Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRI

项目摘要/摘要 前列腺癌是美国男性第二致命的癌症。核磁共振越来越多地被用于指导前列腺 每年有500,000人可免于侵入性活组织检查的副作用。然而，微妙的 侵袭性与惰性(非致命性)癌与良性组织在MRI表现上的差异创造了三个 问题：漏掉癌症，假阳性率高，读者之间只有中等程度的一致性- 是个营养学家。选择性地识别侵袭性和惰性癌症是减少癌症的当务之急 死亡，同时将不必要的活组织检查的副作用降至最低。 我们建议开发和使用以病理为基础的(病理)磁共振生物标记物在放射病理的深层。 帮助放射科医生发现和定位侵袭性癌症和惰性癌症的学习方法 前列腺核磁共振检查。此外，我们提出的方法将是第一个定位侵袭性和惰性癌症的方法。 当它们共存时(76%的指标性病变)。我们在我们独特的数据集中进行了四项初步研究 放射学和病理学图像相匹配。首先，我们发现，在对好斗和懒惰的标签上，我们有很高的一致性 自动化方法和两位病理学家之间的癌症。第二，我们开发了病理核磁共振双... 来自MRI特征的征兆与来自病理图像的特征相关。第三，我们使用 辐射病理深度学习模型中的生物标记物用于检测癌症(AUC：0.86)和侵袭性癌症(AUC： 0.85)。第四，我们发现将放射科医生和辐射病理深度学习模型相结合 帮助确定了放射科医生漏掉的14%更具侵袭性的癌症。 三项创新将改善前列腺癌MRI上侵袭性癌症与惰性癌症的定位。 首先，我们将开发一种新的3D配准方法3D RAPSODI，用于3D重建的MRI和病理 图像消除了切片到切片的对应关系，并将病理上的癌症标签映射到 核磁共振检查。其次，我们将利用我们的相关性学习方法来识别病理MRI生物标记物。第三，我们 将使用深度学习模型来帮助放射科医生在MRI上定位侵袭性癌症。 我们的多学科团队处于独特的地位，可以测试：(AIM 1)病理核磁共振生物标记物是否- 在MRI上测量侵袭性和惰性癌症的视觉差异；(目标2)放射病理深度学习- ING模型可以可靠地自动区分侵袭性和惰性前列腺癌 MRI和(AIM 3)放射科医生在深度学习模型的帮助下提高了检测准确性和 读者之间的一致意见比无人协助的放射科医生要好。 影响：我们提出的辐射病理深度学习模型有可能改善前列腺癌的治疗 通过三种方式：1)发现和瞄准目前每年约50,000名男性中遗漏的侵袭性癌症；2) 在没有癌症或无痛癌症的男性中，每年消除多达500,000例不必要的活检；以及3)减少- 增加检测侵袭性癌症所需的活检样本数量(目前为1-2份，目前为12-18份)。

PROJECT SUMMARY/ABSTRACT Prostate cancer is the second deadliest cancer for American men. MRI is increasingly used to guide prostate biopsies and has potential to spare 500,000 men/year from the side effects of invasive biopsies. Yet, subtle differences in MRI appearance of aggressive vs. indolent (non-lethal) cancer vs. benign tissue creates three problems: missed cancers, high rates of false positives, and only moderate inter-reader agreement among ra- diologists. Selective identification of aggressive and indolent cancers is imperative for reducing cancer death while minimizing side effects from unneeded biopsies. We propose to develop and use pathology-based (pathomic) MRI biomarkers in rad-pathomic deep learning methods to assist radiologists in detecting and localizing aggressive vs. indolent cancers on prostate MRI. In addition, our proposed method will be the first to localize aggressive and indolent cancers when they coexist (76% of index lesions). We performed four preliminary studies in our unique dataset of matched radiology and pathology images. First, we found a high agreement in labeling aggressive vs. indolent cancers between the automated method and two pathologists. Second, we developed pathomic MRI bi- omarkers from MRI features that correlate with features derived from pathology images. Third, we used the biomarkers in rad-pathomic deep learning models to detect cancer (AUC: 0.86) and aggressive cancer (AUC: 0.85) on MRI. Fourth, we showed that combining radiologists and the rad-pathomic deep learning models helped identify 14% more aggressive cancers missed by radiologists. Three innovations will improve the localization of aggressive vs. indolent cancers on prostate MRI. First, we will develop 3D RAPSODI, a novel 3D registration method for 3D reconstructed MRI and pathology images to eliminate the need for slice-to-slice correspondences and map cancer labels from pathology onto MRI. Second, we will leverage our correlation learning method to identify pathomic MRI biomarkers. Third, we will use deep learning models to assist radiologists in localizing aggressive cancer on MRI. Our multidisciplinary team is uniquely positioned to test whether: (Aim 1) pathomic MRI biomarkers empha- size the visual differences of aggressive vs. indolent cancers on MRI; (Aim 2) rad-pathomic deep learn- ing models can reliably and automatically distinguish aggressive from indolent prostate cancers on MRI, and (Aim 3) radiologists assisted by deep learning models have increased detection accuracy and inter-reader agreement than unassisted radiologists. Impact: Our proposed rad-pathomic deep learning models have the potential to improve prostate cancer care in three ways: 1) detecting and targeting aggressive cancers that are currently missed in ~50,000 men/year; 2) eliminating up to 500,000 unnecessary biopsies/year in men with no cancer or indolent cancers; and 3) reduc- ing the number of biopsy samples needed to detect aggressive cancers (1-2 vs. 12-18 currently).