post-operative SRS, then pre-operative SRS may still be preferred, given patient convenience and the potential for a condensed timeline. DISCUSSION: Emerging retrospective and prospective data have demonstrated some benefits of pre-op SRS vs. post-op SRS. This study will show whether there is an increase in the time to the composite endpoint. Additionally, the study will compare overall survival; patient-reported outcomes; morbidity; completion of planned therapies; time to systemic therapy; time to regional progression; time to CNS progression; time to subsequent treatment; rate of radiation necrosis; rate of local recurrence; and rate of leptomeningeal disease. TRIAL REGISTRATION NUMBER: NCT03750227 (Registration date: 21/11/2018).

Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology.

BACKGROUND: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. PURPOSE: Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next-generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. METHODS: Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's 'segment anything' mode, and automatic segmentation using SAM's 'box prompt' mode that implements manual interaction via live prompts during segmentation. RESULTS: Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea. CONCLUSIONS: Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box-prompt method enables the users to augment auto-segmentation interactively and dynamically, leading to patient-specific auto-segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto-segmentation model in radiotherapy.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

BACKGROUND: Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS: PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s. CONCLUSIONS: An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy.

Purpose. To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS).Methods and materials. A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3 cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4 cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45 mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 c.c. with range of 0.4-43.3 c.c.). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC.Results. In the water phantoms, 3D gamma passing rate (2%/2 mm/10%) between VPMC and MCsquare was 99.71 ± 0.23%. In the patient geometries, 3D gamma passing rates (3%/2 mm/10%) between VPMC/MCsquare and RayStation MC were 97.79 ± 2.21%/97.78 ± 1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45 ± 114.08 s (MCsquare) to 8.20 ± 6.42 s (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 s and the subsequent on-the-fly 'trial-and-error' optimization procedure took only 71.4 s on average for the selected three patients.Conclusion. VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy.

Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods and Materials: A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 cc with range of 0.4 - 43.3 cc). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC. Results: In the water phantoms, 3D gamma passing rate (2%/2mm/10%) between VPMC and MCsquare was 99.71±0.23%. In the patient geometries, 3D gamma passing rates (3%/2mm/10%) between VPMC/MCsquare and RayStation MC were 97.79±2.21%/97.78±1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45±114.08 seconds (MCsquare) to 8.20±6.42 seconds (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 seconds and the subsequent on-the-fly "trial-and-error" optimization procedure took only 71.4 seconds on average for the selected three patients. Conclusion: VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

PURPOSE: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s.

Clinical Outcomes of Stereotactic Radiosurgery-Related Radiation Necrosis in Patients with Intracranial Metastasis from Melanoma.

Background: Radiation necrosis (RN) is a clinically relevant complication of stereotactic radiosurgery (SRS) for intracranial metastasis (ICM) treatments. Radiation necrosis development is variable following SRS. It remains unclear if risk factors for and clinical outcomes following RN may be different for melanoma patients. We reviewed patients with ICM from metastatic melanoma to understand the potential impact of RN in this patient population. Methods: Patients who received SRS for ICM from melanoma at Mayo Clinic Arizona between 2013 and 2018 were retrospectively reviewed. Data collected included demographics, tumor characteristics, radiation parameters, prior surgical and systemic treatments, and patient outcomes. Radiation necrosis was diagnosed by clinical evaluation including brain magnetic resonance imaging (MRI) and, in some cases, tissue evaluation. Results: Radiation necrosis was diagnosed in 7 (27%) of 26 patients at 1.6 to 38 months following initial SRS. Almost 92% of all patients received systemic therapy and 35% had surgical resection prior to SRS. Patients with RN trended toward having larger ICM and a prior history of surgical resection, although statistical significance was not reached. Among patients with resection, those who developed RN had a longer period between surgery and SRS start (mean 44 vs 33 days). Clinical improvement following treatment for RN was noted in 2 (29%) patients. Conclusions: Radiation necrosis is relatively common following SRS for treatment of ICM from metastatic melanoma and clinical outcomes are poor. Further studies aimed at mitigating RN development and identifying novel approaches for treatment are warranted.

