Yi, Weixi;
Wang, Yipei;
Thorley, Natasha;
Ng, Alexander;
Punwani, Shonit;
Kasivisvanathan, Veeru;
Barratt, Dean C;
... Hu, Yipeng; + view all
(2025)
T2-Only Prostate Cancer Prediction by Meta-Learning from BI-Parametric MR Imaging.
In:
2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI).
(pp. pp. 1-5).
IEEE: Houston, TX, USA.
Preview |
Text
2411.07416v1.pdf - Accepted Version Download (2MB) | Preview |
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 fea-sibility 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. Open-source code is available at https://github.com/wxyi057/MetaT2.
Archive Staff Only
 |
View Item |
