Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-weighted (T2w) MR sequences. Early machine learning attempts using only T2w images have shown promising diagnostic performance in segmenting radiologist-annotated lesions. Such uni-modal T2-only approaches deliver substantial clinical benefits by reducing costs and expertise required to acquire other sequences. This work investigates an arguably more challenging application using only T2w at inference, but to localize individual cancers based on independent histopathology labels. We formulate DWI images as a latent modality (readily available during training) to classify cancer presence at local Barzell zones, given only T2w images as input. In the resulting expectation-maximization algorithm, a latent modality generator (implemented using a flow matching-based generative model) approximates the latent DWI image posterior distribution in the E-steps, while in M-steps a cancer localizer is simultaneously optimized with the generative model to maximize the expected likelihood of cancer presence. The proposed approach provides a novel theoretical framework for learning from a privileged DWI modality, yielding superior cancer localization performance compared to approaches that lack training DWI images or existing frameworks for privileged learning and incomplete modalities. The proposed T2-only methods perform competitively or better than baseline methods using multiple input sequences (e.g., improving the patient-level F1 score by 14.4\% and zone-level QWK by 5.3\% over the T2w+DWI baseline). We present quantitative evaluations using internal and external datasets from 4,133 prostate cancer patients with histopathology-verified labels.

翻译：多参数MRI日益被推荐为检测和定位前列腺癌的一线无创方法，其至少需要弥散加权（DWI）和T2加权（T2w）MR序列。早期仅使用T2w图像的机器学习尝试在分割放射科医生标注的病变方面展现出有前景的诊断性能。这种仅基于T2w的单模态方法通过降低获取其他序列所需的成本和专业知识，带来了显著的临床效益。本研究探讨了一个更具挑战性的应用场景——在推理阶段仅使用T2w图像，但基于独立组织病理学标签定位个体癌症病灶。我们将DWI图像构建为潜在模态（在训练期间随时可用），从而在仅输入T2w图像的情况下，对局部Barzell区域的癌症存在性进行分类。在由此产生的期望最大化算法中，潜在模态生成器（基于流匹配生成模型实现）在E步中近似潜在DWI图像的后验分布，而在M步中，癌症定位器与生成模型同步优化，以最大化癌症存在的期望似然。所提出的方法为从特权DWI模态中学习提供了新颖的理论框架，与缺乏训练DWI图像的方法或现有特权学习及不完整模态框架相比，实现了更优的癌症定位性能。所提出的仅T2方法在性能上与使用多个输入序列的基线方法相当或更优（例如，相较于T2w+DWI基线，患者级别F1分数提升14.4%，区域级别QWK提升5.3%）。我们利用来自4,133名具有组织病理学验证标签的前列腺癌患者的内部及外部数据集进行了定量评估。