Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI

Abstract

Background: Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipeline for ONFH based on magnetic resonance imaging (MRI) radiomics.

Methods: In the segmentation model development dataset, T1-weighted MRI of 222 hips from two hospitals were retrospectively collected and randomly split into training (n = 190) and test (n = 32) sets. In the prognosis prediction model development dataset, 206 hips were also retrospectively collected from two hospitals and divided into training set (n = 155) and external test set (n = 51) according to data source. A deep learning model for automatic lesion segmentation was trained with nnU-Net, from which three-dimensional regions of interest were segmented and a total of 107 radiomics features were extracted. After intra-class correlation coefficients screening, feature correlation coefficient screening and Least Absolute Shrinkage and Selection Operator regression feature selection, a machine learning model for ONFH prognosis prediction was trained with Logistic Regression (LR) and Light Gradient Boosting Machine (LightGBM) algorithm.

Results: The segmentation model achieved an average dice similarity coefficient of 0.848 and an average 95% Hausdorff distance of 3.794 in the test set, compared to the manual segmentation results. After feature selection, nine radiomics features were included in the prognosis prediction model. External test showed that the LightGBM model exhibited acceptable predictive performance. The area under the curve (AUC) of the prediction model was 0.851 (95% CI: 0.7268-0.9752), with an accuracy of 0.765, sensitivity of 0.833, and specificity of 0.727. Decision curve analysis showed that the LightGBM model exhibited favorable clinical utility.

Conclusion: This study presents an automated pipeline for predicting femoral head collapse in ONFH with acceptable performance. Further research is necessary to determine the clinical applicability of this radiomics-based approach and to assess its potential to assist in treatment decision-making for ONFH.

Keywords: Deep learning; MRI; Machine learning; Osteonecrosis of the femoral head; Radiomics.

Fig. 1

Study flow chart of the enrolled patients

Fig. 2

Work flow of the study

Fig. 3

Schematic diagram of lesion segmentation. A-C: MRI slices from a random patient in the test set; D-F: nnU-Net model segmentation result; G-I: manual segmentation result

Fig. 4

Receiver operating characteristic curves of prediction models. Performance of the machine learning models on the training set (A) and the external test set (B). AUC: area under the curve

Fig. 5

Decision curve analysis of the LightGBM model. Decision curve analysis showed the LightGBM model could bring clinical benefit when the threshold was set at 0.22 to 0.76 in the training set (A), and at 0.16 to 0.77 in the test set (B)

Fig. 6

Feature importance of the LightGBM model

Fig. 7

Representative images from three patients in the test set. A-C: A patient correctly predicted by the Lightgbm model, who did not experience collapse; D-F: Another patient correctly predicted by the model, who experienced collapse. G-I: A patient incorrectly predicted by the model, who ultimately experienced collapse