A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

Abstract

Background

Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression.

Methods

We categorized 264 patients from retrospective databases of two centers into training ( n = 159) and validation ( n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed.

Results

Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84−0.96) and 0.86 (95% CI 0.76−0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24−0.50], P < 0.001) and validation (HR = 0.42 [0.26−0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes ( H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552).

Conclusions

The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

Key message

• What is already known on this topic: The relationship between sub-regional radiomics and genomic alterations in the context of immunotherapy for lung cancer has not been reported.

• What this study adds: The sub-regional radiomics model (SRRM) showed better performance in predicting immunotherapy response for non-small cell lung carcinoma than conventional radiomics, tumor mutational burden score, and programmed death ligand-1 biomarkers. H3C4 and PAX5 mutations were associated with the immunotherapy-responsive SRRM-low group, while EGFR mutation was significantly associated with SRRM-high group, especially the L858R mutation sub-type. SRRM-low group showed longer progression-free survival than the SRRM-high group in the training and validation cohorts.

• How this study might affect research, practice or policy: This approach is generalizable to any medical imaging analysis, including immunotherapy, offering a novel noninvasive way to tailor cancer treatment.