Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.

To thoroughly analyze factors affecting the generalization ability of deep learning algorithms on brain tumor detection and segmentation models. We searched PubMed, Embase, Web of Science, Cochrane Library, and IEEE from inception to July 25, 2023, and 19 studies with 12,000 patients were identified. The criteria required studies to use magnetic resonance imaging (MRI) for brain tumor detection and segmentation, offer clear performance metrics, and use external validation data sets. The study focused on outcomes such as sensitivity and Dice score. Study quality was assessed using QUADAS-2 and CLAIM tools. The meta-analysis evaluated varying algorithms and their performance across different validation data sets. MRI hardware as the manufacturer may contribute to data set diversity, impacting AI model generalizability. The study found that the best algorithms had a pooled lesion-wise Dice score of 84%, with pooled sensitivities of 87% (patient-wise) and 86% (lesion-wise). Post-2022 methodologies highlighted evolving artificial intelligence techniques. Performance differences were evident among tumor types, likely due to size disparities. 3D models outperformed their 2D and ensemble counterparts in detection. Although specific preprocessing techniques improved segmentation outcomes, some hindered detection. The study underscores the potential of deep learning in improving brain tumor diagnostics and treatment planning. We also identify the need for further research, including developing a comprehensive diversity index, expanded meta-analyses, and using generative adversarial networks for data diversification, paving the way for AI-driven advancements in oncological patient care. PROPERO (CRD42023459108).

The clinical treatments of pelvic cancers, such as cervical cancer, adenocarcinoma, and prostate cancer, include not only surgical resection but also radiotherapy. Radiotherapy uses radiation to target and control the development of the disease. Before radiotherapy, precise marking and delineation are essential for radiation dose planning. The delineated areas include the lesion site and clinically determined potential areas, such as lymph nodes and surrounding organs at risk (OAR), including the bladder, uterus, large intestine, and small intestine. Currently, the clinical labeling of these lesions and OAR areas is done manually by experienced radiation oncologists and medical physicists. The pelvic cavity's anatomical structure is complex, and the lymph node area in the pelvic cavity is recognized as a high-risk area, thus included in the irradiation area. However, lymph nodes on CT images have fewer clear borders than other OAR areas, making it difficult to delineate and increasing variability between different markers. This study employs artificial intelligence algorithms to automatically delineate clinical target volumes (CTV), especially pelvic lymph nodes, and attempts to incorporate anatomical information to facilitate more accurate annotation. This approach aims to reduce the burden of clinical treatment planning and improve precision in radiotherapy.