Bunino Francesca Margherita, Lanza Ezio, Sellaro Gianluca, Levi Riccardo, Zulian Davide, Giudici Simone, Del Fabbro Daniele
Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, MI, Italy.
Department of Emergency and Trauma Surgery, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 35, 20122 Milan, MI, Italy.
J Clin Med. 2025 Sep 5;14(17):6286. doi: 10.3390/jcm14176286.
Small Bowel Obstruction (SBO) accounts for 15% of emergency department (ED) admissions. While conservative management is recommended, surgery becomes necessary when strangulation is suspected. Identifying which patients need surgery remains a challenge, as traditional imaging lacks sufficient sensitivity and specificity. This study aimed to explore radiomic features to identify potential predictors of strangulation. This retrospective study included patients admitted to a tertiary referral hospital ED between 2019 and 2023, diagnosed with Adhesion Small Bowel Obstruction (aSBO) via contrast-enhanced abdominal CT scans. Two patient groups were examined: those who underwent surgery with bowel resection and ischemic changes confirmed histologically (operative management-OM) and those successfully treated with conservative management (CM). All CT scans were reviewed blindly by a general surgeon and an experienced radiologist. Pre-obstructive loop segmentation was performed using 3D Slicer software, with slice-by-slice contouring of intestinal borders on images of suspected strangulated bowel. Radiomic features were extracted, followed by univariate and multivariate regression analysis. A total of 55 patients were included: 27 CM and 28 OM. Significant differences emerged in GLCM (Gray Level Co-occurrence Matrix), GLDM (Gray Level Dependence Matrix), GLRLM (Gray Level Run Length Matrix), and GLSZM (Gray Level Size Zone Matrix), particularly involving entropy and uniformity. These metrics reflect subtle variations in gray levels not visible to the naked eye. Differences in entropy, uniformity, and energy align with imaging and histopathological findings, supporting the development of radiomic models and future AI-based prediction tools.
小肠梗阻（SBO）占急诊科（ED）入院病例的15%。虽然建议采取保守治疗，但当怀疑有绞窄时则需要进行手术。确定哪些患者需要手术仍然是一项挑战，因为传统影像学缺乏足够的敏感性和特异性。本研究旨在探索放射组学特征以识别绞窄的潜在预测指标。这项回顾性研究纳入了2019年至2023年间入住一家三级转诊医院急诊科、经腹部增强CT扫描诊断为粘连性小肠梗阻（aSBO）的患者。研究了两组患者：一组接受了肠切除手术且组织学证实有缺血改变（手术治疗-OM），另一组通过保守治疗（CM）成功治愈。所有CT扫描均由一名普通外科医生和一名经验丰富的放射科医生进行盲法评估。使用3D Slicer软件进行梗阻前肠袢分割，在疑似绞窄肠段的图像上逐片勾勒肠边界。提取放射组学特征，随后进行单变量和多变量回归分析。总共纳入了55例患者：27例CM患者和28例OM患者。在灰度共生矩阵（GLCM）、灰度依赖矩阵（GLDM）、灰度游程长度矩阵（GLRLM）和灰度大小区域矩阵（GLSZM）方面出现了显著差异，特别是涉及熵和均匀性。这些指标反映了肉眼不可见的灰度细微变化。熵、均匀性和能量方面的差异与影像学和组织病理学结果一致，支持放射组学模型和未来基于人工智能的预测工具的开发。
