Habitat imaging on contrast-enhanced magnetic resonance imaging predicts early response to transarterial chemoembolization in hepatocellular carcinoma

Figure 2 Schematic diagram of the methodological workflow. The workflow is divided into two parts: The first part is preprocessing and feature acquisition. First, through region of interest segmentation, masks are obtained from the original images of hepatocellular carcinoma patients; then, via habitat clustering, subregion clustering is performed using simple linear iterative clustering superpixels to obtain habitats; subsequently, feature extraction and feature selection are conducted in sequence. The second part is model construction and application: A combined model is built based on the intratumoral heterogeneity score, radiomics score, and clinical data; model evaluation is completed using the receiver operating characteristic curve, calibration curve, SHapley Additive exPlanation analysis, and decision curve; finally, the model is applied in clinical practice to distinguish between responders (treatment responders) and non-responders (non-treatment responders). ROI: Region of interest; HCC: Hepatocellular carcinoma; GMM: Gaussian mixture model; BIC: Bayesian information criterion; ROC: Receiver operating characteristic; SHAP: SHapley Additive exPlanation.