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).

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.

Focused versus conventional radiotherapy in spinal oncology: is there any difference in fusion rates and pseudoarthrosis?

INTRODUCTION: Radiotherapy is considered standard of care for adjuvant peri-operative treatment of many spinal tumors, including those with instrumented fusion. Unfortunately, radiation treatment has been linked to increased risk of pseudoarthrosis. Newer focused radiotherapy strategies with enhanced conformality could offer improved fusion rates for these patients, but this has not been confirmed. METHODS: We performed a retrospective analysis of patients at three tertiary care academic institutions with primary and secondary spinal malignancies that underwent resection, instrumented fusion, and peri-operative radiotherapy. Two board certified neuro-radiologists used the Lenke fusion score to grade fusion status at 6 and 12-months after surgery. Secondary outcomes included clinical pseudoarthrosis, wound complications, the effect of radiation timing and radiobiological dose delivered, the use of photons versus protons, tumor type, tumor location, and use of autograft on fusion outcomes. RESULTS: After review of 1252 spinal tumor patients, there were 60 patients with at least 6 months follow-up that were included in our analyses. Twenty-five of these patients received focused radiotherapy, 20 patients received conventional radiotherapy, and 15 patients were treated with protons. There was no significant difference between the groups for covariates such as smoking status, obesity, diabetes, intraoperative use of autograft, and use of peri-operative chemotherapy. There was a significantly higher rate of fusion for patients treated with focused radiotherapy compared to those treated with conventional radiotherapy at 6-months (64.0% versus 30.0%, Odds ratio: 4.15, p = 0.036) and 12-months (80.0% versus 42.1%, OR: 5.50, p = 0.022). There was a significantly higher rate of clinical pseudoarthrosis in the conventional radiotherapy cohort compared to patients in the focused radiotherapy cohort (19.1% versus 0%, p = 0.037). There was no difference in fusion outcomes for any of the secondary outcomes except for use of autograft. The use of intra-operative autograft was associated with an improved fusion at 12-months (66.7% versus 37.5%, OR: 3.33, p = 0.043). CONCLUSION: Focused radiotherapy may be associated with an improved rate of fusion and clinical pseudoarthrosis when compared to conventional radiation delivery strategies in patients with spinal tumors. Use of autograft at the time of surgery may be associated with improved 12-month fusion rates. Further large-scale prospective and randomized controlled studies are needed to better stratify the effects of radiation delivery modality in these patients.

Carbon fiber-reinforced PEEK instrumentation in the spinal oncology population: a retrospective series demonstrating technique, feasibility, and clinical outcomes.

OBJECTIVE: The authors aimed to demonstrate the feasibility and advantages of carbon fiber-reinforced PEEK (CFRP) composite implants in patients with both primary and secondary osseous spinal tumors. METHODS: Twenty-eight spinal tumor patients who underwent fixation with CFRP hardware were retrospectively identified in a Spine Tumor Quality Database at a single institution. Demographic, procedural, and follow-up data were retrospectively collected. RESULTS: The study population included 14 females and 14 males with a mean age of 60 years (range 30-86 years). Five patients had primary bone tumors, and the remaining patients had metastatic tumors. Breast cancer was the most common metastatic tumor. The most common presenting symptom was axial spine pain (25 patients, 89%), and the most common Spine Instability Neoplastic Score was 7 (range 6-14). Two patients in this series had anterior cervical procedures. The remaining patients underwent posterior thoracolumbar fixation. The average fusion length included 4.6 vertebral segments (range 3-8). The mean clinical follow-up time with surgical or oncology teams was 6.5 months (range 1-23 months), and the mean interval for last follow-up imaging (CT or MRI) was 6.5 months (range 1-22 months). Eighteen patients received postoperative radiation at the authors' institution (16 with photon therapy, 2 with proton therapy). Eleven of the patients (39%) in this series died. At the last clinical follow-up, 26 patients (93%) had stable or improved neurological function compared with their preoperative status. At the last imaging follow-up, local disease control was observed in 25 patients (89%). Two patients required reoperation in the immediate postoperative period, one for surgical site infection and the other for compressive epidural hematoma. One patient was noted to have lucencies around the most cephalad screws 3 months after surgery. No hardware fracture or malfunction occurred intraoperatively. No patients required delayed surgery for hardware loosening, fracture, or other failure. Early tumor recurrence was detected in 3 patients. Early detection was attributed to the imaging characteristics of the CFRP hardware. CONCLUSIONS: CFRP spinal implants appear to be safe and comparable to conventional titanium implants in terms of functionality. The imaging characteristics of CFRP hardware facilitate radiation planning and assessment of surveillance imaging. CFRP hardware may enhance safety and efficacy, particularly with particle therapy dosimetry. Larger patient populations with longer-term follow-up are needed to confirm the various valuable aspects of CFRP spinal implants.

Management and outcomes of primary vaginal Cancer.

OBJECTIVE: To analyze clinical characteristics and survival of patients with primary vaginal cancer. METHODS: Retrospective analysis of patients with primary squamous, adenocarcinoma and adenosquamous cell carcinoma of the vagina identified from the Mayo Clinic Cancer Registry between 1998 and 2018. RESULTS: A total of 124 patients were identified: stage I, 39 patients; stage II, 44, stage III, 20 and stage IV, 21. Patients with stage III and IV were older as compared to stage I and II. (mean ages 61 vs 67) (p = 0.024). Squamous cell carcinoma made up 71% of tumors. History of other malignancy was present in 24% patients. Median follow-up time was 60 months (range 1-240). Five-year PFS in stage I, II, III and IV was 58.7%, 59.4%, 67.3% and 31.8%, respectively (p = 0.039). Five-year DSS was 84.3%, 73.7%, 78.7% and 26.5% respectively (p < 0.001). Advanced stage, tumor size >4 cm, entire vaginal involvement, and lymph node (LN) metastasis were poor prognosticators in univariate analysis. Primary surgery in stage I/II patients had similar survival outcomes as compared to primary radiation, but post-operative RT rate was 55%. Brachytherapy alone was associated with a high local recurrence (80%) in stage I/II patients. The addition of brachytherapy had improved 5-year PFS and DSS than EBRT alone in patients with stage III/IVA. (p < 0.001). CONCLUSION: Surgery or radiation is effective treatment for vaginal cancer stage I and II. The addition of brachytherapy to external pelvic radiation increases survival in stages III-IV.

