Advances in Radiation Oncology最新文献

{"title":"Comparative Analysis of Atlas and Neural Network Autosegmentation Methods for Pediatric Craniospinal Irradiation With the Development of a Knowledge-Based Quality Assurance Tool","authors":"Ozgur Ates ,&nbsp;James Man Git Tsui ,&nbsp;Zachary Wooten ,&nbsp;Sydney Hutcheson ,&nbsp;Rico Zhang ,&nbsp;Jared Becksfort ,&nbsp;Thomas E. Merchant ,&nbsp;Chia-ho Hua","doi":"10.1016/j.adro.2025.101847","DOIUrl":"10.1016/j.adro.2025.101847","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to evaluate the performance of Atlas and neural network autosegmentation methods and develop a knowledge-based quality assurance (QA) tool for pediatric craniospinal irradiation (CSI).</div></div><div><h3>Methods and Materials</h3><div>Autosegmentation was performed on 63 CSI patients using 3 methods: Atlas, commercial artificial intelligence (AI), and in-house AI. The performance of these methods was analyzed using 13 quantitative metrics, comprising 6 overlap and 7 distance metrics, across 13 critical organs and a linear mixed-effect model analysis was performed. Additionally, a knowledge-based QA tool was developed by leveraging distinctive computed tomography number distributions from 100 CSI patients for each organ, using the kernel density estimation (KDE) method to ensure robust error detection and validation. The QA tool was tested on 50 CSI cases by comparing baseline KDEs from 100 CSI patients.</div></div><div><h3>Results</h3><div>The linear mixed-effect analysis showed that the in-house AI outperformed both the Atlas and commercial AI methods in overlap and distance metrics. The in-house AI outperformed the commercial AI with a higher average overlap of 0.01 ± 0.01 and surpassed the Atlas method by 0.02 ± 0.01. In terms of distance metrics, the in-house AI matched the commercial AI (–0.31 ± 0.72 mm) and exceeded the Atlas method by 3.10 ± 0.68 mm. Paired t-tests showed the in-house AI was superior to the Atlas in 13.0% of cases, while the Atlas outperformed the in-house method in 8.9% of comparisons. Similarly, the in-house AI was better than the commercial AI in 35.3% of tests, with the commercial AI outperforming in 32.7%. The QA tool results demonstrated that 100% agreement with baseline KDEs occurred in 46.4% of tests for Atlas, 46.5% for the commercial AI, and 60.7% for the in-house AI.</div></div><div><h3>Conclusions</h3><div>The in-house AI excelled over the Atlas and commercial AI methods in autosegmentation accuracy for pediatric CSI patients. Furthermore, a knowledge-based QA tool enables clinicians to detect and correct gross errors in autosegmentation.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 9","pages":"Article 101847"},"PeriodicalIF":2.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Severe Treatment-Related Toxicity in Oral Cavity Squamous Cell Carcinoma with Dyskeratosis Congenita: A Case Report and Critical Review of Radiation-Induced Complications","authors":"Rufus Banks BS,&nbsp;Akul Munjal MD,&nbsp;Erin Healy MD,&nbsp;Jeremy P. Harris MD, MPhil","doi":"10.1016/j.adro.2025.101828","DOIUrl":"10.1016/j.adro.2025.101828","url":null,"abstract":"","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 8","pages":"Article 101828"},"PeriodicalIF":2.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proton Therapy for Uveal Melanoma on a Pencil Beam Scanning Gantry","authors":"Hang Qi PhD ,&nbsp;Lei Hu PhD ,&nbsp;Sheng Huang PhD ,&nbsp;Yen-Po Lee MS ,&nbsp;Qing Chen MS ,&nbsp;Francis Yu MS ,&nbsp;Huifang Zhai MS ,&nbsp;Yunjie Yang PhD ,&nbsp;Minglei Kang PhD ,&nbsp;Peter Park CMD ,&nbsp;Andy Shim CMD ,&nbsp;Xiaoxuan Xu PhD ,&nbsp;David H. Abramson MD ,&nbsp;Jasmine H. Francis MD ,&nbsp;Arpit Chhabra MD ,&nbsp;Charles B. Simone II, MD ,&nbsp;Christopher A. Barker MD ,&nbsp;Haibo Lin PhD","doi":"10.1016/j.adro.2025.101782","DOIUrl":"10.1016/j.adro.2025.101782","url":null,"abstract":"<div><h3>Purpose</h3><div>We present our experience treating ocular tumors in a standard pencil beam scanning (PBS) gantry room without apertures, which could broaden access to proton therapy for patients with ocular cancer globally. Besides, this study explores the dosimetric benefits of beam-specific apertures.