Technical note: Comprehensive evaluations of gantry and couch rotation isocentricities for implementing proton stereotactic radiosurgery.

BACKGROUND: Mechanical accuracy should be verified before implementing a proton stereotactic radiosurgery (SRS) program. Linear accelerator (Linac)-based SRS systems often use electronic portal imaging devices (EPIDs) to verify beam isocentricity. Because proton therapy systems do not have EPID, beam isocentricity tests of proton SRS may still rely on films, which are not efficient. PURPOSE: To validate that our proton SRS system meets mechanical precision requirements and to present an efficient method to evaluate the couch and gantry's rotational isocentricity for our proton SRS system. METHODS: A dedicated applicator to hold brass aperture for proton SRS system was designed. The mechanical precision of the system was tested using a metal ball and film for 11 combinations of gantry and couch angles. A more efficient quality assurance (QA) procedure was developed, which used a scintillator device to replace the film. The couch rotational isocentricity tests were performed using orthogonal kV x-rays with the couch rotated isocentrically to five positions (0°, 315°, 270°, 225°, and 180°). At each couch position, the distance between the metal ball in kV images and the imaging isocenter was measured. The gantry isocentricity tests were performed using a cone-shaped scintillator and proton beams at five gantry angles (0°, 45°, 90°, 135°, and 180°), and the isocenter position and the distance of each beam path to the isocenter were obtained. Daily QA procedure was performed for 1 month to test the robustness and reproducibility of the procedure. RESULTS: The gantry and couch rotational isocentricity exhibited sub-mm precision, with most measurements within ±0.5 mm. The 1-month QA results showed that the procedure was robust and highly reproducible to within ±0.2 mm. The gantry isocentricity test using the cone-shaped scintillator was accurate and sensitive to variations of ±0.2 mm. The QA procedure was efficient enough to be completed within 30 min. The 1-month isocentricity position variations were within 0.5 mm, which demonstrating that the overall proton SRS system was stable and precise. CONCLUSION: The proton SRS Winston-Lutz QA procedure using a cone-shaped scintillator was efficient and robust. We were able to verify radiation delivery could be performed with sub-mm mechanical precision.

Clinical outcomes and toxicities of 100 patients treated with proton therapy for chordoma on the proton collaborative group prospective registry.

BACKGROUND: We present efficacy and toxicity outcomes among patients with chordoma treated on the Proton Collaborative Group prospective registry. METHODS: Consecutive chordoma patients treated between 2010-2018 were evaluated. One hundred fifty patients were identified, 100 had adequate follow-up information. Locations included base of skull (61%), spine (23%), and sacrum (16%). Patients had a performance status of ECOG 0-1 (82%) and median age of 58 years. Eighty-five percent of patients underwent surgical resection. The median proton RT dose was 74 Gy (RBE) (range 21-86 Gy (RBE)) using passive scatter proton RT (PS-PBT) (13%), uniform scanning proton RT (US-PBT) (54%) and pencil beam scanning proton RT (PBS-PBT) (33%). Rates of local control (LC), progression-free survival (PFS), overall survival (OS) and acute and late toxicities were assessed. RESULTS: 2/3-year LC, PFS, and OS rates are 97%/94%, 89%/74%, and 89%/83%, respectively. LC did not differ based on surgical resection (p = 0.61), though this is likely limited by most patients having undergone a prior resection. Eight patients experienced acute grade 3 toxicities, most commonly pain (n = 3), radiation dermatitis (n = 2), fatigue (n = 1), insomnia (n = 1) and dizziness (n = 1). No grade ≥ 4 acute toxicities were reported. No grade ≥ 3 late toxicities were reported, and most common grade 2 toxicities were fatigue (n = 5), headache (n = 2), CNS necrosis (n = 1), and pain (n = 1). CONCLUSIONS: In our series, PBT achieved excellent safety and efficacy outcomes with very low rates of treatment failure. CNS necrosis is exceedingly low (<1%) despite the high doses of PBT delivered. Further maturation of data and larger patient numbers are necessary to optimize therapy in chordoma.

