A survey of cancer patients' interest in undertaking exercise to promote relaxation during radiotherapy for breast cancer and metastatic cancer.

BACKGROUND: Approximately 25-50% of patients undergoing radiotherapy (RT) experience psychological distress and anxiety, which can detrimentally affect both their quality of life and treatment outcomes. While previous research has demonstrated that relaxation exercises can enhance the tolerability of RT and alleviate associated stress and anxiety, the specific needs for such therapies in radiation oncology remain under-explored. This study aims to investigate the demand for and preferences toward relaxation exercises among radiotherapy patients, addressing a critical gap in patient-centered care. METHODS: A prospective pseudonymized survey study using a one-time paper-based questionnaire was conducted from 2022 to 2023 among patients undergoing curative-intent RT for breast cancer or patients undergoing palliative RT for bone metastases. Patients were asked in a 11-item questionnaire about their anxiety, pre-existing practice of relaxation exercises/interventions, their interest in relaxation exercises, and preferences on the type and format of instruction. Data were analyzed descriptively. RESULTS: 100 patients (74 female and 26 male) responded, of whom 68 received curative-intent adjuvant RT and 32 palliative RT. Median age was 62 years. 78% of patients indicated a desire to be actively involved in their radiotherapy, but only 27% had used relaxation exercises prior to RT. 44.8% of both curatively and palliatively treated patients who wanted to be actively involved in their therapy desired to learn how to best relax. 56.4% of respondents were willing to spend extra time learning offered exercises. CONCLUSION: The survey indicates that patients undergoing RT, both for curative or palliative intent, desire relaxation exercises to relieve stress and anxiety from RT. It is therefore important to assess the need for relaxation interventions in individual patients and to develop suitable programs or collaborate with other healthcare professionals to meet these needs.

Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.

BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.

Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities.

Although radiotherapy continues to evolve as a mainstay of the oncological armamentarium, research and innovation in radiotherapy in low-income and middle-income countries (LMICs) faces challenges. This third Series paper examines the current state of LMIC radiotherapy research and provides new data from a 2022 survey undertaken by the International Atomic Energy Agency and new data on funding. In the context of LMIC-related challenges and impediments, we explore several developments and advances-such as deep phenotyping, real-time targeting, and artificial intelligence-to flag specific opportunities with applicability and relevance for resource-constrained settings. Given the pressing nature of cancer in LMICs, we also highlight some best practices and address the broader need to develop the research workforce of the future. This Series paper thereby serves as a resource for radiation professionals.

Practice Patterns of Spatially Fractionated Radiation Therapy: A Clinical Practice Survey.

Purpose: Spatially fractionated radiation therapy (SFRT) is increasingly used for bulky advanced tumors, but specifics of clinical SFRT practice remain elusive. This study aimed to determine practice patterns of GRID and Lattice radiation therapy (LRT)-based SFRT. Methods and Materials: A survey was designed to identify radiation oncologists' practice patterns of patient selection for SFRT, dosing/planning, dosimetric parameter use, SFRT platforms/techniques, combinations of SFRT with conventional external beam radiation therapy (cERT) and multimodality therapies, and physicists' technical implementation, delivery, and quality procedures. Data were summarized using descriptive statistics. Group comparisons were analyzed with permutation tests. Results: The majority of practicing radiation oncologists (United States, 100%; global, 72.7%) considered SFRT an accepted standard-of-care radiation therapy option for bulky/advanced tumors. Treatment of metastases/recurrences and nonmetastatic primary tumors, predominantly head and neck, lung cancer and sarcoma, was commonly practiced. In palliative SFRT, regimens of 15 to 18 Gy/1 fraction predominated (51.3%), and in curative-intent treatment of nonmetastatic tumors, 15 Gy/1 fraction (28.0%) and fractionated SFRT (24.0%) were most common. SFRT was combined with cERT commonly but not always in palliative (78.6%) and curative-intent (85.7%) treatment. SFRT-cERT time sequencing and cERT dose adjustments were variable. In curative-intent treatment, concurrent chemotherapy and immunotherapy were found acceptable by 54.5% and 28.6%, respectively. Use of SFRT dosimetric parameters was highly variable and differed between GRID and LRT. SFRT heterogeneity dosimetric parameters were more commonly used (P = .008) and more commonly thought to influence local control (peak dose, P = .008) in LRT than in GRID therapy. Conclusions: SFRT has already evolved as a clinical practice pattern for advanced/bulky tumors. Major treatment approaches are consistent and follow the literature, but SFRT-cERT combination/sequencing and clinical utilization of dosimetric parameters are variable. These areas may benefit from targeted education and standardization, and knowledge gaps may be filled by incorporating identified inconsistencies into future clinical research.

