Multimodal imaging fusion and machine learning model development: differential diagnosis of spinal inflammatory lesions using combined CT hounsfield units and MRI features.
Authors
Affiliations (11)
Affiliations (11)
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, 100029, Beijing, China.
- Thoracic Surgery Center, Shandong Provincial Public Health Clinical Center, Jinan, 250102, China.
- Department of Bone Joint and Surgery II, Shandong Public Health Clinical Center, Jinan, 250102, China.
- Department of Spine Surgery, Laizhou People's Hospital, 261400, Yantai, China.
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China.
- Department of Spine and Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan, 250014, Shandong Province, China.
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250102, China.
- College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, 100029, Beijing, China.
- Department of Orthopedics, Shandong Wendeng Orthopedic Hospital, 264400, Weihai, China.
- Department of Bone Joint and Surgery II, Shandong Public Health Clinical Center, Jinan, 250102, China. [email protected].
- Department of Spine and Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 16369 Jingshi Road, Jinan, 250014, Shandong Province, China. [email protected].
Abstract
The objective is to develop a differential diagnosis model for tuberculous spondylitis (TS) and pyogenic spondylitis (PS) by integrating MRI morphological features and computed tomography (CT) density parameters (Hounsfield Units, HU). This study aims to leverage multimodal data complementarity to achieve fusion of qualitative and quantitative information, thereby providing clinicians with a rapid and objective decision support tool for spinal inflammatory lesion characterization. Imaging data were extracted from MRI and CT scans of patients with TS and PS, then compared and summarized. Receiver operating characteristic (ROC) curves were used to determine optimal HU value thresholds. The least absolute shrinkage and selection operator (Lasso) regression was applied to identify the most predictive features for model construction. A logistic regression-based predictive model was developed and visualized as a nomogram. Model validation was performed using bootstrap resampling, ROC analysis, and decision curve analysis (DCA). A total of 171 patients with TS (n = 91) or PS (n = 80) were included. Statistically significant differences in MRI features were observed between the two groups (P < 0.05). Additionally, significant HU value differences were found in diseased vertebral endplates, small cavitary abscesses, large cavitary abscesses, and intravertebral abscesses between TS and PS patients (P < 0.05). The predictive model incorporated seven independent predictors. Calibration curves, ROC analysis, and DCA all demonstrated excellent model performance. Combined MRI and CT HU value analysis effectively differentiates TS from PS. The predictive model integrating imaging features and quantitative parameters demonstrates high accuracy and clinical utility, offering a novel approach to optimize diagnostic and treatment strategies for spinal infectious diseases.