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コンドウ シュンスケ
KONDOU Shunsuke
近藤 俊輔 所属 医学部 医学科（東京女子医科大学病院） 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 noninvasive urinary microRNA-based assay for the detection of pancreatic cancer from early to late stages: a case control study. |
| 掲載誌名 | 正式名：EClinicalMedicine 略 称：EClinicalMedicine ISSNコード：25895370/25895370 |
| 掲載区分 | 国外 |
| 巻・号・頁 | 78,pp.102936 |
| 著者・共著者 | Baba Shogo, Kawasaki Tadatoshi, Hirano Satoshi, Nakamura Toru, Asano Toshimichi, Okazaki Ryo, Yoshida Koji, Kawase Tomoya, Kurahara Hiroshi, Oi Hideyuki, Yokoyama Masaya, Kita Junji, Imura Johji, Kinoshita Kazuya, Kondo Shunsuke, Okada Mao, Satake Tomoyuki, Igawa Yukiko Shimoda, Yoshida Tatsuya, Yamaguchi Hiroki, Ando Yoriko, Mizunuma Mika, Ichikawa Yuki, Hida Kyoko, Nishihara Hiroshi, Kato Yasutaka |
| 発行年月 | 2024/12 |
| 概要 | BACKGROUND:Pancreatic cancer is highly aggressive and has a low survival rate primarily due to late-stage diagnosis and the lack of effective early detection methods. We introduce here a novel, noninvasive urinary extracellular vesicle miRNA-based assay for the detection of pancreatic cancer from early to late stages.METHODS:From September 2019 to July 2023, Urine samples were collected from patients with pancreatic cancer (n = 153) from five distinct sites (Hokuto Hospital, Kawasaki Medical School Hospital, National Cancer Center Hospital, Kagoshima University Hospital, and Kumagaya General Hospital) and non-cancer participants (n = 309) from two separate sites (Hokuto Hospital and Omiya City Clinic). The main inclusion criteria included a diagnosis of pancreatic cancer based on pathological or imaging examination, while multiple primary cancers were excluded. Extracellular vesicles were enriched using a polymer-based precipitation method, and miRNAs were comprehensively analyzed by small RNA sequencing. A machine learning model for pancreatic cancer detection was developed using a training dataset (n = 315) consisting of 99 pancreatic cancer participants (of which 33 were early-stage [I/IIA]) and 216 non-cancer participants, and validated with a test dataset (n = 147) consisting of 54 pancreatic cancer participants (of which 9 were early-stage [I/IIA]) and 93 non-cancer participants.FINDINGS:This method showed consistent performance, with areas under the receiver operating characteristic curves of 0.972 (95% confidence interval [CI], 0.928-0.996) and 0.963 (95% CI, 0.932-0.988) in the training and test sets, respectively. The sensitivities for pancreatic cancer detection were 93.9% (95% CI, 87.5%-97.3%) and 77.8% (95% CI, 64.9%-87.3%) overall and 97.0% (95% CI, 83.9%-99.8%) and 77.8% (95% CI, 44.2%-95.9%) for stage I/IIA pancreatic cancer, respectively. The specificities were 91.7% (95% CI, 87.1%-94.7%) and 95.7% (95% CI, 89.4%-98.5%), respectively. We also evalua |
| DOI | 10.1016/j.eclinm.2024.102936 |
| PMID | 39764541 |