Deep learning based digital cell profiles for risk stratification of urine cytology images
Deep learning based digital cell profiles for risk stratification of urine cytology images
复制标题
DOI：
10.1002/cyto.a.24313
复制
发表时间：
2021-02-20
期刊：
影响因子：
3.7
通讯作者：
Rajpoot, Nasir
中科院分区：
文献类型：
--
作者：
Awan, Ruqayya;Benes, Ksenija;Rajpoot, Nasir
尿细胞学检查是一种检测高级别膀胱癌的检查。在临床实践中，病理学家将在显微镜下手动扫描样本以定位非典型和恶性细胞。他们将评估这些细胞的形态来做出诊断。在尿细胞学中准确识别非典型和恶性细胞是一项具有挑战性的任务，并且是识别低风险和高风险恶性肿瘤的不同诊断的重要组成部分。计算机辅助鉴定的恶性肿瘤的尿细胞学可以补充临床医生的治疗管理，并在进行进一步的测试提供建议。在这项研究中，我们提出了一种识别非典型和恶性细胞的方法，然后对其进行分析，以自动预测诊断风险。对于细胞检测和分类，我们采用了两种不同的基于深度学习的方法。基于在细胞水平上表现最好的网络预测，我们使用非典型细胞的计数以及非典型和恶性细胞的总计数来识别低风险和高风险病例。受试者工作特征（ROC）曲线下的面积显示，与仅计数恶性细胞相比，非典型细胞和恶性细胞的总计数在诊断时更好。恶性细胞计数和非典型细胞及恶性细胞总数的ROC曲线下面积分别为0.81和0.83。我们的实验还表明，数字风险可以更好地预测最终的基于组织病理学的诊断。我们还分析了细胞和整个载玻片图像水平上注释的变异性，并探讨了这种变异性背后可能的内在原理。
Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low-risk and high-risk malignancy. Computer-assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning-based approaches. Based on the best performing network predictions at the cell level, we identified low-risk and high-risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology-based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability.