Deep-Learning-Based Radiomics to Predict Surgical Risk Factors for Lumbar Disc Herniation in Young Patients: A Multicenter Study.

IF 2.7 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Journal of Multidisciplinary Healthcare Pub Date : 2024-12-07 eCollection Date: 2024-01-01 DOI:10.2147/JMDH.S493302
Zheng Fan, Tong Wu, Yang Wang, Zhuoru Jin, Tong Wang, Da Liu
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引用次数: 0

Abstract

Objective: The aim of this study is to develop and validate a deep-learning radiomics model for predicting surgical risk factors for lumbar disc herniation (LDH) in young patients to assist clinicians in identifying surgical candidates, alleviating symptoms, and improving prognosis.

Methods: A retrospective analysis of patients from two medical centers was conducted. From sagittal and axial MR images, the regions of interest were handcrafted to extract radiomics features. Various machine-learning algorithms were employed and combined with clinical features, resulting in the development of a deep-learning radiomics nomogram (DLRN) to predict surgical risk factors for LDH in young adults. The efficacy of the different models and the clinical benefits of the model were compared.

Results: We derived six sets of features, including clinical features, radiomics features (Rad_SAG and Rad_AXI) and deep learning features (DL_SAG and DL_AXI) from sagittal and axial MR images, as well as fused deep-learning radiomics (DLR) features. The support vector machine(SVM) algorithm exhibited the best performance. The area under the curve (AUC) of DLR in the training and testing cohorts of 0.991 and 0.939, respectively, were significantly better than those of the models developed with radiomics(Rad_SAG=0.914 and 0.863, Rad_AXI=0.927 and 0.85) and deep-learning features(DL_SAG=0.959 and 0.818, DL_AXI=0.960 and 0.811). The AUC of DLRN coupled with clinical features(ODI, Pfirrmann grade, SLRT, MMFI, and MSU classification) were 0.994 and 0.941 in the training and testing cohorts, respectively. Analysis of the calibration and decision curves demonstrated good agreement between the predicted and observed outcomes, and the use of the DLRN to predict the need for surgical treatment of LDH demonstrated significant clinical benefits.

Conclusion: The DLRN established based on clinical and DLR features effectively predicts surgical risk factors for LDH in young adults, offering valuable insights for diagnosis and treatment.

基于深度学习的放射组学预测年轻患者腰椎间盘突出的手术危险因素:一项多中心研究。
目的:本研究的目的是开发和验证一个深度学习放射组学模型,用于预测年轻患者腰椎间盘突出症(LDH)的手术危险因素,以帮助临床医生确定手术候选人,减轻症状,改善预后。方法:对两个医疗中心的患者进行回顾性分析。从矢状和轴向mri图像中,手工制作感兴趣的区域以提取放射组学特征。采用了各种机器学习算法并结合临床特征,从而开发了深度学习放射组学(DLRN)来预测年轻人LDH的手术危险因素。比较不同模型的疗效及模型的临床获益。结果:我们从矢状面和轴向面MR图像中提取了6组特征,包括临床特征、放射组学特征(Rad_SAG和Rad_AXI)和深度学习特征(DL_SAG和DL_AXI),以及融合了深度学习放射组学(DLR)特征。支持向量机(SVM)算法表现出最好的性能。训练和测试队列DLR的曲线下面积(AUC)分别为0.991和0.939,显著优于放射组学(Rad_SAG=0.914和0.863,Rad_AXI=0.927和0.85)和深度学习特征(DL_SAG=0.959和0.818,DL_AXI=0.960和0.811)模型。训练组和测试组DLRN与临床特征(ODI、Pfirrmann分级、SLRT、MMFI和MSU分类)的AUC分别为0.994和0.941。校准曲线和决策曲线的分析表明,预测结果和观察结果之间具有良好的一致性,使用DLRN预测LDH手术治疗的需要显示出显著的临床益处。结论:基于临床和DLR特征建立的DLRN能有效预测青壮年LDH的手术危险因素,为诊断和治疗提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Multidisciplinary Healthcare
Journal of Multidisciplinary Healthcare Nursing-General Nursing
CiteScore
4.60
自引率
3.00%
发文量
287
审稿时长
16 weeks
期刊介绍: The Journal of Multidisciplinary Healthcare (JMDH) aims to represent and publish research in healthcare areas delivered by practitioners of different disciplines. This includes studies and reviews conducted by multidisciplinary teams as well as research which evaluates or reports the results or conduct of such teams or healthcare processes in general. The journal covers a very wide range of areas and we welcome submissions from practitioners at all levels and from all over the world. Good healthcare is not bounded by person, place or time and the journal aims to reflect this. The JMDH is published as an open-access journal to allow this wide range of practical, patient relevant research to be immediately available to practitioners who can access and use it immediately upon publication.
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