基于核磁共振成像的放射组学预测经皮内窥镜腰椎间盘切除术后复发的 L4-5 椎间盘突出症。

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Antao Lin, Hao Zhang, Yan Wang, Qian Cui, Kai Zhu, Dan Zhou, Shuo Han, Shengwei Meng, Jialuo Han, Lei Li, Chuanli Zhou, Xuexiao Ma
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引用次数: 0

摘要

背景:近年来,放射组学已被证明是诊断和预测疾病的有效工具。现有证据表明,影像学特征在预测腰椎间盘突出症(rLDH)复发方面起着关键作用。因此,本研究旨在利用放射组学评估经皮内镜腰椎间盘切除术(PELD)患者的复发风险,以促进制定更合理的手术和围手术期管理策略:这是一项回顾性病例对照研究,涉及 487 名接受经皮内镜腰椎间盘切除术(PELD)的 L4/5 水平患者。rLDH组和阴性组采用倾向评分匹配法(PSM)进行匹配。通过类内相关系数(ICC)分析、t 检验和 LASSO 分析,从术前腰椎 MRI 图像中提取了共计 1409 个放射学特征。随后,利用 ROC 曲线分析、AUC、特异性、灵敏度、混淆矩阵和 2 次重复 3 倍交叉验证,构建并评估了 6 个预测模型。最后,夏普利加法解释(SHAP)分析为模型提供了直观的解释:经过筛选和匹配,复发组和对照组共纳入了 128 名患者。此外,在提取的放射学特征中,有 18 个特征被选中用于生成 6 个模型,其预测 rLDH 的 AUC 为 0.551-0.859。在这些模型中,SVM、RF 和 XG Boost 表现优异。最后,交叉验证显示它们的准确率分别为 0.674-0.791、0.647-0.729 和 0.674-0.718:基于 MRI 的放射组学可用于预测 rLDH 的风险,通过提取肉眼无法观察到的影像信息,为围手术期治疗提供更全面的指导。同时,未来可通过纳入更多数据和开展多中心研究来提高模型的准确性和可推广性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics based on MRI to predict recurrent L4-5 disc herniation after percutaneous endoscopic lumbar discectomy.

Background: In recent years, radiomics has been shown to be an effective tool for the diagnosis and prediction of diseases. Existing evidence suggests that imaging features play a key role in predicting the recurrence of lumbar disk herniation (rLDH). Thus, this study aimed to evaluate the risk of rLDH in patients undergoing percutaneous endoscopic lumbar discectomy (PELD) using radiomics to facilitate the development of more rational surgical and perioperative management strategies.

Method: This was a retrospective case-control study involving 487 patients who underwent PELD at the L4/5 level. The rLDH and negative groups were matched using propensity score matching (PSM). A total of 1409 radiomic features were extracted from preoperative lumbar MRI images using intraclass correlation coefficient (ICC) analysis, t-test, and LASSO analysis. Afterward, 6 predictive models were constructed and evaluated using ROC curve analysis, AUC, specificity, sensitivity, confusion matrix, and 2 repeated 3-fold cross-validations. Lastly, the Shapley Additive Explanation (SHAP) analysis provided visual explanations for the models.

Results: Following screening and matching, 128 patients were included in both the recurrence and control groups. Moreover, 18 of the extracted radiomic features were selected for generating six models, which achieved an AUC of 0.551-0.859 for predicting rLDH. Among these models, SVM, RF, and XG Boost exhibited superior performances. Finally, cross-validation revealed that their accuracy was 0.674-0.791, 0.647-0.729, and 0.674-0.718.

Conclusion: Radiomics based on MRI can be used to predict the risk of rLDH, offering more comprehensive guidance for perioperative treatment by extracting imaging information that cannot be visualized with the naked eye. Meanwhile, the accuracy and generalizability of the model can be improved in the future by incorporating more data and conducting multicenter studies.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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