Outcomes of Proton Beam Therapy Compared With Intensity-Modulated Radiation Therapy for Uterine Cancer.

Purpose: To compare Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) in patients with endometrial cancer receiving adjuvant pelvic radiation therapy with proton beam therapy (PT) versus intensity-modulated radiation therapy (IMRT). Materials and Methods: Patients with uterine cancer treated with curative intent who received either adjuvant PT or IMRT between 2014 and 2020 were identified. Patients were enrolled into a prospective registry using a gynecologic-specific subset of PRO-CTCAE designed to assess symptom impact on daily living. Questions included gastrointestinal (GI) symptoms of diarrhea, flatulence, bowel incontinence, and constipation in addition to other pertinent gynecologic, urinary, and other general symptoms. Symptom-based questions were on a 0- to 4-point scale, with grade 3+ symptoms occurring frequently or almost always. Patient-reported toxicity was analyzed at baseline, end of treatment (EOT), and at 3, 6, 9, and 12 months after treatment. Unequal variance t tests were used to determine if treatment type was a significant factor in baseline-adjusted PRO-CTCAE. Results: Sixty-seven patients met inclusion criteria. Twenty-two received PT and 45 patients received IMRT. Brachytherapy boost was delivered in 73% of patients. Median external beam dose was 45 Gy for both PT and IMRT (range, 45-58.8 Gy). When comparing PRO-CTCAE, PT was associated with less diarrhea at EOT (P = .01) and at 12 months (P = .24) than IMRT. Loss of bowel control at 12 months was more common in patients receiving IMRT (P = .15). Any patient reporting grade 3+ GI toxicity was noted more frequently with IMRT (31% versus 9%, P = .09). Discussion: Adjuvant PT is a promising treatment for patients with uterine cancer and may reduce patient-reported GI toxicity as compared with IMRT.

The Complementary Role of 68Ga-DOTATATE PET/CT in Diagnosis of Recurrent Meningioma.

Introduction: Contrast-enhanced brain MRI is the choice of imaging modality in diagnosis and posttreatment evaluation, its role is limited in distinguishing recurrent lesion from postoperative change. 68Ga-DOTATATE is a somatostatin analog PET tracer which has high affinity to meningioma expressing somatostatin receptor. Methods and subjects: In this case series review, we described 8 patients with brain MRI suspected of recurrent meningioma who underwent focused 68Ga-DOTATATE PET/CT scan for radiation treatment planning. Results: The combined brain MRI and PET/CT allowed improved conspicuity of the lesions and aided radiation treatment planning. The time from the initial surgery to PET/CT scans varied widely ranging from 1 year to 12 years. Three patients had PET/CT shortly after the initial surgery (1-3 years) and underwent targeted radiation therapy. Subsequent imaging showed no evidence of recurrence. Four patients had prolonged time between the PET/CT and the initial surgery (7-12 years) which showed extensive tumor burden. All four patients expired shortly after the last PET/CT scan. Conclusion: 68Ga-DOTATATE PET shows promising complementary role in detection and treatment planning of recurrent meningioma.

Long-term outcomes of prostate intensity-modulated radiation therapy incorporating a simultaneous intra-prostatic MRI-directed boost.

