Li Da-Lue, Zhu Ling, Liu Shun-Li, Wang Zhi-Bo, Liu Jing-Nong, Zhou Xiao-Ming, Hu Ji-Lin, Liu Rui-Qing
Department of Emergency, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China.
Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China.
World J Gastrointest Surg. 2025 Jun 27;17(6):106155. doi: 10.4240/wjgs.v17.i6.106155.
Early identification of bowel resection risks is crucial for patients with incarcerated inguinal hernia (IIH). However, the prompt detection of these risks remains a significant challenge. Advancements in radiomic feature extraction and machine learning algorithms have paved the way for innovative diagnostic approaches to assess IIH more effectively.
To devise a sophisticated radiomic-clinical model to evaluate bowel resection risks in IIH patients, thereby enhancing clinical decision-making processes.
This single-center retrospective study analyzed 214 IIH patients randomized into training ( = 161) and test ( = 53) sets (3:1). Radiologists segmented hernia sac-trapped bowel volumes of interest (VOIs) on computed tomography images. Radiomic features extracted from VOIs generated Rad-scores, which were combined with clinical data to construct a nomogram. The nomogram's performance was evaluated against standalone clinical and radiomic models in both cohorts.
A total of 1561 radiomic features were extracted from the VOIs. After dimensionality reduction, 13 radiomic features were used with eight machine learning algorithms to develop the radiomic model. The logistic regression algorithm was ultimately selected for its effectiveness, showing an area under the curve (AUC) of 0.828 [95% confidence interval (CI): 0.753-0.902] in the training set and 0.791 (95%CI: 0.668-0.915) in the test set. The comprehensive nomogram, incorporating clinical indicators showcased strong predictive capabilities for assessing bowel resection risks in IIH patients, with AUCs of 0.864 (95%CI: 0.800-0.929) and 0.800 (95%CI: 0.669-0.931) for the training and test sets, respectively. Decision curve analysis revealed the integrated model's superior performance over standalone clinical and radiomic approaches.
This innovative radiomic-clinical nomogram has proven to be effective in predicting bowel resection risks in IIH patients and has substantially aided clinical decision-making.
对于嵌顿性腹股沟疝（IIH）患者，早期识别肠道切除风险至关重要。然而，及时检测这些风险仍然是一项重大挑战。放射组学特征提取和机器学习算法的进步为更有效地评估IIH的创新诊断方法铺平了道路。
设计一种复杂的放射组学-临床模型，以评估IIH患者的肠道切除风险，从而加强临床决策过程。
这项单中心回顾性研究分析了214例IIH患者，随机分为训练组（n = 161）和测试组（n = 53）（3:1）。放射科医生在计算机断层扫描图像上分割疝囊包裹的感兴趣肠容积（VOIs）。从VOIs中提取的放射组学特征生成Rad分数，将其与临床数据相结合构建列线图。在两个队列中，将列线图的性能与独立的临床和放射组学模型进行比较评估。
从VOIs中总共提取了1561个放射组学特征。降维后，13个放射组学特征与8种机器学习算法一起用于开发放射组学模型。逻辑回归算法最终因其有效性而被选中，在训练组中的曲线下面积（AUC）为0.828 [95%置信区间（CI）：0.753 - 0.902]，在测试组中为0.791（95%CI：0.668 - 0.915）。纳入临床指标的综合列线图在评估IIH患者肠道切除风险方面显示出强大的预测能力，训练组和测试组的AUC分别为0.864（95%CI：0.800 - 0.929）和0.800（95%CI：0.669 - 0.931）。决策曲线分析显示，综合模型的性能优于独立的临床和放射组学方法。
这种创新的放射组学-临床列线图已被证明在预测IIH患者肠道切除风险方面有效，并极大地辅助了临床决策。
