OncoPath: AI-Driven Breast Cancer Tumor Detection and Personalized Treatment Prediction Using Deep Learning and Clinical Guidelines

Abstract

Breast cancer is a leading cause of cancer-related deaths among women, making early detection and accurate diagnosis critical. This study introduces OncoPath, an AI-powered system for breast cancer tumor detection and personalized treatment prediction. The system uses deep learning for tumor segmentation, machine learning for predicting cancer stages, and clinical guidelines to suggest personalized treatment plans. By analyzing breast imaging data, OncoPath detects tumors, assesses malignancy risk using the BI-RADS framework, and predicts cancer stages. The system then recommends treatment options such as surgery, chemotherapy, or radiation, tailored to the predicted stage and the patient's specific factors. To ensure model reliability, evaluation metrics such as Dice Coefficient (73.69 %) and Precision (75 %) are used. A key innovation of OncoPath is its user-friendly interface, designed for easy integration into hospital workflows, offering real-time visualizations of tumor segmentation, risk classification, and treatment suggestions. This modular system is built to evolve with future advancements, incorporating multimodal data and explainable AI techniques to improve decision-making transparency. With this approach, OncoPath aims to streamline diagnostic tasks, reduce errors, and optimize patient outcomes in clinical settings.

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