Purpose/objectives: This retrospective study demonstrates the long-term outcomes of treating prostate cancer using intensity modulated (IMRT) with incorporation of MRI-directed boost. Materials/methods: From February 2009 to February 2013, 78 men received image-guided IMRT delivering 77.4 Gy in 44 fractions with simultaneously integrated boost to 81-83 Gy to an MRI-identified lesion. Patients with intermediate-risk or high-risk prostate cancer were recommended to receive 6 and 24-36 months of adjuvant hormonal therapy, respectively. Results: Median follow-up was 113 months (11-147). There were 18 low-risk, 43 intermediate-risk, and 17 high-risk patients per NCCN risk stratification included in this study. Adjuvant hormonal therapy was utilized in 32 patients (41%). The 10-year biochemical control rate for all patients was 77%. The 10-year biochemical control rates for low-risk, intermediate-risk, and high-risk diseases were 94%, 81%, and 88%, respectively (p = 0.35). The 10-year rates of local control, distant control, and survival were 99%, 88%, and 66%, respectively. Of 25 patients who died, only four (5%) died of prostate cancer. On univariate analysis, T-category and pretreatment PSA level were associated with distant failure rate (p = 0.02). There was no grade =3 genitourinary and gastrointestinal toxicities that persisted at the last follow-up. Conclusions: This study demonstrated the long-term efficacy of using MRI to define an intra-prostatic lesion for SIB to 81-83Gy while treating the entire prostate gland to 77.4 Gy with IMRT. Our study confirms that modern MRI can be used to locally intensify dose to prostate tumors providing high long-term disease control while maintaining favorable long-term toxicity.

High-Dose Frameless Stereotactic Radiosurgery for Trigeminal Neuralgia: A Single-Institution Experience and Systematic Review.

OBJECTIVE: Stereotactic radiosurgery is an effective treatment option for trigeminal neuralgia (TN), with frameless stereotactic radiosurgery (fSRS) allowing for a less invasive experience. A single-institutional series and systematic review of the literature were performed for cases of TN treated with fSRS. METHODS: Patients at our institution with TN that were treated with fSRS from the years 2012-2021 were included. Similarly, multiple databases were searched for studies regarding TN treated with fSRS where patient-level data was included from 2004-2020. Pain levels, via the Barrow Neurological Institute (BNI) scale, before and after treatment were analyzed. Pooled analysis was performed to compare treatment outcomes between studies using CyberKnife and LINAC modalities. RESULTS: Twenty-three patients at our institution were treated with LINAC fSRS (median treatment dose: 85 Gy). Most patients had TN refractory to previous procedural treatments. Eight (35%) patients had an excellent posttreatment response (BNI I-II), while 11 (48%) patients had a good result (BNI IIIa/b). Eight patients had recurrence of pain. A total of 30 articles were included in the systematic review, encompassing 1705 patients. At last follow-up, 63.1% (774/1227) of patients endorsed an excellent response, while 16.1% (197/1227) had a good response, and 22.5% (215/957) of patients had recurrence. Pain response, facial numbness rates, and pain recurrence rates were not significantly different between CyberKnife and LINAC modalities. CONCLUSIONS: Frameless SRS for TN appears to be an efficacious noninvasive option for patients with substantial comorbidities, who have failed other treatment methods, although it can be limited by higher recurrence rates.

Exploratory study of seed spots analysis to characterize dose and linear-energy-transfer effect in adverse event initialization of pencil-beam-scanning proton therapy.

