Call for Review: Interpretable and Explainable Models for Breast Cancer Recurrence Prediction — Systematic Review Protocol

Researchers at the University of Benghazi are conducting a systematic review comparing interpretable statistical models with explainable AI approaches for predicting breast cancer recurrence. The review, registered in PROSPERO, aims to identify methodological gaps and guide future development of trustworthy prognostic models. The team is seeking feedback on their protocol before completing data extraction.

We are conducting a systematic review comparing intrinsically interpretable statistical models, such as logistic regression and Cox proportional hazards models, with explainable artificial intelligence XAI approaches, including machine learning and deep learning models supported by SHAP, LIME, attention mechanisms, or other explanation methods, for predicting breast cancer recurrence after primary treatment. The review is registered in PROSPERO: CRD420251145602; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251145602 https://www.researchgate.net/deref/https%3A%2F%2Fwww.crd.york.ac.uk%2FPROSPERO%2Fview%2FCRD420251145602 Our goal is to critically appraise published models across the black-box, grey-box, and white-box spectrum, identify current methodological and reporting gaps, and outline future directions for trustworthy, clinically usable prognostic models in oncology. We are currently completing data extraction and would welcome feedback from colleagues working in breast cancer prognosis, prediction modelling, oncology, or explainable/interpretable AI before we move to risk-of-bias assessment and evidence synthesis. The full protocol, including the eligibility criteria, search strategy, and data extraction/appraisal plan, is attached to this post. We would be grateful for comments on: • the review question and eligibility criteria PICO ; • the search strategy across PubMed, Scopus, IEEE Xplore, Google Scholar, and Research4Life; • the planned data extraction and risk-of-bias/quality appraisal tools, including CHARMS, PROBAST+AI, and TRIPOD+AI. Please share any comments under this post or contact the corresponding author directly. Corresponding author: Eiman Sahly, University of Benghazi, Libya eiman.sahly@uob.edu.ly https://www.researchgate.net/deref/mailto%3Aeiman.sahly%40uob.edu.ly ORCID: 0000-0002-5888-6305 Review team: Eiman Sahly, Sophie Pilleron, Aiman Gannous, and Abdelfattah Elbarsha