Predictive Value of Baseline CT Radiomics for Jianpibushen Prescription Efficacy in Femoral Head Necrosis.

Abstract

Objectives: To explore the predictive value of baseline CT radiomics for the 6-month and 12-month treatment efficacy of the Jianpibushen Prescription in femoral head necrosis (FHN), with the goal of optimizing treatment strategies.

Methods: Retrospectively, ARCO stage 2-4 FHN patients who underwent hip joint CT scans before receiving Jianpibushen Prescription treatment from September 2016 to December 2023 were collected. 315 patients (M/F = 210/105, median age 39.0 years) were included. A total of 1928 radiomics features were extracted, downscaled and filtered. Finally, features were selected to construct the radiomics predictive model of the efficacy at 6 and 12 months.

Results: For predicting the treatment efficacy at 6 months, eight features were selected to build model using Bootstrap Aggregating Decision Tree (Bagging). The model attained an AUC of 0.999 (0.997-1.0) in the training set and 0.736 (0.638-0.834) in the validation set. For predicting the 12-month treatment efficacy, a comparable radiomics model was constructed with Random Forest, with AUCs of 0.995 (0.991-0.999) in the training set and 0.783 (0.676-0.89) in the validation set.

Conclusion: Baseline CT radiomics features can relatively accurately predict the 6-month and 12-month efficacy of Jianpibushen Prescription, thus facilitating individualized and precise clinical treatment.

Advances in knowledge: For the first time, this study established a relatively accurate prediction model for the 6-month and 12-month efficacy of the Jianpibushen Prescription on FHN, based on baseline CT radiomics features, thus optimizing treatment strategies.