Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198{n}={198} ) multi-parametric prostate MRI exams collected at UCSF from 2016–2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

中文翻译：

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组织病理学的混合监督改善了 MRI 的前列腺癌分类


MRI 的非侵入性前列腺癌分类有可能通过提供临床重大疾病的早期检测来彻底改变患者护理，但迄今为止显示的阳性预测价值有限。为了解决这个问题，我们提出了一种基于图像的深度学习方法，通过对随后接受活检的患者进行 MRI 筛查来预测具有临床意义的前列腺癌，这些患者的结果范围从良性病理到最高级别的肿瘤。具体来说，我们证明，尽管与基于图像的分割的一致性降低了成本，但通过不同的组织病理学基本事实进行的混合监督可以提高分类性能。先前的方法利用来自靶向活检和全前列腺切除术的病理结果作为基本事实，以强有力地监督临床上有意义的癌症的定位，而我们的方法还利用从具有区域定位的非靶向系统活检中提取的弱监督信号来提高整体性能。我们的关键创新是通过分布而不是简单地通过值进行回归，从而能够使用传统上被深度学习策略忽略的额外病理学发现。我们在 2016 年至 2019 年在 UCSF 收集的 973 个（测试 n=198{n}={198} ）多参数前列腺 MRI 检查数据集上评估了我们的模型，然后进行 MRI/超声融合（靶向）活检和系统（非靶向）活检前列腺活检，证明经过组织病理学混合监督训练的深层网络可以超过前列腺 MRI 解释的前列腺成像报告和数据系统 (PI-RADS) 临床标准的性能（71.6% vs 66.7% 平衡准确度和0.724 与 0.716 AUC）。