Abstract Estimates suggest that around 20%-40% of cancer patients are affected by brain metastases. A subset of these patients first present with central nervous system manifestations with an unknown primary. The issue then arises regarding which region of the body should be imaged, if not the entire body, exposing the patient to more radiation. To address this issue, streamline workflows, and save precious time, we developed a software clinicians can use to identify primary tumor locus. “BrainMets” is a user-friendly software that handles a DICOM input, allows the user to segment the region(s) of interest manually, and uses a deep learning model to predict the most likely tumor origin. The model is a mixed-effects neural network pre-trained on radiomic features from open-source data by Ocaña-Tienda et al. (2023) using 637 high-resolution imaging studies from 10 different medical centers with an accuracy of 90.5%, precision of 92.7%, recall of 87.8%, and F1 score of 90.2%. The software leverages the open-source software 3D-Slicer to help segment regions of interest, extract radiomic features, and make predictions. It is built using Flask, a Python package for backend development. The frontend user interface is created using HTML/CSS and Bootstrap. The “BrainMets” software thus strives to enhance diagnostic workflow significantly, reduce appointment times for scans, and improve patient experience. Future enhancements include integrating more AI models, improving healthcare IT system compatibility, and updating datasets to boost predictive accuracy and generalizability. This tool sets a new standard in personalized medicine, optimizing treatment strategies for cancer patients with brain metastases. As adoption increases, it could substantially improve patient outcomes and reduce healthcare costs associated with misdiagnosis and overtreatment. Further research and development will focus on expanding its applicability across various types of cancers.
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