</div></div><div><h3>Methods and Materials</h3><div>We retrospectively evaluated 11 consecutive patients with uveal melanoma treated in a clinic gantry room. The dose deviations between the planned and received by the patient were investigated by assessing the forward calculation of the treatment plan on the synthetic computed tomography of cone beam computed tomography. Each plan was forward calculated with a beam-specific brass aperture (BSA) using a Monte Carlo algorithm to explore dosimetric improvements. We compared the plan quality to the delivered plan (DP) using target coverage (D95%) and mean/maximum doses to the adjacent organs.</div></div><div><h3>Results</h3><div>A close agreement between the planned and delivered dose was achieved, with D95% deviations within 3.6% for all treatments, maintaining dose constraints for critical organs. Similar target coverage was reached, with D95% at 101% ± 1.0% (DP) and 101% ± 3.2% (BSA). BSA was effective (<em>P</em> &lt; .05) in reducing the mean [<em>D</em>_Mean (DP, BSA)Gy] and maximum [<em>D</em>_Max (DP, BSA)Gy] dose to organs: retina <em>D</em>_Mean (37.7, 29.5), cornea <em>D</em>_Mean (10.7, 2.4), conjunctiva <em>D</em>_Mean (13.6, 4.1), lacrimal gland <em>D</em>_Mean (25.5, 14.1), optic nerve <em>D</em>_Mean (19.6, 13.1), lens <em>D</em>_Max (22.4, 8.5), cornea <em>D</em>_Max (24.4, 10.2), eyebrow <em>D</em>_Max (15.3, 6.8). BSA lowered the mean dose to surrounding organs and significantly decreased the maximum dose to nonabutting organs (lens, cornea, eyebrow), but had little impact on the maximum dose to the abutting organs (retina, optic nerve).</div></div><div><h3>Conclusions</h3><div>We demonstrate the successful implementation of ocular proton treatment with a standard PBS gantry beamline without apertures. The beam-specific apertures effectively reduced doses to the organs adjacent to the target in the PBS proton treatment while maintaining similar target coverage. This approach offers an opportunity to expand access to ocular proton therapy widely.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 8","pages":"Article 101782"},"PeriodicalIF":2.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Normal Lung Volume Choices on Radiation Pneumonitis Risk Prediction in Locally Non-small Cell Lung Cancer Radiation Therapy","authors":"Alyssa Gadsby MS,&nbsp;Tian Liu PhD,&nbsp;Robert Samstein MD,&nbsp;Jiahan Zhang PhD,&nbsp;Yang Lei PhD,&nbsp;Kenneth E. Rosenzweig MD,&nbsp;Ming Chao PhD","doi":"10.1016/j.adro.2025.101825","DOIUrl":"10.1016/j.adro.2025.101825","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to evaluate the impact of varying definitions of normal lung volume on the prediction of radiation pneumonitis (RP) risk in patients with locally advanced non-small cell lung cancer undergoing radiation therapy.</div></div><div><h3>Methods and Materials</h3><div>Dosimetric variables V20, V5, and mean lung dose (MLD) were extracted from the treatment plans of 442 patients enrolled in the NRG Oncology Radiation Therapy Oncology Group 0617 trial. Three different definitions of lung volume were evaluated: total lung excluding gross tumor target, total lung excluding clinical target volume, and total lung excluding planning target volume (TL-PTV). Patients were grouped as “no-RP2” (<em>N</em> = 377, grade ≤1 RP) and “RP2” (<em>N</em> = 65, grade ≥2 RP). Statistical analyses were performed to assess the effect of lung volume definition on RP2 prediction. Three supervised machine learning models—logistic regression, k-nearest neighbor (kNN), and eXtreme Gradient Boosting—were used to evaluate predictive performance. Model performance was quantified using the area under the receiver operating characteristic curve, and statistical significance was tested via a bootstrap analysis. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions to model predictions.</div></div><div><h3>Results</h3><div>Statistical analyses showed that V20 and MLD were significantly associated with RP2, while differences among the lung volume definitions were not statistically significant. Both k-nearest neighbor and eXtreme Gradient Boosting classifiers consistently yielded higher area under the receiver operating characteristic curve values for the TL-PTV definition compared to the other definitions, a finding supported by bootstrap analysis. SHAP analysis further indicated that V20 and MLD were the most influential predictors of RP2.