BACKGROUND: Both dose and linear energy transfer (LET) could play a substantial role in adverse event (AE) initialization of cancer patients treated with pencil-beam-scanning (PBS) proton therapy. However, not all the voxels within the AE regions are directly induced from the dose and LET effect. It is important to study the synergistic effect of dose and LET in AE initialization by only including a subset of voxels that are dosimetrically important. PURPOSE: To perform exploratory investigation of the dose and LET effects upon AE initialization in PBS using seed spots analysis. METHODS: A total of 113 head-and-neck (H&N) cancer patients receiving curative PBS were included. Among them, 20 patients experienced unanticipated CTCAEv4.0 grade ≥3 AEs (AE group) and 93 patients did not (control group). Within the AE group, 13 AE patients were included in the seed spot analysis to derive the descriptive features of AE initialization and the remaining 7 mandible osteoradionecrosis patients and 93 control patients were used to derive the feature-based volume constraint of mandible osteoradionecrosis. The AE regions were contoured and the corresponding dose-LET volume histograms of AE regions were generated for all patients in the AE group. We selected high LET voxels (the highest 5% of each dose bin) with a range of moderate-to-high dose (≥∼40-Gy relative biological effectiveness) as critical voxels. Critical voxels that were contiguous with each other were grouped into clusters. Each cluster was considered a potential independent seed spot for AE initialization. Seed spots were displayed in a 2D dose-LET plane based on their mean dose and LET to derive the descriptive features of AE initialization. A volume constraint of mandible osteoradionecrosis was then established based on the extracted features using a receiver operating characteristic curve. RESULTS: The product of dose and LET (xBD) was found to be a descriptive feature of seed spots leading to AE initialization in this preliminary study. The derived xBD volume constraint for mandible osteoradionecrosis showed good performance with an area under curve of 0.87 (sensitivity of 0.714 and specificity of 0.807 in the leave-one-out cross-validation) for the very limited patient data included in this study. CONCLUSION: Our exploratory study showed that both dose and LET were observed to be important in AE initializations. The derived xBD volume constraint could predict mandible osteoradionecrosis reasonably well in the very limited H&N cancer patient data treated with PBS included in this study.

A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.

INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD: We included GBM patients with a new or increasingly enhancing lesion within the original radiation field. We labeled those who subsequently were stable or improved on imaging and clinically as PsP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PsP, or TP based on the histological diagnosis. We coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model and using five-fold cross-validation to predict TP vs PsP. RESULT: We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (range 70-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560 (range 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05% (SD 9.11) mean specificity. CONCLUSION: We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

Radiation Therapy for IDH-Mutant Grade 2 and Grade 3 Diffuse Glioma: An ASTRO Clinical Practice Guideline.

PURPOSE: This guideline provides evidence-based recommendations for adults with isocitrate dehydrogenase (IDH)-mutant grade 2 and grade 3 diffuse glioma, as classified in the 2021 World Health Organization (WHO) Classification of Tumours. It includes indications for radiation therapy (RT), advanced RT techniques, and clinical management of adverse effects. METHODS: The American Society for Radiation Oncology convened a multidisciplinary task force to address 4 key questions focused on the RT management of patients with IDH-mutant grade 2 and grade 3 diffuse glioma. Recommendations were based on a systematic literature review and created using a predefined consensus-building methodology and system for grading evidence quality and recommendation strength. RESULTS: A strong recommendation for close surveillance alone was made for patients with oligodendroglioma, IDH-mutant, 1p/19q codeleted, WHO grade 2 after gross total resection without high-risk features. For oligodendroglioma, WHO grade 2 with any high-risk features, adjuvant RT was conditionally recommended. However, adjuvant RT was strongly recommended for oligodendroglioma, WHO grade 3. A conditional recommendation for close surveillance alone was made for astrocytoma, IDH-mutant, WHO grade 2 after gross total resection without high-risk features. Adjuvant RT was conditionally recommended for astrocytoma, WHO grade 2, with any high-risk features and strongly recommended for astrocytoma, WHO grade 3. Dose recommendations varied based on histology and grade. Given known adverse long-term effects of RT, consideration for advanced techniques such as intensity modulated radiation therapy/volumetric modulated arc therapy or proton therapy were given as strong and conditional recommendations, respectively. Finally, based on expert opinion, the guideline recommends assessment, surveillance, and management for toxicity management. CONCLUSIONS: Based on published data, the American Society for Radiation Oncology task force has proposed recommendations to inform the management of adults with IDH-mutant grade 2 and grade 3 diffuse glioma as defined by WHO 2021 classification, based on the highest quality published data, and best translated by our task force of subject matter experts.