Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positives), as opposed to the existing models that are trained to discriminate over all patients. We develop a single voxel-level classification model, with a simple percentage threshold to determine positive cases, at levels of lesions, Barzell-zones and patients. Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging. Among varying definition of clinical significance, the proposed strategy, for example, achieved a specificity of 44.1% (with AI assistance) from 36.3% (by radiologists alone), at a controlled sensitivity of 80.0% on the publicly available UCLA data set. This provides measurable clinical values in a range of applications such as reducing unnecessary biopsies, lowering cost in cancer screening and quantifying risk in therapies.

翻译：当前，前列腺癌的磁共振成像诊断主要依赖放射科医生的判读，而现代基于人工智能的方法已被开发用于独立于放射科医生检测具有临床意义的癌症。本研究提出开发深度学习模型，通过分类放射科医生识别的患者或病灶（即放射科医生阳性病例）来提高整体癌症诊断准确性，这与现有模型需对所有患者进行鉴别训练的模式不同。我们构建了单个体素级分类模型，采用简单的百分比阈值在病灶、Barzell分区及患者层面判定阳性病例。基于来自两个临床数据集的实验数据（分别包含UCLA和UCL PROMIS研究中超过800例和500例患者的组织病理学标记磁共振图像），我们证明所提出的策略能够通过增强放射科医生对磁共振图像的判读来提高诊断准确性。在不同临床意义定义下，例如在公开可用的UCLA数据集中，该策略在控制灵敏度为80.0%的条件下，将特异性从放射科医生单独诊断的36.3%提升至44.1%（AI辅助后）。这在一系列应用中提供了可量化的临床价值，例如减少不必要的活检、降低癌症筛查成本以及量化治疗风险。