Awan Ruqayya, Benes Ksenija, Azam Ayesha, Song Tzu-Hsi, Shaban Muhammad, Verrill Clare, Tsang Yee Wah, Snead David, Minhas Fayyaz, Rajpoot Nasir
Department of Computer Science, University of Warwick, Coventry, UK.
The Royal Wolverhampton NHS Trust, Wolverhampton, UK.
Cytometry A. 2021 Jul;99(7):732-742. doi: 10.1002/cyto.a.24313. Epub 2021 Feb 20.
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.
尿液细胞学检查是一种用于检测高级别膀胱癌的测试。在临床实践中，病理学家会在显微镜下手动扫描样本以定位非典型细胞和恶性细胞。他们会评估这些细胞的形态以做出诊断。在尿液细胞学检查中准确识别非典型细胞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不同诊断的重要组成部分。尿液细胞学检查中恶性肿瘤的计算机辅助识别可以辅助临床医生进行治疗管理，并为进一步检查提供建议。在本研究中，我们提出了一种识别非典型细胞和恶性细胞并对其进行分析以自动预测诊断风险的方法。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。基于细胞水平上表现最佳的网络预测，我们使用非典型细胞计数以及非典型细胞和恶性细胞的总数来识别低风险和高风险病例。受试者操作特征（ROC）曲线下面积表明，与仅使用恶性细胞计数相比，非典型细胞和恶性细胞的总数在诊断方面表现更好。我们分别获得了基于恶性细胞计数和非典型细胞与恶性细胞总数的ROC曲线下面积，分别为0.81和0.83。我们的实验还表明，数字风险可能是基于最终组织病理学诊断的更好预测指标。我们还分析了细胞和全玻片图像水平注释的变异性，并探讨了这种变异性背后可能存在的内在原因。