</div></div><div><h3>Conclusions</h3><div>In line with previous studies, both statistical analysis and SHAP interpretation confirmed that V20 and MLD were associated with RP2. The machine learning models indicated that defining normal lung volume as TL-PTV yielded the highest predictive performance for RP2 risk. Further validation using external data sets are warranted to confirm these findings.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 8","pages":"Article 101825"},"PeriodicalIF":2.2,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric Clues to Addressing Urinary Toxicity Following Stereotactic Prostate Radiation Therapy","authors":"Michael Cardoso MBBS ,&nbsp;Matthew Richardson BMedRadSc ,&nbsp;Phillip Chlap MSc ,&nbsp;Sarah Keats BSc ,&nbsp;Alan Glyde BSc ,&nbsp;Sankar Arumugam PhD ,&nbsp;David Pryor MBBS ,&nbsp;Joseph Bucci MBBS ,&nbsp;Jarad Martin PhD ,&nbsp;Mark Sidhom MBBS","doi":"10.1016/j.adro.2025.101850","DOIUrl":"10.1016/j.adro.2025.101850","url":null,"abstract":"<div><h3>Purpose</h3><div>Delayed genitourinary (GU) toxicity is reported following definitive stereotactic body radiation therapy (SBRT) for prostate cancer in 15% to 30% of patients. The purpose of this study is to investigate whether there is a relationship between radiation dose to the bladder and urethra and GU toxicity grade ≥ 2 (National Cancer Institute Common Terminology Criteria for Adverse Events 4.0) in patients treated with SBRT.</div></div><div><h3>Methods and Materials</h3><div>PROstate Multicenter External beam radioTHErapy Using Stereotactic boost was a phase 2 multicenter trial exploring an SBRT boost of 19 to 20 Gy in 2 fractions to the prostate combined with fractionated external beam radiation therapy as a virtual high-dose-rate brachytherapy boost for patients with prostate cancer. Several bladder and urethral constraints were mandated prospectively. Bladder and the urethral planning organ at risk volume (PRV) dosimetry was correlated with physician-reported GU toxicity for patients ≥ 6 months following SBRT. An association between prior transurethral resection of the prostate (TURP) and urinary toxicity was also examined. Univariant and multivariate analyses were performed.</div></div><div><h3>Results</h3><div>Of the 151 patients, 87 had complete dosimetric data, and these patients were included in this analysis. In this cohort, 19.5% experienced grade ≥ 2 GU toxicity more than 6 months after stereotactic radiation therapy. On univariate analysis, prostatic urethral length, urethral PRV volume, bladder D2 cc, D5 cc, D10 cc, D15 cc, and bladder V8 were predictive of GU toxicity (all <em>P</em> &lt; .05). In the 14 patients who had prior TURP, 6 (43%) experienced GU toxicity compared with 15% for those without prior TURP (<em>P</em> = .015). Multivariate analysis showed that prostatic urethral length, urethral PRV volume, bladder 10 cc, and bladder 15 cc remained statistically significant factors predicting GU toxicity.</div></div><div><h3>Conclusions</h3><div>Prostate SBRT delivered as a virtual high-dose-rate boost is well tolerated overall. However, delayed GU toxicity is experienced by a significant minority of patients. Additional bladder constraints including D10 cc &lt; 17 Gy and D15 cc &lt; 15 Gy may further reduce the risk of delayed GU toxicity. Prior TURP may be a plausible additional risk factor.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 9","pages":"Article 101850"},"PeriodicalIF":2.2,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM","authors":"Jingtong Zhao MS ,&nbsp;Eugene Vaios MD, MBA ,&nbsp;Evan Calabrese MD, PhD ,&nbsp;Zhenyu Yang PhD ,&nbsp;Scott Robertson PhD ,&nbsp;John Ginn PhD ,&nbsp;Ke Lu PhD ,&nbsp;Fang-Fang Yin PhD ,&nbsp;Zachary Reitman MD, PhD ,&nbsp;John Kirkpatrick MD, PhD ,&nbsp;Scott Floyd MD, PhD ,&nbsp;Peter Fecci MD, PhD ,&nbsp;Chunhao Wang PhD","doi":"10.1016/j.adro.2025.101826","DOIUrl":"10.1016/j.adro.2025.101826","url":null,"abstract":"<div><h3>Purpose</h3><div>Stereotactic radiosurgery (SRS) is widely used for brain metastases (BM), but the risk of radionecrosis poses a challenge in post-SRS management. Given the lack of noninvasive imaging methods for distinguishing radionecrosis from recurrence, we aimed to design a deep ensemble learning model that integrates patient clinical features and genomic profiles to identify radionecrosis in patients with BM with post-SRS radiographic progression.