A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Issue 12 (12th May 2021)

• Record Type: Journal Article
• Authors: Nojima, Satoshi; Terayama, Kei; Shimoura, Saeko; Hijiki, Sachiko; Nonomura, Norio; Morii, Eiichi; Okuno, Yasushi; Fujita, Kazutoshi
• Is Part Of: Cancer cytopathology. Volume 129:Issue 12(2021)
• Page Start: 984
• Page End: 995
• Publication Date: 2021-05-12
• Subjects: artificial intelligence -- deep learning -- novel diagnostic system -- urine cytology -- urothelial carcinoma; Cancer -- Cytopathology -- Periodicals; Pathology, Cellular -- Periodicals; Cytology -- Technique -- Periodicals; 611.01815
• DOI: 10.1002/cncy.22443
• Languages: English
• ISSNs: 1934-662X

Abstract

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. Abstract : Deep‐learning systems at the authors' institutions achieve excellent accuracy for the detection of urothelial carcinoma cells. Another deep learning system could diagnose the malignant potential of tumors beyond what is possible with classical cytology.