Prospective validation of multimodal risk stratification using clinical and quantitative imaging features for 12-month outcomes after minimally invasive surgery for lumbar disc herniation
5 affiliations
- 1The First Affiliated Hospital of Guangxi Medical University , ,
- 2The People's Hospital of Baise City , ,
- 3The Second Affiliated Hospital of Guangxi Medical University , ,
- 4Spinal Orthopedic Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities , , ,
- 5Liuzhou Railway Central Hospital , ,
Abstract
Background: Lumbar disc herniation (LDH) is a common spinal disorder leading to chronic low back pain and radiating leg pain. Minimally invasive surgery is the preferred treatment, but postoperative recovery varies significantly, making accurate preoperative prognosis prediction a challenge.
Methods: This study developed and prospectively validated a multimodal risk-stratification framework integrating structured clinical variables, conventional imaging parameters, CT/MRI radiomics, and high-dimensional image-derived features. A total of 773 patients were analyzed, including 665 patients in the retrospective development cohort and 108 patients in an independent prospective validation cohort. The retrospective cohort was divided into a training cohort (n = 532) and an internal validation cohort (n = 133). Feature selection, parameter optimization, and classification-threshold determination were performed exclusively within the training cohort, and the finalized framework was subsequently evaluated in the internal and prospective validation cohorts.
Results: The integrated multimodal approach achieved an ROC-AUC of 0.937 (95% CI, 0.900–0.975) and a PR-AUC of 0.849 in the internal validation cohort, and an ROC-AUC of 0.976 (95% CI, 0.951–1.000) and a PR-AUC of 0.941 in the prospective validation cohort. Its discrimination was comparable to that achieved using structured clinical and conventional imaging information alone and was higher than that of isolated quantitative imaging approaches. At the prespecified training-derived classification threshold of 0.318, sensitivity, specificity, and balanced accuracy in the prospective cohort were 85.3%, 95.9%, and 90.6%, respectively. Calibration analysis demonstrated residual probability miscalibration despite strong discrimination, although training-derived logistic recalibration improved the Brier score.
Conclusion: Multimodal integration of clinical and quantitative imaging information demonstrated strong discrimination for 12-month postoperative outcome stratification in both internal and prospective validation cohorts. However, the incremental discriminative value beyond structured clinical and conventional imaging information was limited, and residual miscalibration remained in the prospective cohort. Further multicenter validation, standardized quantitative imaging workflows, and calibration assessment are warranted before clinical translation.
Related
- Patient-reported propagated sensation along meridians during acupuncture for lumbar disc herniation: protocol for an exploratory prospective cohort study with multimodal neurophysiological and immune measurements.
- Development and Prospective Internal-Validation Protocol of the SpinePROTECT Score: A Digital Biopsychosocial Risk Stratification Framework for Spine Surgery.
- Multimodal integration of carotid plaque ultrasound radiomics and dynamic systemic immune-inflammation trajectories for subacute risk stratification after stroke thrombectomy: development and internal validation of an exploratory prognostic model
- Unilateral Biportal Endoscopy Versus Percutaneous Endoscopic Interlaminar Discectomy for Highly Migrated Lumbar Disc Herniation: A Retrospective Comparative Study With Quantitative MRI-Based Decompression Analysis.
- Development and Internal Validation of a Preoperative Risk Stratification Model for Difficult Laparoscopic Cholecystectomy in Acute Cholecystitis
BibTeX
@article{zhu2026prospective,
title = {{Prospective validation of multimodal risk stratification using clinical and quantitative imaging features for 12-month outcomes after minimally invasive surgery for lumbar disc herniation}},
author = {Jichong Zhu and Hao Li and Chunxian Lu and Hengkua Huang and Tianyou Chen and Sitan Feng and Jiarui Chen and Ruifei Bai and Xinli Zhan and Chong Liu and Chengqian Huang},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1909448},
url = {https://europepmc.org/article/PMC/PMC13719450},
note = {CC BY},
}
RIS
TY - JOUR
TI - Prospective validation of multimodal risk stratification using clinical and quantitative imaging features for 12-month outcomes after minimally invasive surgery for lumbar disc herniation
AU - Jichong Zhu
AU - Hao Li
AU - Chunxian Lu
AU - Hengkua Huang
AU - Tianyou Chen
AU - Sitan Feng
AU - Jiarui Chen
AU - Ruifei Bai
AU - Xinli Zhan
AU - Chong Liu
AU - Chengqian Huang
PY - 2026
DA - 2026/10/16
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1909448
UR - https://europepmc.org/article/PMC/PMC13719450
AB - Background: Lumbar disc herniation (LDH) is a common spinal disorder leading to chronic low back pain and radiating leg pain. Minimally invasive surgery is the preferred treatment, but postoperative recovery varies significantly, making accurate preoperative prognosis prediction a challenge. Methods: This study developed and prospectively validated a multimodal risk-stratification framework integrating structured clinical variables, conventional imaging parameters, CT/MRI radiomics, and high-dimensional image-derived features. A total of 773 patients were analyzed, including 665 patients in the retrospective development cohort and 108 patients in an independent prospective validation cohort. The retrospective cohort was divided into a training cohort (n = 532) and an internal validation cohort (n = 133). Feature selection, parameter optimization, and classification-threshold determination were performed exclusively within the training cohort, and the finalized framework was subsequently evaluated in the internal and prospective validation cohorts. Results: The integrated multimodal approach achieved an ROC-AUC of 0.937 (95% CI, 0.900–0.975) and a PR-AUC of 0.849 in the internal validation cohort, and an ROC-AUC of 0.976 (95% CI, 0.951–1.000) and a PR-AUC of 0.941 in the prospective validation cohort. Its discrimination was comparable to that achieved using structured clinical and conventional imaging information alone and was higher than that of isolated quantitative imaging approaches. At the prespecified training-derived classification threshold of 0.318, sensitivity, specificity, and balanced accuracy in the prospective cohort were 85.3%, 95.9%, and 90.6%, respectively. Calibration analysis demonstrated residual probability miscalibration despite strong discrimination, although training-derived logistic recalibration improved the Brier score. Conclusion: Multimodal integration of clinical and quantitative imaging information demonstrated strong discrimination for 12-month postoperative outcome stratification in both internal and prospective validation cohorts. However, the incremental discriminative value beyond structured clinical and conventional imaging information was limited, and residual miscalibration remained in the prospective cohort. Further multicenter validation, standardized quantitative imaging workflows, and calibration assessment are warranted before clinical translation.
SN - 2624-8212
ER -