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Background Cancer patients are often treated with radiation, therefore increasing their exposure to high energy emissions. In such cases, medical errors may be threatening or fatal, inducing the need to innovate new methods for maximum reduction of irreversible events. Training is an efficient and methodical tool to subject professionals to the real world and heavily educate them on how to perform with minimal errors. An evolving technique for this is Serious Gaming that can fulfill this purpose, especially with the rise of COVID-19 and the shift to the online world, by realistic and visual simulations built to present engaging scenarios. This paper presents the first Serious Game for Lung Cancer Radiotherapy training that embodies Biomedical Engineering principles and clinical experience to create a realistic and precise platform for coherent training. Methods To develop the game, thorough 3D modeling, animation, and gaming fundamentals were utilized to represent the whole clinical process of treatment, along with the scores and progress of every player. The model's goal is to output coherency and organization for students' ease of use and progress tracking, and to provide a beneficial educational experience supplementary to the users' training. It aims to also expand their knowledge and use of skills in critical cases where they must perform crucial decision-making and procedures on patients of different cases. Results At the end of this research, one of the accomplished goals consists of building a realistic model of the different equipment and tools accompanied with the radiotherapy process received by the patient on Maya 2018, including the true beam table, gantry, X-ray tube, CT Scanner, and so on. The serious game itself was then implemented on Unity Scenes with the built models to create a gamified authentic environment that incorporates the 5 main series of steps; Screening, Contouring, External Beam Planning, Plan Evaluation, Treatment, to simulate the practical workflow of an actual Oncology treatment delivery for lung cancer patients. Conclusion This serious game provides an educational and empirical space for training and practice that can be used by students, trainees, and professionals to expand their knowledge and skills in the aim of reducing potential errors. [ABSTRACT FROM AUTHOR]

Background: The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy.
Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners.
Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion.
Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model.
Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images., (Creative Commons Attribution license.)

Introduction: Brain metastases (BM) are rare in colorectal cancer (CRC) and are associated with a dismal prognosis. This work aims to report the rate of BM in CRC patients treated in a single institution, along with survival and prognostic factors., Methods: Medical charts for patients with histologically proven CRC were retrospectively reviewed., Results: A total of 538 patients were identified, of whom 33% developed any metastatic disease and 4.4% presented BM. Lung was the most frequently associated metastatic site (in 68% of the cases). The only factor independently associated with BM development was the presence of metastatic disease at the time of initial presentation. The median duration from initial diagnosis to BM development was 38.6 months (SD 29.1 months). Median survival after BM development was 62 days (95% confidence interval [CI] 56-68). Patients diagnosed with BM within 1 year of cancer diagnosis responded significantly better to treatment than those who acquired BM later, with a median survival after BM diagnosis of 261 days versus 61 days, respectively ( p = .002). Patients with BM who received antiangiogenic therapy had an improved median survival compared to those who did not (151 days vs 59 days, p = 0.02; hazard ratio for death 0.29 [95% CI 0.09-0.94])., Conclusion: CRC with BM is an aggressive disease resistant to standard treatment and is associated with poor outcomes. Adding antiangiogenic therapy might be of value for those patients. Patients with BM developing early in the disease course might respond better to treatment.