</div></div><div><h3>Methods and Materials</h3><div>We studied 90 BMs from 62 patients with non-small cell lung cancer, with 27 biopsy-confirmed post-SRS local recurrences. Clinical features and molecular features were collected. A deep neural network (DNN) was trained for radionecrosis/recurrence prediction using the 3-month post-SRS T1+c magnetic resonance imaging. Preceding the binary prediction output, latent variables were extracted as 1024 deep features. An ensemble learning model was then developed, comprising 2 submodels that fused deep features with clinical (“<em>D+C”</em>) or genomic (“<em>D+G”</em>) features. We employed our positional encoding method to optimally fuse the low-dimensional clinical/genomic features with the high-dimensional image features. The postfusion feature in each submodel yielded a logit result after traversing fully connected layers. The ensemble's final output was the synthesized result of these 2 submodels’ logits via logistic regression. Model training employed an 8:2 train/test split, and 10 model versions were developed for robustness evaluation. Performance metrics were compared against image-only DNN model and “<em>D+C”</em> and “<em>D+G”</em> submodels.</div></div><div><h3>Results</h3><div>The deep ensemble model showed satisfactory performance on the test set, with the area under the receiver operating characteristic curve (ROC_AUC) = 0.91 ± 0.04, sensitivity = 0.87 ± 0.16, specificity = 0.86 ± 0.08, and accuracy = 0.87 ± 0.04. This significantly outperformed the image-only DNN result (ROC_AUC = 0.71 ± 0.05, sensitivity = 0.66 ± 0.32). Higher average performance was also observed compared to the “D+C” result (ROC_AUC = 0.82 ± 0.03, sensitivity = 0.67 ± 0.17) and “D+G” result (ROC_AUC = 0.83 ± 0.02, sensitivity = 0.76 ± 0.22).</div></div><div><h3>Conclusions</h3><div>The deep ensemble model achieved the best performance among the models evaluated in this study for distinguishing BM radionecrosis from recurrence using 3-month post-SRS T1+c MR images, clinical features, and genomic features. This highlights the potential of artificial intelligence in clinical decision-making for BM management, warranting further investigation into its clinical applications.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 8","pages":"Article 101826"},"PeriodicalIF":2.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Regard to Dennstädt et al.","authors":"Ilker Sengul MD ,&nbsp;Demet Sengul MD","doi":"10.1016/j.adro.2025.101799","DOIUrl":"10.1016/j.adro.2025.101799","url":null,"abstract":"","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 7","pages":"Article 101799"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Reply to Sengul I and Sengul D","authors":"Fabio Dennstädt MD ,&nbsp;Janna Hastings PhD ,&nbsp;Paul Martin Putora MD, PhD ,&nbsp;Erwin Vu MD ,&nbsp;Galina Fischer MD, PhD ,&nbsp;Krisztian Süveg MD ,&nbsp;Markus Glatzer MD ,&nbsp;Elena Riggenbach MD ,&nbsp;Hông-Linh Hà ,&nbsp;Nikola Cihoric MD","doi":"10.1016/j.adro.2025.101800","DOIUrl":"10.1016/j.adro.2025.101800","url":null,"abstract":"","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 7","pages":"Article 101800"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spine Stereotactic Body Radiation Therapy for a Patient With a Metallic Implant in Low-Field Magnetic Resonance-Guided Linear Accelerator: A Case Report","authors":"Hiroki Nakayama PhD ,&nbsp;Hiroyuki Okamoto PhD ,&nbsp;Kae Okuma MD, PhD ,&nbsp;Ayaka Nagao MD ,&nbsp;Yuki Tsunoda BSc ,&nbsp;Satoshi Nakamura PhD ,&nbsp;Takahito Chiba MSc ,&nbsp;Tetsu Nakaichi PhD ,&nbsp;Miki Yonemura PhD ,&nbsp;Riki Oshika MSc ,&nbsp;Yuta Kobayashi MSc ,&nbsp;Hironori Kishida MSc ,&nbsp;Shoki Nakamura MSc ,&nbsp;Masato Nishitani MSc ,&nbsp;Shuka Nishina MSc ,&nbsp;Takumi Sakamoto MSc ,&nbsp;Hana Endo BSc ,&nbsp;Junichi Kuwahara MSc ,&nbsp;Yasunori Shuto MSc ,&nbsp;Masataka Ueda MSc ,&nbsp;Hiroshi Igaki MD, PhD","doi":"10.1016/j.adro.2025.101843","DOIUrl":"10.1016/j.adro.2025.101843","url":null,"abstract":"<div><div>The rise in radiation therapy challenges facing the management of pain and neurologic symptoms from vertebral metastasis has paralleled advances in cancer treatment and