Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy

Abstract

Introduction: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM.

Materials and methods: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy.

Results: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P _AUROC < .05 and/or P _accuracy < .05). One radiological model performed better than random guessing during CV (P _accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P _AUROC = .048).

Conclusion: Radiomic features can predict postoperative spinal cord function in CSM patients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.

Keywords: cervical spondylotic myelopathy; machine learning; radiomics.

FIGURE 1

Image preprocessing pipeline. The left (right) column represents images collected from a 1.5 T scanner (3 T scanner). After resampling, cropping, and intensity normalization, images were comparable across scanners. Corresponding automatic segmentation of the spinal cord (yellow line) is shown

FIGURE 2

Radiomics analysis pipeline. Radiomic features were extracted from the spinal cord at the MCL of preprocessed images with or without filters. Feature reduction methods combined with binary classifiers resulted in ML models. Models were trained and cross validated on the training dataset and tested on the testing dataset. ML, machine learning; MCL, maximum compression level

FIGURE 3

Heatmaps of AUROC and accuracy through 5‐fold CV. R1 (R2) referred radiological (radiomic) models. (A) AUROC; (B) accuracy. CV, cross‐validation; AUROC, area under the receiver operating characteristic curve

FIGURE 4

Heatmaps of AUROC and accuracy on the testing cohort. R1 (R2) referred radiological (radiomic) models. (A) ROC‐AUC; (B) accuracy. AUROC, area under the receiver operating characteristic curve