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イハラ トシコ
Toshiko Ito-Ihara
猪原 登志子 所属 川崎医科大学 医学部 応用医学 先端医療開発学 職種 教授 |
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| 論文種別 | 原著 |
| 言語種別 | 英語 |
| 査読の有無 | 査読あり |
| 表題 | A Fully Automated Artificial Intelligence System to Assist Pathologists' Diagnosis to Predict Histologically High-grade Urothelial Carcinoma from Digitized Urine Cytology Slides Using Deep Learning. |
| 掲載誌名 | 正式名：European urology oncology 略 称：Eur Urol Oncol ISSNコード：25889311/25889311 |
| 掲載区分 | 国外 |
| 巻・号・頁 | 7(2),pp.258-265 |
| 著者・共著者 | Keisuke Tsuji, Masatomo Kaneko, Yuki Harada, Atsuko Fujihara, Kengo Ueno, Masaya Nakanishi, Eiichi Konishi, Tetsuro Takamatsu, Go Horiguchi, Satoshi Teramukai, Toshiko Ito-Ihara, Osamu Ukimura |
| 発行年月 | 2024/04 |
| 概要 | BACKGROUND:Urine cytology, although a useful screening method for urothelial carcinoma, lacks sensitivity. As an emerging technology, artificial intelligence (AI) improved image analysis accuracy significantly.OBJECTIVE:To develop a fully automated AI system to assist pathologists in the histological prediction of high-grade urothelial carcinoma (HGUC) from digitized urine cytology slides.DESIGN, SETTING, AND PARTICIPANTS:We digitized 535 consecutive urine cytology slides for AI use. Among these slides, 181 were used for AI development, 39 were used as AI test data to identify HGUC by cell-level classification, and 315 were used as AI test data for slide-level classification.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Out of the 315 slides, 171 were collected immediately prior to bladder biopsy or transurethral resection of bladder tumor, and then outcomes were compared with the histological presence of HGUC in the surgical specimen. The primary aim was to compare AI prediction of the histological presence of HGUC with the pathologist's histological diagnosis of HGUC. Secondary aims were to compare the time required for AI evaluation and concordance between the AI's classification and pathologist's cytology diagnosis.RESULTS AND LIMITATIONS:The AI capability for predicting the histological presence of HGUC was 0.78 for the area under the curve. Comparing the AI predictive performance with pathologists' diagnosis, the AI sensitivity of 63% for histological HGUC prediction was superior to a pathologists' cytology sensitivity of 46% (p = 0.0037). On the contrary, there was no significant difference between the AI specificity of 83% and pathologists' specificity of 89% (p = 0.13), and AI accuracy of 74% and pathologists' accuracy of 68% (p = 0.08). The time required for AI evaluation was 139 s. With respect to the concordance between the AI prediction and pathologist's cytology diagnosis, the accuracy was 86%. Agreements with positive and negative findings were 92% an |
| DOI | 10.1016/j.euo.2023.11.009 |
| PMID | 38065702 |