Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies
Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies
复制标题
深度学习用于自动格里森模式分类，以确定前列腺活检的等级组别
DOI：
10.1007/s00428-019-02577-x
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发表时间：
2019-07-01
期刊：
影响因子：
3.5
通讯作者：
Marquering, Henk A.
中科院分区：
文献类型：
--
作者：
Lucas, Marit;Jansen, Ilaria;Marquering, Henk A.
使用格里森模式（GP）对前列腺癌进行组织病理学分级存在较大的观察者间差异，这可能导致患者的治疗效果不佳。随着前列腺活检数字化和全切片图像的引入，计算机辅助分级变得可行。计算机辅助分级有可能改善前列腺癌的组织病理学分级和治疗选择。使用卷积神经网络自动检测 GP 并确定年级组 (GG)。总共，来自 38 名患者的 96 份前列腺活检在像素级上进行了注释。通过重新训练 Inception-v3 卷积神经网络 (CNN) 来自动检测数字化前列腺活检中的 GP 3 和 GP >= 4。随后将CNN的结果转换为GP>=3和GP>=4的概率图，并根据这些概率图获得整个活检的GG。非典型和恶性 (GP >= 3) 区域的区分准确度为 92%，敏感性和特异性分别为 90% 和 93%。 GP>=4与GP的区别
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP >= 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP >= 3 and GP >= 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP >= 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP >= 4 and GP