Ohara Jun, Maeda Yasuharu, Ogata Noriyuki, Kuroki Takanori, Misawa Masashi, Kudo Shin-Ei, Nemoto Tetsuo, Yamochi Toshiko, Iacucci Marietta
Department of Pathology, Showa University School of Medicine, Tokyo, Japan.
Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
Clin Gastroenterol Hepatol. 2025 Apr;23(5):846-854.e7. doi: 10.1016/j.cgh.2024.06.040. Epub 2024 Jul 25.
In the management of ulcerative colitis (UC), histological remission is increasingly recognized as the ultimate goal. The absence of neutrophil infiltration is crucial for assessing remission. This study aimed to develop an artificial intelligence (AI) system capable of accurately quantifying and localizing neutrophils in UC biopsy specimens to facilitate histological assessment.
Our AI system, which incorporates semantic segmentation and object detection models, was developed to identify neutrophils in hematoxylin and eosin-stained whole slide images. The system assessed the presence and location of neutrophils within either the epithelium or lamina propria and predicted components of the Nancy Histological Index and the PICaSSO Histologic Remission Index. We evaluated the system's performance against that of experienced pathologists and validated its ability to predict future clinical relapse risk in patients with clinically remitted UC. The primary outcome measure was the clinical relapse rate, defined as a partial Mayo score of ≥3.
The model accurately identified neutrophils, achieving a performance of 0.77, 0.81, and 0.79 for precision, recall, and F-score, respectively. The system's histological score predictions showed a positive correlation with the pathologists' diagnoses (Spearman's ρ = 0.68-0.80; P < .05). Among patients who relapsed, the mean number of neutrophils in the rectum was higher than in those who did not relapse. Furthermore, the study highlighted that higher AI-based PICaSSO Histologic Remission Index and Nancy Histological Index scores were associated with hazard ratios increasing from 3.2 to 5.0 for evaluating the risk of UC relapse.
The AI system's precise localization and quantification of neutrophils proved valuable for histological assessment and clinical prognosis stratification.
在溃疡性结肠炎（UC）的管理中，组织学缓解越来越被视为最终目标。中性粒细胞浸润的缺失对于评估缓解至关重要。本研究旨在开发一种人工智能（AI）系统，该系统能够准确量化和定位UC活检标本中的中性粒细胞，以促进组织学评估。
我们的AI系统结合了语义分割和目标检测模型，用于识别苏木精和伊红染色的全切片图像中的中性粒细胞。该系统评估上皮或固有层中中性粒细胞的存在和位置，并预测南希组织学指数和PICaSSO组织学缓解指数的组成部分。我们将该系统的性能与经验丰富的病理学家的性能进行了评估，并验证了其预测临床缓解的UC患者未来临床复发风险的能力。主要结局指标是临床复发率，定义为梅奥部分评分≥3。
该模型准确识别了中性粒细胞，精确率、召回率和F值分别达到0.77、0.81和0.79。该系统的组织学评分预测与病理学家的诊断呈正相关（Spearman's ρ = 0.68 - 0.80；P <.05）。在复发的患者中，直肠中的中性粒细胞平均数高于未复发的患者。此外，该研究强调，基于AI更高的PICaSSO组织学缓解指数和南希组织学指数评分与评估UC复发风险的风险比从3.2增加到5.0相关。
AI系统对中性粒细胞的精确定位和量化被证明对组织学评估和临床预后分层有价值。
