通过 CorrSigNIA 选择性识别和定位惰性和侵袭性前列腺癌：一种 MRI-病理学相关性和深度学习框架。
Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.
机构信息
Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA 94305, USA.
出版信息
Med Image Anal. 2022 Jan;75:102288. doi: 10.1016/j.media.2021.102288. Epub 2021 Nov 6.
Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.
自动化方法可用于检测前列腺癌并在磁共振成像（MRI）上区分惰性和侵袭性病变，有助于早期诊断和治疗计划。现有的前列腺癌检测自动化方法大多依赖于准确性有限的ground truth 标签，忽略了切除组织上观察到的疾病病理特征，并且在混合病变中同时存在侵袭性（Gleason 模式≥4）和惰性（Gleason 模式=3）癌症时，无法选择性地识别。在本文中，我们提出了一种放射病理学融合方法 CorrSigNIA，用于选择性识别和定位 MRI 上的惰性和侵袭性前列腺癌。CorrSigNIA 使用来自根治性前列腺切除术患者的注册 MRI 和全组织病理学图像来获得准确的 ground truth 标签，并学习放射学和病理学图像之间的相关特征。然后，将这些相关特征用于卷积神经网络架构中，以检测和定位前列腺 MRI 上的正常组织、惰性癌和侵袭性癌。CorrSigNIA 在 98 名男性的数据集上进行了训练和验证，其中包括 74 名接受根治性前列腺切除术的男性和 24 名 MRI 正常的男性。CorrSigNIA 在三个独立的测试集中进行了测试，其中包括 55 名接受根治性前列腺切除术的男性、275 名接受靶向活检的男性和 15 名 MRI 正常的男性。CorrSigNIA 在区分有癌症和无癌症的男性方面达到了 80%的准确率，在检测根治性前列腺切除术和活检队列患者的癌症方面的病变水平 ROC-AUC 为 0.81±0.31，在检测根治性前列腺切除术和活检队列患者的临床显著癌症方面的病变水平 ROC-AUC 分别为 0.82±0.31 和 0.86±0.26。CorrSigNIA 在不同的评估指标和队列中均优于其他方法。在临床环境中，CorrSigNIA 可用于前列腺癌检测以及前列腺癌惰性和侵袭性成分的选择性识别，从而通过帮助指导靶向活检、减少不必要的活检以及选择和计划治疗来改善前列腺癌护理。
Zotero 插件