A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens

作者：Satoshi Nojima, Kei Terayama, Saeko Shimoura, Sachiko Hijiki, Norio Nonomura, Eiichi Morii, Yasushi Okuno, Kazutoshi Fujita · 发表于：Cancer Cytopathology · 年份：2021 · DOI：10.1002/cncy.22443 · 被引用次数：60 · 研究领域：AI in cancer detection、Bladder and Urothelial Cancer Treatments、Cutaneous Melanoma Detection and Management

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that cannot be performed by pathologists has not been fully evaluated. METHODS: The authors initially obtained low-power field cytology images from archived Papanicolaou-stained urinary cytology glass slides from 232 patients. To aid in the development of a diagnosis support system that could identify suspicious atypical cells, the images were divided into high-power field panel image sets for training and testing of the 16-layer Visual Geometry Group convolutional neural network. The DLS was trained using linked information pertaining to whether urothelial carcinoma (UC) in the corresponding histology specimen was invasive or noninvasive, or high-grade or low-grade, followed by an evaluation of whether the DLS could diagnose these characteristics. RESULTS: The DLS achieved excellent performance (eg, an area under the curve [AUC] of 0.9890; F1 score, 0.9002) when trained on high-power field images of malignant and benign cases. The DLS could diagnose whether the lesions were invasive UC (AUC, 0.8628; F1 score, 0.8239) or high-grade UC (AUC, 0.8661; F1 score, 0.8218). Gradient-weighted class activation mapping of these images indicated that the diagnoses were based on the color of tumor cell nuclei. CONCLUSIONS: The DLS could accurately s...