Mr-based radiomics analysis of intra-tumor heterogeneity for early treatment response prediction in locally advanced cervical cancer treated with concurrent chemoradiotherapy.

OBJECTIVE

Accurately predicting the sensitivity to concurrent chemoradiotherapy (CCRT) is crucial for patients with locally advanced cervical cancer (LACC). The objective of this study was to develop an imaging intra-tumoral heterogeneity (IITH) model based on IITH features to predict the early treatment response to CCRT in patients with LACC.

METHODS

The data was collected retrospectively from three medical centers, and a total of 211 patients with LACC who underwent CCRT were included in the study. Radiomics feature characterization was performed on pre-treatment contrast-enhanced T1-weighted imaging (CE-T1WI) at the voxel level to generate IITH feature vectors. These feature vectors were then clustered into one class using the K-means method, resulting in the formation of multiple tumor subregions. Radiomics features were extracted from multiple tumor subregions, filtered, and the best features were selected to build the IITH model.

RESULTS

Center A (n = 160) was designated as the training cohort, while Center B (n = 36) and CC-Tumor-Heterogeneity (CCTH) (n = 15) datasets were utilized as external test cohorts. At the patient cohort level, three distinct radiomics feature vectors of information were incorporated at each voxel. When the tumor was segmented into seven sub-regions, clustering was optimal. Each subregional radiomic feature was extracted, and after filtering, the IITH prediction model was constructed. The AUC of the IITH model was 0.858 for the training cohort and 0.829 for the external test cohort.

CONCLUSIONS

An IITH model was developed from tumor subregions, which demonstrated promising performance in predicting early treatment response to CCRT in LACC.