2024/11/11 by Weixi Yi, Yi, Weixi, Yipei Wang +15 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Prostate Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.07416
openalex publication_date 2024/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. While machine learning (ML) models have demonstrated radiologist-level accuracy in detecting prostate cancer from these two sequences, this study investigates the potential of ML-enabled methods using only the T2w sequence as input during inference time. We first discuss the technical feasibility of such a T2-only approach, and then propose a novel ML formulation, where DWI sequences - readily available for training purposes - are only used to train a meta-learning model, which subsequently only uses T2w sequences at inference. Using multiple datasets from more than 3,000 prostate cancer patients, we report superior or comparable performance in localising radiologist-identified prostate cancer using our proposed T2-only models, compared with alternative models using T2-only or both sequences as input. Real patient cases are presented and discussed to demonstrate, for the first time, the exclusively true-positive cases from models with different input sequences.