A combined model using pre-treatment CT radiomics and clinicopathological features of non-small cell lung cancer to predict major pathological responses after neoadjuvant chemoimmunotherapy

Abstract

Objective

To investigate the relationship between clinical pathological characteristics, pretreatment CT radiomics, and major pathologic response (MPR) of non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy, and to establish a combined model to predict the major pathologic response of neoadjuvant chemoimmunotherapy.

Methods

A retrospective study of 211 patients with NSCLC who underwent neoadjuvant chemoimmunotherapy and surgical treatment from January 2019 to April 2021 was conducted. The patients were divided into two groups: the MPR group and the non-MPR group. Pre-treatment CT images were segmented using ITK SNAP software to extract radiomics features using Python software. Then a radiomics model, a clinical model, and a combined model were constructed and validated using a receiver operating characteristic (ROC) curve. Finally, Delong's test was used to compare the three models.

Results

The radiomics model achieved an AUC of 0.70 (95 % CI: 0.62-0.78) in the training group and 0.60 (95 % CI: 0.45-0.76) in the validation group. RECIST assessment results were screened from all clinical characteristics as independent factors for MPR with multivariate logistic regression analysis. The AUC of the clinical model for predicting MPR was 0.66 (95 % CI: 0.59-0.73) in the training group and 0.77 (95 % CI: 0.66-0.87) in the validation group. The combined model with combined radiomics and clinicopathological characteristics achieved an AUC was 0.76 (95 % CI: 0.68-0.84) in the training group, and 0.80 (95 % CI: 0.67-0.92) in the validation group. Delong's test showed that the AUC of the combined model was significantly higher than that of the radiomics model alone in both the training group (P = 0.0067) and the validation group (P = 0.0009).The calibration curve showed good agreement between predicted and actual MPR. Clinical decision curve analysis showed that the combined model was superior to radiomics alone.

Conclusions

Radiomics model can predict MPR in NSCLC after neoadjuvant chemoimmunotherapy with similar accuracy to RECIST assessment criteria. The combined model based on pretreatment CT radiomics and clinicopathological features showed better predictive power than independent radiomics model or independent clinicopathological features, suggesting that it may be more useful for guiding personalized neoadjuvant chemoimmunotherapy treatment strategies.