AI-GUIDED SURGICAL PATHOLOGY FOR PERSONALIZED ONCOLOGY

Abstract

This study using deep learning-based histopathological image analysis with the ability to learn in an iterative manner of clinical returns will develop and test an AI-guided surgical pathology paradigm to achieve personalised oncology treatment.  High-resolution WSIs of cancer tissue biopsies were interpreted within convolutional neural networks, due to prior annotation as done by pathologists via a mixed-methods experimental design.  Quantitative analysis also demonstrated the good diagnostic performance with high sensitivity and specificity across the tumour subtypes of the models.  The HAP analysis confirmed the independent relevance of morphological features like nuclear pleomorphism and mitotic activity, and the subjective pathologist and oncologist input was incorporated to assess the interpretability and clinical utility.  In over 90 percent of test conditions, the clinical decision support component recommended personalised therapy that was in agreement with expert consensus.  A feedback integration loop allowed continuous improvement of models with respect to practical observations and contributed to a higher dependability and contextual flexibility of the system.  Fig. 1 indicates the visual methodology workflow, and they show the full process of tissue sample to feedback-driven AI enhancement.  So, taking everything into consideration, the proposed framework paves the way toward scalable structure of next-generation clinical decision-making systems and describes how AI can advance the diagnostic accuracy and personalisation of therapy in oncology.