Takenaka Kento, Fujii Toshimitsu, Kawamoto Ami, Suzuki Kohei, Shimizu Hiromichi, Maeyashiki Chiaki, Yamaji Osamu, Motobayashi Maiko, Igarashi Akira, Hanazawa Ryoichi, Hibiya Shuji, Nagahori Masakazu, Saito Eiko, Okamoto Ryuichi, Ohtsuka Kazuo, Watanabe Mamoru
Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan.
Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Japan.
Lancet Gastroenterol Hepatol. 2022 Mar;7(3):230-237. doi: 10.1016/S2468-1253(21)00372-1. Epub 2021 Nov 29.
A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video colonoscopy and evaluate its validity in the real-time detection of histological mucosal inflammation.
In this multicentre, cross-sectional study, we prospectively enrolled consecutive patients (≥15 years) with ulcerative colitis who had an indication for colonoscopy at five hospitals in Japan. Patients in clinical remission were randomly assigned (1:2) to study 1 and study 2. Those with clinically active disease were assigned to study 2 only. Study 1 assessed the validity of real-time histological assessment using DNUC and study 2 validated the consistency of endoscopic scoring between DNUC and experts. The primary endpoint for study 1 was comparison of the results judged by DNUC (healing or active) with biopsy specimens evaluated by pathologists. In study 2, the primary endpoint was the ability of DNUC to determine the Ulcerative Colitis Endoscopic Index of Severity score compared with centrally evaluated scoring by inflammatory bowel disease endoscopy experts.
From April 1, 2020, to March 31, 2021, 770 patients (180 in study 1 and 590 in study 2) were enrolled. Using real-time histological evaluation, DNUC was able to evaluate the presence or absence of histological inflammation in 729 (81%) of 900 biopsy specimens. For predicting histological remission, the DNUC had a sensitivity of 97·9% (95% CI 97·0-98·5) and a specificity of 94·6% (91·1-96·9). Moreover, its positive predictive value was 98·6% (97·7-99·2) and negative predictive value was 92·1% (88·7-94·3). The intraclass correlation coefficient between DNUC and experts for endoscopic scoring was 0·927 (95% CI 0·915-0·938).
DNUC provided consistently accurate endoscopic scoring and showed potential for reducing the number of biopsies required. This system is an objective and consistent application for video colonoscopy that has potential for use in various medical situations.
Tokyo Medical and Dental University and Sony.
内镜检查与组织学评估相结合对于溃疡性结肠炎患者的管理至关重要。我们旨在将我们之前的深度神经网络系统（深度神经溃疡性结肠炎[DNUC]）应用于全结肠镜视频，并评估其在实时检测组织学黏膜炎症方面的有效性。
在这项多中心横断面研究中，我们前瞻性纳入了日本五家医院中符合结肠镜检查指征的连续性溃疡性结肠炎患者（≥15岁）。临床缓解的患者被随机分配（1:2）至研究1和研究2。临床活动期患者仅被分配至研究2。研究1评估使用DNUC进行实时组织学评估的有效性，研究2验证DNUC与专家之间内镜评分的一致性。研究1的主要终点是比较DNUC判断的结果（愈合或活动）与病理学家评估的活检标本结果。在研究2中主要终点是与炎症性肠病内镜专家集中评估的评分相比，DNUC确定溃疡性结肠炎内镜严重程度指数评分的能力。
从2020年4月1日至2021年3月31日，共纳入770例患者（研究1中180例，研究2中590例）。使用实时组织学评估，DNUC能够评估900份活检标本中729份（81%）是否存在组织学炎症。对于预测组织学缓解，DNUC的敏感性为97.9%（95%CI 97.0 - 98.5），特异性为94.6%（9示1 - 96.9）。此外，其阳性预测值为98.6%（97.7 - 99.2），阴性预测值为92.1%（88.7 -示4.3）。DNUC与专家内镜评分之间的组内相关系数为0.927（95%CI 0.915 - 0.938）。
DNUC提供了一致准确的内镜评分，并显示出减少所需活检数量的潜力。该系统是一种客观且一致的结肠镜视频应用，具有在各种医疗情况下使用的潜力。
东京医科齿科大学和索尼。