patient prognosis. Surgical options include decompression to alleviate spinal cord compression symptoms, with consideration for spinal stability through fixation using titanium alloy implants. Previous studies comparing radiation alone with postoperative irradiation following decompression surgery showed superior functional outcomes. Stereotactic body radiation therapy (SBRT), which concentrates radiation on the tumor while sparing surrounding organs, has clinical advantages when combined with surgery. However, the accurate delineation of targets and organs, particularly the spinal cord of a patient with metallic implants, is difficult with computed tomography and magnetic resonance (MR) images because of metal artifacts and registration errors. The low-field MR-guided radiation therapy (the low-field MRgRT) system offers advantages in target delineation with metal artifact suppression because of its associated low-magnetic field and no need for registration between a computed tomography image and an MR image for the delineation of targets and organs because the low-field MRgRT system uses the MR image acquired by itself as the primary image. The advantages make the low-field MRgRT suitable for spine SBRT, especially for patients with metallic implants. Here, we present a case of successful postoperative spine SBRT using the low-field MRgRT, ensuring the identification of the spinal cord, and safe and accurate treatment in a patient with metallic implants. The conclusion highlights the low-field MRgRT as a viable option for postoperative spine SBRT, which is particularly beneficial for patients with metallic implants, ensuring treatment safety and accuracy.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 9","pages":"Article 101843"},"PeriodicalIF":2.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining Barriers to Practice in Genitourinary and Gynecologic Radiation Oncology: Results from 2 Nationwide Surveys","authors":"Patrick Doyle MS ,&nbsp;Neha Vapiwala MD, FACR, FASTRO, FASCO ,&nbsp;Mutlay Sayan MD ,&nbsp;Miranda Lam MD, MBA ,&nbsp;Shalini Moningi MD","doi":"10.1016/j.adro.2025.101848","DOIUrl":"10.1016/j.adro.2025.101848","url":null,"abstract":"<div><h3>Purpose</h3><div>Gender diversity in academic radiation oncology (RO) has become a topic of interest in recent years, with studies showing that practicing female academic radiation oncologists (AROs) are outnumbered by male colleagues at a rate of approximately 3:1. Gender differences are also observed in subspecialties whose patient populations are overwhelmingly of a single gender, such as genitourinary (GU) and gynecologic (GYN) RO. We aimed to investigate whether challenges exist for academic RO physicians who primarily treat patients of another gender, and, if so, what barriers they face in practice.</div></div><div><h3>Methods and Materials</h3><div>We conducted 2 national surveys of female GU academic RO physicians and male GYN academic RO physicians working at Accreditation Council for Graduate Medical Education-accredited academic centers. Survey questions focused on career path, challenges faced, and barriers to practicing GU or GYN oncology.</div></div><div><h3>Results</h3><div>A total of 13/42 (30.2%) GU survey recipients responded as treating GU oncology and 31/77 (40.3%) GYN survey recipients responded as treating GYN oncology. Of these respondents, 9 GU and 3 GYN physicians reported facing challenges as an academic RO faculty member because of their gender identity, and 5 GU and 4 GYN physicians reported that their subspecialty specifically presented challenges. Neither group commonly reported difficulties developing trust and rapport with patients. In the GU academic RO group, reports of challenging relationships with other professional colleagues were common. Difficulties finding or serving as a mentor were also common in both groups.</div></div><div><h3>Conclusions</h3><div>Female GU AROs and male GYN AROs may face unique challenges. Identifying and understanding these challenges directly from practicing physicians are important steps in improving professional success, career satisfaction, and clinical care quality.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 9","pages":"Article 101848"},"PeriodicalIF":2.2,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}