Dosimetric Validation for Prospective Clinical Trial of GRID Collimator-Based Spatially Fractionated Radiation Therapy: Dose Metrics Consistency and Heterogeneous Pattern Reproducibility.

PURPOSE: Dose heterogeneity within a tumor target is likely responsible for the biologic effects and local tumor control from spatially fractionated radiation therapy (SFRT). This study used a commercially available GRID-pattern dose mudulated nonuniform radiation therapy (GRID) collimator to assess the interplan variability of heterogeneity dose metrics in patients with various bulky tumor sizes and depths. METHODS AND MATERIALS: The 3-dimensional heterogeneity metrics of 14 bulky tumors, ranging from 155 to 2161 cm3 in volume, 6 to 23 cm in equivalent diameter, and 3 to 13 cm in depth, and treated with GRID collimator-based SFRT were studied. A prescription dose of 15 Gy was given at the tumor center with 6 MV photons. The dose-volume histogram indices, dose heterogeneity parameters, and peak/valley dose ratios were derived; the equivalent uniform doses of cancer cells with various radiosensitivities in each plan were estimated. To account for the spatial fractionation, high dose core number density of the tumor target was defined and calculated. RESULTS: Among 14 plans, the dose-volume histogram indices D5, D10, D50, D90, and D95 (doses covering 5%, 10%, 50%, 90%, and 95% of the target volume) were found within 10% variation. The dose ratio of D10/D90 also showed a moderate consistency (range, 3.9-5.0; mean, 4.4). The equivalent uniform doses were consistent, ranging from 4.3 to 5.5 Gy, mean 4.6 Gy, for radiosensitive cancer cells and from 5.8 to 6.9 Gy, mean 6.2 Gy, for radioresistant cancer cells. The high dose core number density was within 20% among all plans. CONCLUSIONS: GRID collimator-based SFRT delivers a consistent heterogeneity dose distribution and high dose core density across bulky tumor plans. The interplan reproducibility and simplicity of GRID therapy may be useful for certain clinical indications and interinstitutional clinical trial design, and its heterogeneity metrics may help guide multileaf-collimator-based SFRT planning to achieve similar or further optimized dose distributions.

Overview and Recommendations for Prospective Multi-institutional Spatially Fractionated Radiation Therapy Clinical Trials.

PURPOSE: The highly heterogeneous dose delivery of spatially fractionated radiation therapy (SFRT) is a profound departure from standard radiation planning and reporting approaches. Early SFRT studies have shown excellent clinical outcomes. However, prospective multi-institutional clinical trials of SFRT are still lacking. This NRG Oncology/American Association of Physicists in Medicine working group consensus aimed to develop recommendations on dosimetric planning, delivery, and SFRT dose reporting to address this current obstacle toward the design of SFRT clinical trials. METHODS AND MATERIALS: Working groups consisting of radiation oncologists, radiobiologists, and medical physicists with expertise in SFRT were formed in NRG Oncology and the American Association of Physicists in Medicine to investigate the needs and barriers in SFRT clinical trials. RESULTS: Upon reviewing the SFRT technologies and methods, this group identified challenges in several areas, including the availability of SFRT, the lack of treatment planning system support for SFRT, the lack of guidance in the physics and dosimetry of SFRT, the approximated radiobiological modeling of SFRT, and the prescription and combination of SFRT with conventional radiation therapy. CONCLUSIONS: Recognizing these challenges, the group further recommended several areas of improvement for the application of SFRT in cancer treatment, including the creation of clinical practice guidance documents, the improvement of treatment planning system support, the generation of treatment planning and dosimetric index reporting templates, and the development of better radiobiological models through preclinical studies and through conducting multi-institution clinical trials.

An International Consensus on the Design of Prospective Clinical-Translational Trials in Spatially Fractionated Radiation Therapy for Advanced Gynecologic Cancer.

Despite the unexpectedly high tumor responses and limited treatment-related toxicities observed with SFRT, prospective multi-institutional clinical trials of SFRT are still lacking. High variability of SFRT technologies and methods, unfamiliar complex dose and prescription concepts for heterogeneous dose and uncertainty regarding systemic therapies present major obstacles towards clinical trial development. To address these challenges, the consensus guideline reported here aimed at facilitating trial development and feasibility through a priori harmonization of treatment approach and the full range of clinical trial design parameters for SFRT trials in gynecologic cancer. Gynecologic cancers were evaluated for the status of SFRT pilot experience. A multi-disciplinary SFRT expert panel for gynecologic cancer was established to develop the consensus through formal panel review/discussions, appropriateness rank voting and public comment solicitation/review. The trial design parameters included eligibility/exclusions, endpoints, SFRT technology/technique, dose/dosimetric parameters, systemic therapies, patient evaluations, and embedded translational science. Cervical cancer was determined as the most suitable gynecologic tumor for an SFRT trial. Consensus emphasized standardization of SFRT dosimetry/physics parameters, biologic dose modeling, and specimen collection for translational/biological endpoints, which may be uniquely feasible in cervical cancer. Incorporation of brachytherapy into the SFRT regimen requires additional pre-trial pilot investigations. Specific consensus recommendations are presented and discussed.

