仅用T2序列实现前列腺癌精准预测，突破传统依赖多模态影像的限制。
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
- 基于元学习框架，训练时用DWI数据，推理时仅需T2序列
- 在3000+患者数据上表现优于或相当传统双序列模型
- 首次展示纯T2模型的真阳性病例，适合临床快速筛查
当前基于影像的前列腺癌诊断需要同时使用磁共振T2加权（T2w）和扩散加权成像（DWI）序列，且额外序列可能提升准确性。然而，DWI序列的扩散模式测量耗时、易受伪影影响且对成像参数敏感。尽管机器学习模型已能在双序列输入下达到放射科医生水平的检测精度，本研究探索仅以T2w序列为输入的机器学习方法可行性。我们首先分析该纯T2方法的技术可行性，提出一种新型机器学习范式：训练阶段使用可获得的DWI序列，但推理阶段仅依赖T2w序列。基于超过3000名患者的多个数据集，我们的纯T2模型在定位放射科医生标记的前列腺癌方面表现优于或相当于使用单序列或双序列输入的替代模型。通过真实患者案例展示并讨论了不同输入序列模型产生的唯一真阳性病例，首次验证了纯T2模型的有效性。
原文摘要 · Abstract (English)
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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