Satoshi Nojima1, Kei Terayama2,3,4,5, Saeko Shimoura3, Sachiko Hijiki1, Norio Nonomura6, Eiichi Morii1, Yasushi Okuno3,5, Kazutoshi Fujita6,7. 1. Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan. 2. Graduate School of Medical Life Science, Yokohama City University, Kanagawa, Japan. 3. Graduate School of Medicine, Kyoto University, Kyoto, Japan. 4. RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. 5. Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology, and Innovation Hub, Kanagawa, Japan. 6. Department of Urology, Osaka University Graduate School of Medicine, Osaka, Japan. 7. Department of Urology, Kindai University Faculty of Medicine, Osaka, Japan.
Abstract
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 screen UC cells and determine the malignant potential of tumors more accurately than classical cytology. The use of a DLS during cytopathology screening could help urologists plan therapeutic strategies, which, in turn, may be beneficial for patients.
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 screen UC cells and determine the malignant potential of tumors more accurately than classical cytology. The use of a DLS during cytopathology screening could help urologists plan therapeutic strategies, which, in turn, may be beneficial for patients.
Authors: Łukasz Białek; Konrad Bilski; Jakub Dobruch; Wojciech Krajewski; Tomasz Szydełko; Piotr Kryst; Sławomir Poletajew Journal: Cancers (Basel) Date: 2022-03-16 Impact factor: 6.639