Six-year outcome of a prospective study evaluating tumor bed boost with intra-operative electron irradiation followed by whole-breast irradiation for early-stage breast cancer.

A prospective study was performed for patients with early-stage breast cancer in which a single fraction of intraoperative electron irradiation (IOERT) was given to the tumor bed, followed by conventional fractionated whole-breast irradiation (WBRT). Patients with T1/T2N0 breast cancer underwent lumpectomy and sentinel lymph node biopsy. A tumor bed boost of 10 Gy of IOERT using 6-12 MeV electrons was administered by a dedicated mobile linear accelerator in the operating room. After adequate wound healing, 48 Gy WBRT was given to the whole breast in 24 fractions. Fifty-two patients were enrolled between February 2003 and January 2005. At a median follow-up of 79 months, there were two local relapses. The 6-year actuarial overall survival and distant control rates were 89% and 96%, respectively. At last follow-up, cosmesis was graded as excellent or good in 45 (87%), fair in five (10%), and poor in two patients (4%), respectively. Difficulty in wound healing occurred in two patients who had additional surgery later. One patient developed significant fibrosis after aspiration of a symptomatic seroma. The result of this pilot study shows the feasibility of using IOERT as the tumor bed boost in lieu of 6-8 days of standard electron beam treatment with good local control and cosmetic results. However, late surgical intervention of the lumpectomy bed may result in more pronounced tissue fibrosis and wound healing difficulty.

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.

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.

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.

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.

Outcome and toxicity for patients treated with intensity modulated radiation therapy for localized prostate cancer.

PURPOSE: We evaluate long-term disease control and chronic toxicities observed in patients treated with intensity modulated radiation therapy for clinically localized prostate cancer. MATERIALS AND METHODS: A total of 302 patients with localized prostate cancer treated with image guided intensity modulated radiation therapy between July 2000 and May 2005 were retrospectively analyzed. Risk groups (low, intermediate and high) were designated based on National Comprehensive Cancer Network guidelines. Biochemical control was based on the American Society for Therapeutic Radiology and Oncology (Phoenix) consensus definition. Chronic toxicity was measured at peak symptoms and at last visit. Toxicity was scored based on Common Terminology Criteria for Adverse Events v4. RESULTS: The median radiation dose delivered was 75.6 Gy (range 70.2 to 77.4) and 35.4% of patients received androgen deprivation therapy. Patients were followed until death or from 6 to 138 months (median 91) for those alive at last evaluation. Local and distant recurrence rates were 5% and 8.6%, respectively. At 9 years biochemical control rates were 77.4% for low risk, 69.6% for intermediate risk and 53.3% for high risk cases (log rank p = 0.05). On multivariate analysis T stage and prostate specific antigen group were prognostic for biochemical control. At last followup only 0% and 0.7% of patients had persistent grade 3 or greater gastrointestinal and genitourinary toxicity, respectively. High risk group was associated with higher distant metastasis rate (p = 0.02) and death from prostate cancer (p = 0.0012). CONCLUSIONS: This study represents one of the longest experiences with intensity modulated radiation therapy for prostate cancer. With a median followup of 91 months, intensity modulated radiation therapy resulted in durable biochemical control rates with low chronic toxicity.

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.

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.

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.

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.

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.

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.

Comprehensive Commissioning and Clinical Implementation of GammaTiles STaRT for Intracranial Brain Cancer.

Purpose: To validate the dose calculation accuracy and dose distribution of GammaTiles for brain tumors, and to suggest a surgically targeted radiation therapy (STaRT) workflow for planning, delivery, radiation safety documentation, and posttreatment validation. Methods and Materials: Novel surgically targeted radiation therapy, GammaTiles, uses Cs-131 radiation isotopes embedded in collagen-based tiles that can be resorbed after surgery. GammaTile target delineation and dose calculation were performed on MIM Symphony software. Point-based and complex seed distribution calculations in MIM Symphony were verified with hand calculations and BrachyVision calculations. Vendor-provided 2-dimensional dose distribution calculation accuracy was validated using gafchromic EBT3 film measurements at various depths. A workflow was established for safe and effective GammaTile implants. Results: Good agreement was observed between different calculations. Calculation accuracy of less than 0.5% was achieved for all points except one, which had rounding issues for very low doses and resulted in just below 5% difference. Differences in anisotropy and geometry positioning were noticed in the delineation of Cs-131 IsoRay seeds in the compared systems, resulting in minor discrepancies in the calculated dosimetry distributions. Film measurements showed profiles with relatively good agreement of 0% to 5% in nongradient regions with higher differences between 5% to 10% in the sharp dose fall-off regions. Conclusions: A comprehensive evaluation of GammaTile geometry, dose distribution, and clinical workflow was conducted. Safe intro-operative implantation of GammaTiles requires extensive preplanning and interdisciplinary collaboration. A STaRT workflow was outlined to provide a guideline for an accurate treatment planning and safe implant process at other institutions.