Radiomics-based prediction of early antipsychotic treatment response for first-episode schizophrenia

This study aims to develop and validate cranial CT-based radiomics models for predicting early antipsychotic response in first-episode schizophrenia (SCZ).

We retrospectively enrolled 530 antipsychotic-naïve first-episode SCZ patients and split them 7:3 into training and independent test sets. Early response was defined as ≥25% Brief Psychiatric Rating Scale (BPRS) reduction at week four. CT images were resampled, registered to MNI space, and parcellated using the Harvard-Oxford atlas. Radiomic features (RFs) were extracted and selected via a multi-stage pipeline (ANOVA, mutual information, LASSO-SVM, and interaction screening). An XGBoost classifier was trained to generate radiomics-only predictions. A radiomics score (Rad-Score) was derived by logistic regression and combined with clinical predictors in a nomogram. Spearman's correlations between RFs and symptoms were tested with FDR control.

Among the RFs of 530 patients (313 responders and 217 non-responders), we retained 11 radiomic features and one interaction term, predominantly from temporal regions and the thalamus. In the test set, AUCs were 0.72 for the clinical-only model and 0.86 for the radiomics-only model; the nomogram integrating Rad-Score with age, duration of untreated psychosis, marital status, and education achieved AUC = 0.92. Sensitivity analyses using ≥30% and ≥40% response thresholds showed consistent discrimination. Rad-Score correlated with symptom improvement (rs = 0.504; q < 0.001).

Baseline cranial CT radiomics, particularly when combined with routine clinical factors, enables accurate early prediction of antipsychotic response and yields interpretable biomarkers linked to symptom change in first-episode schizophrenia.