A Dosimetric Parameter Reference Look-Up Table for GRID Collimator-Based Spatially Fractionated Radiation Therapy.

Computations of heterogeneity dose parameters in GRID therapy remain challenging in many treatment planning systems (TPS). To address this difficulty, we developed reference dose tables for a standard GRID collimator and validate their accuracy. The .decimal Inc. GRID collimator was implemented within the Eclipse TPS. The accuracy of the dose calculation was confirmed in the commissioning process. Representative sets of simulated ellipsoidal tumours ranging from 6-20 cm in diameter at a 3-cm depth; 16-cm ellipsoidal tumours at 3, 6, and 10 cm in depth were studied. All were treated with 6MV photons to a 20 Gy prescription dose at the tumour center. From these, the GRID therapy dosimetric parameters (previously recommended by the Radiosurgery Society white paper) were derived. Differences in D5 through D95 and EUD between different tumour sizes at the same depth were within 5% of the prescription dose. PVDR from profile measurements at the tumour center differed from D10/D90, but D10/D90 variations for the same tumour depths were within 11%. Three approximation equations were developed for calculating EUDs of different prescription doses for three radiosensitivity levels for 3-cm deep tumours. Dosimetric parameters were consistent and predictable across tumour sizes and depths. Our study results support the use of the developed tables as a reference tool for GRID therapy.

The optimal management of brain metastases from gestational trophoblastic neoplasia.

INTRODUCTION: Gestational trophoblastic diseases and neoplasias (GTDs and GTNs) comprise a spectrum of diseases arising from abnormally proliferating placental/trophoblastic tissue following an antecedent molar or non-molar pregnancy. These can spread to the brain hematogenously in about 10% of patients, mostly in high-risk disease. The optimal management of patients with brain metastases from GTN is unclear, with multiple systemic regimens under use and an uncertain role for radiotherapy. AREAS COVERED: Here, we review the epidemiology, workup, and treatment of GTN with central nervous system (CNS) involvement. Literature searches in PubMed and Google Scholar were conducted using combinations of keywords such as 'gestational trophoblastic disease,' 'gestational trophoblastic neoplasia,' 'choriocarcinoma,' and 'brain metastases.' EXPERT OPINION: Systemic therapy is the frontline treatment for GTN with brain metastases, and radiotherapy should only be considered in the context of a clinical trial or for resistant/recurrent disease. Surgery has a limited role in palliating symptoms or relieving intracranial pressure/bleeding. Given the highly specialized care these patients require, treatment at a high-volume referral center with multidisciplinary collaboration likely leads to better outcomes. Randomized trials should be conducted to determine the best systemic therapy option for GTN.

An International Consensus on the Design of Prospective Clinical-Translational Trials in Spatially Fractionated Radiation Therapy.

PURPOSE: Spatially fractionated radiation therapy (SFRT), which delivers highly nonuniform dose distributions instead of conventionally practiced homogeneous tumor dose, has shown high rates of clinical response with minimal toxicities in large-volume primary or metastatic malignancies. However, prospective multi-institutional clinical trials in SFRT are lacking, and SFRT techniques and dose parameters remain variable. Agreement on dose prescription, technical administration, and clinical and translational design parameters for SFRT trials is essential to enable broad participation and successful accrual to rigorously test the SFRT approach. We aimed to develop a consensus for the design of multi-institutional clinical trials in SFRT, tailored to specific primary tumor sites, to help facilitate development and enhance the feasibility of such trials. METHODS AND MATERIALS: Primary tumor sites with sufficient pilot experience in SFRT were identified, and fundamental trial design questions were determined. For each tumor site, a comprehensive consensus effort was established through disease-specific expert panels. Clinical trial design criteria included eligibility, SFRT technology and technique, dose and fractionation, target- and normal-tissue dose parameters, systemic therapies, clinical trial endpoints, and translational science considerations. Iterative appropriateness rank voting, expert panel consensus reviews and discussions, and public comment posting were used for consensus development. RESULTS: Clinical trial criteria were developed for head and neck cancer and soft-tissue sarcoma. Final consensus among the 22 trial design categories each (a total of 163 criteria) was high to moderate overall. Uniform patient cohorts of advanced bulky disease, standardization of SFRT technologies and dosimetry and physics parameters, and collection of translational correlates were considered essential to trial design. Final guideline recommendations and the degree of agreement are presented and discussed. CONCLUSIONS: This consensus provides design guidelines for the development of prospective multi-institutional clinical trials testing SFRT in advanced head and neck cancer and soft-tissue sarcoma through in-advance harmonization of the fundamental clinical trial design among SFRT experts, potential investigators, and the SFRT community.

MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

PURPOSE: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS: The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION: This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging.

BACKGROUND: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

The dosimetric benefit of in-advance respiratory training for deep inspiration breath holding is realized during daily treatment in left breast radiotherapy: A comparative retrospective study of serial surface motion tracking.

INTRODUCTION: A novel approach of in-advance preparatory respiratory training and practice for deep inspiration breath holding (DIBH) has been shown to further reduce cardiac dose in breast cancer radiotherapy patients, enabled by deeper (extended) DIBH. Here we investigated the consistency and stability of such training-induced extended DIBH after training completion and throughout the daily radiotherapy course. METHODS: Daily chestwall motion from real-time surface tracking transponder data was analysed in 67 left breast radiotherapy patients treated in DIBH. Twenty-seven received preparatory DIBH training/practice (prep Trn) 1-2 weeks prior to CT simulation, resulting in an extended DIBH (ext DIBH) and reduced cardiac dose at simulation. Forty had only conventional immediate pre-procedure DIBH instruction without prep Trn and without extended DIBH (non-Trn group). Day-to-day variability in chestwall excursion pattern during radiotherapy was compared among the groups. RESULTS: The average of daily maximum chestwall excursions was overall similar, 2.5 ± 0.6 mm for prep Trn/ext DIBH vs. 2.9 ± 0.8 mm for non-Trn patients (P = 0.24). Chestwall excursions beyond the 3-mm tolerance threshold were less common in the prep Trn/ext DIBH group (18.8% vs. 37.5% of all fractions within the respective groups, P = 0.038). Among patients with cardiopulmonary disease those with prep Trn/ext DIBH had fewer chestwall excursions beyond 3 mm (9.4% vs. 46.7%, P = 0.023) and smaller average maximum excursions than non-Trn patients (2.4 ± 0.3 vs. 3.0 ± 0.6 mm, P = 0.047, respectively). CONCLUSION: Similar stability of daily DIBH among patients with and without preparatory training/practice suggests that the training-induced extended DIBH and cardiac dose reductions were effectively sustained throughout the radiotherapy course. Training further reduced beyond-tolerance chestwall excursions, particularly in patients with cardiopulmonary disease.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

BACKGROUND: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Tackling impostor syndrome: A multidisciplinary approach.

What is Imposter Syndrome, whom does it affect, and when, and why is it important to recognize? In this multidisciplinary article, the phenomenon is defined and discussed by a psychiatrist, followed by strategic advice by a radiologist, interventional radiologist and radiation oncologist.

Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer.

RATIONALE AND OBJECTIVES: To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer. MATERIALS AND METHODS: This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [Tmax] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs). RESULTS: There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower Tmax (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either Tmax (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011). CONCLUSION: This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

Systematic Review and Meta-Analysis on the Use of Photon-based Stereotactic Radiosurgery Versus Fractionated Stereotactic Radiotherapy for the Treatment of Uveal Melanoma.

PURPOSE: The purpose of this report is to assess the efficacy and adverse event profile of photon-based stereotactic radiosurgery (SRS) compared with fractionated stereotactic radiotherapy (fSRT) for the treatment of uveal melanoma. Primary outcomes include incidence proportions of local control, enucleation, metastatic progression, disease-specific, and overall mortality. Treatment-related toxicities such as incidence proportions of radiation retinopathy, neovascular glaucoma, optic neuropathy, and cataract formation were examined as secondary outcomes. Five-year survival and 5-year local control rates were also assessed. MATERIALS AND METHODS: PubMed, Embase, Web of Science, Scopus, and 2 Cochrane databases were searched up to December 31, 2018. Random effects models were used to calculate pooled incidence proportions of outcome measures. Meta-regression was carried out to explore the potential impact of dose per fraction on local control. RESULTS: Twenty-four articles with a total of 1745 patients were included in the meta-analysis. There were no statistically significant differences between photon-based fSRT and SRS for all primary, secondary and 5-year outcome measures, including local control (P=0.28), enucleation (P=0.51), and neovascular glaucoma (P=0.40). The 5-year local control rate was 90% (95% confidence interval: 76%, 96%) for fSRT and 89% (70%, 97%) for SRS. CONCLUSIONS: Our meta-analysis showed no difference in tumor control, survival and toxicities, as defined in this paper, between SRS and fSRT for uveal melanoma. Confounding biases remain an expected limitation in this study of novel treatment modalities deployed in rare tumors. Further investigation is needed to validate outcomes and compare stereotactic treatment techniques.