T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging

• URL: http://arxiv.org/abs/2411.07416v1
• Date: Mon, 11 Nov 2024 22:38:45 GMT
• Title: T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
• Authors: Weixi Yi, Yipei Wang, Natasha Thorley, Alexander Ng, Shonit Punwani, Veeru Kasivisvanathan, Dean C. Barratt, Shaheer Ullah Saeed, Yipeng Hu,
• Abstract summary: Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences. measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. This study investigates the potential of machine learning (ML) methods using only the T2w sequence as input during inference time.
• Score: 39.64252838533947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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