Gutierrez-Becker Benjamin, Fraessle Stefan, Yao Heming, Luscher Jerome, Girycki Rafal, Machura Bartosz, Czornik Janusz, Goslinsky Jaroslaw, Pitura Marek, Levitte Steven, Arús-Pous Josep, Fisher Emily, Bojic Daniela, Richmond David, Bigorgne Amelie E, Prunotto Marco
Roche, Pharma Research & Early Development, Data and Analytics, Basel, Switzerland.
Biology Research AI Development (BRAID), Genentech Research and Early Development, San Francisco, CA, USA.
J Crohns Colitis. 2025 Jan 11;19(1). doi: 10.1093/ecco-jcc/jjae187.
Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC.
Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials.
The high agreement between UC-SCALE and central reading at the level of the colon section (𝜅 = 0.80), and the agreement between central and local reading (𝜅 = 0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, ps < 0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction.
Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.
诸如梅奥诊所内镜亚评分（MCES）等经过验证的评分方法在评估溃疡性结肠炎（UC）严重程度时，仅考虑结肠最严重部位的情况，而未考虑疾病范围。我们提出了溃疡性结肠炎严重程度分类及局部范围（UC - SCALE）算法，该算法可对UC的内镜严重程度和疾病范围进行全面且自动化的评估。
溃疡性结肠炎严重程度分类及局部范围由3个主要部分组成：（1）一个质量过滤器，用于从结肠镜检查视频中选择可读图像（帧）；（2）一个评分系统，为每个可读帧赋予一个MCES评分；（3）一个摄像头定位算法，将每个帧分配到结肠内的一个位置。溃疡性结肠炎严重程度分类及局部范围使用来自III期艾托珠单抗临床试验的4326份乙状结肠镜检查视频进行训练和测试。
UC - SCALE与结肠节段层面的中心阅片之间高度一致（κ = 0.80），且中心阅片与局部阅片之间的一致性（κ = 0.84），这表明UC - SCALE与经验丰富的阅片者之间的评分者间一致性相似。此外，UC - SCALE与疾病活动标志物如钙卫蛋白、C反应蛋白以及患者报告的结局、医生整体评估和格博斯组织学评分相关（rs 0.40 - 0.55，p < 0.0001）。最后，通过评估基线和诱导期之间的个体内镜严重程度，证明了使用UC - SCALE的价值。
我们的全自动评分系统能够对UC患者的内镜严重程度进行准确、客观且局部的评估。此外，我们提供评分的拓扑表示作为疾病严重程度的标志物，其与临床指标高度相关。溃疡性结肠炎严重程度分类及局部范围可重现中心阅片结果，并有望在临床试验和日常实践中加强疾病严重程度评估。
