A preliminary study of MRI-based radiomics combined with clinical features for Differential Diagnosis of Brucella Spondylitis and Pyogenic Spondylitis
10.3760/cma.j.cn121113-20230130-00033
- VernacularTitle:MRI影像组学特征联合临床特征鉴别布鲁杆菌性脊柱炎和化脓性脊柱炎
- Author:
Yasin PARHAT
1
;
Yimit YASEN
;
Mardan MURADIL
;
Yusufu AIERPATI
;
Tao XU
;
Xiaoyu CAI
;
Weibin SHENG
;
Mamat MARDAN
Author Information
1. 新疆医科大学第一附属医院脊柱外科，乌鲁木齐 830054
- Keywords:
Brucella spondylitis;
Pyogenic spondylitis;
Magnetic resonance imaging;
Radiomics;
Differential Diagnosis
- From:
Chinese Journal of Orthopaedics
2023;43(18):1223-1232
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To elucidate the diagnostic utility of clinical features and radiomics characteristics derived from magnetic resonance imaging T2-weighted fat-suppressed images (T2WI-FS) in differentiating brucellosis spondylitis from pyogenic spondylitis.Methods:Clinical records of 26 patients diagnosed with Brucellosis Spondylitis and 23 with Pyogenic Spondylitis were retrospectively reviewed from Xinjiang Medical University First Affiliated Hospital between January 2019 and December 2021. Confirmatory diagnosis was ascertained through histopathological examination and/or microbial culture. Demographic characteristics, symptoms, clinical manifestations, and hematological tests were collected, followed by a univariate analysis to discern clinically significant risk factors. For the radiomics evaluation, preoperative sagittal T2WI-FS images were utilized. Regions of interest (ROIs) were manually outlined by two adept radiologists. Employing the PyRadiomics toolkit, an extensive array of radiomics features encompassing shape, texture, and gray-level attributes were extracted, yielding a total of 1,500 radiomics parameters. Feature normalization and redundancy elimination were implemented to optimize the predictive efficacy of the model. Discriminatory radiomics features were identified through statistical methods like t-tests or rank-sum tests, followed by refinement via least absolute shrinkage and selection operator (LASSO) regression. An integrative logistic regression model incorporated selected clinical risk factors, radiomics attributes, and a composite radiomics score (Rad-Score). The diagnostic performance of three models clinical risk factors alone, Rad-Score alone, and a synergistic combination were appraised using a confusion matrix and receiver operating characteristic (ROC) analysis.Results:The cohort comprised 49 patients, including 36 males and 13 females, with a mean age of 53.79±13.79 years. C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) emerged as significant clinical risk factors ( P<0.005). A total of seven discriminative radiomics features (logarithm glrlm SRLGLE, exponential glcm Imc1, exponential glcm MCC, exponential gldm SDLGLE, square glcm ClusterShade, squareroot glszm SALGLE and wavelet.HHH glrlm Run Variance) were isolated through LASSO regression. Among these selected features, the square glcmClusterShade feature exhibited the best performance, with an area under the curve (AUC) value of 0.780. It demonstrated a sensitivity of 68.8%, specificity of 94.4%, accuracy of 82.4%, precision of 91.7%, and negative predictive value of 0.773. Furthermore, the logarithm glrlm SRLGLE feature had an AUC of 0.736, sensitivity of 68.8%, specificity of 72.2%, accuracy of 76.5%, precision of 72.2%, and negative predictive value of 0.812. The exponential glcm Imc1 feature had an AUC of 0.736, sensitivity of 50.0%, specificity of 94.4%, accuracy of 73.5%, precision of 88.9%, and negative predictive value of 0.680. Three diagnostic models were constructed: the clinical risk factors model, the radiomics score model, and the integrated model (clinical risk factors+radiomics score), which showed AUC values of 0.801, 0.818, and 0.875, respectively. Notably, the integrated model exhibited superior diagnostic efficacy. Conclusion:The amalgamation of clinical and radiomics variables within a sophisticated, integrated model demonstrates promising efficacy in accurately discriminating between Brucellosis Spondylitis and Pyogenic Spondylitis. This cutting-edge methodology underscores its potential in facilitating nuanced clinical decision-making, precise diagnostic differentiation, and the tailoring of therapeutic regimens.
