Sakamoto K, Okabayashi K, Seishima R, Shigeta K, Kiyohara H, Mikami Y, Kanai T, Kitagawa Y
Department of Surgery, Keio University School of Medicine, 35 Shinano-Machi Shinjuku-Ku, Tokyo, 1608582, Japan.
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
Tech Coloproctol. 2025 May 10;29(1):113. doi: 10.1007/s10151-025-03139-x.
The surgeries in drug-resistant ulcerative colitis are determined by complex factors. This study evaluated the predictive performance of radiomics analysis on the basis of whether patients with ulcerative colitis in hospital were in the surgical or medical treatment group by discharge from hospital.
This single-center retrospective cohort study used CT at admission of patients with US admitted from 2015 to 2022. The target of prediction was whether the patient would undergo surgery by the time of discharge. Radiomics features were extracted using the rectal wall at the level of the tailbone tip of the CT as the region of interest. CT data were randomly classified into a training cohort and a validation cohort, and LASSO regression was performed using the training cohort to create a formula for calculating the radiomics score.
A total of 147 patients were selected, and data from 184 CT scans were collected. Data from 157 CT scans matched the selection criteria and were included. Five features were used for the radiomics score. Univariate logistic regression analysis of clinical information detected a significant influence of severity (p < 0.001), number of drugs used until surgery (p < 0.001), Lichtiger score (p = 0.024), and hemoglobin (p = 0.010). Using a nomogram combining these items, we found that the discriminatory power in the surgery and medical treatment groups was AUC 0.822 (95% confidence interval (CI) 0.841-0.951) for the training cohort and AUC 0.868 (95% CI 0.729-1.000) for the validation cohort, indicating a good ability to discriminate the outcomes.
Radiomics analysis of CT images of patients with US at the time of admission, combined with clinical data, showed high predictive ability regarding a treatment strategy of surgery or medical treatment.
耐药性溃疡性结肠炎的手术治疗由复杂因素决定。本研究基于溃疡性结肠炎住院患者出院时是在手术治疗组还是药物治疗组，评估了影像组学分析的预测性能。
这项单中心回顾性队列研究使用了2015年至2022年收治的溃疡性结肠炎患者入院时的CT。预测目标是患者出院时是否会接受手术。以CT尾骨尖水平的直肠壁作为感兴趣区域提取影像组学特征。CT数据被随机分为训练队列和验证队列，并使用训练队列进行LASSO回归以创建计算影像组学评分的公式。
共选取147例患者，收集到184次CT扫描的数据。157次CT扫描的数据符合入选标准并被纳入。5个特征用于影像组学评分。对临床信息进行单因素逻辑回归分析发现，疾病严重程度（p < 0.001）、手术前使用药物的数量（p < 0.001）、Lichtiger评分（p = 0.024）和血红蛋白（p = 0.010）有显著影响。使用结合这些项目的列线图，我们发现训练队列中手术组和药物治疗组的鉴别能力为AUC 0.822（95%置信区间（CI）0.841 - 0.951），验证队列中为AUC 0.868（95% CI 0.729 - 1.000），表明具有良好的结果鉴别能力。
入院时对溃疡性结肠炎患者的CT图像进行影像组学分析，并结合临床数据，在手术或药物治疗策略方面显示出较高的预测能力。
