Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB–III non‐small cell lung cancer patients using radiomic features

Abstract

Background

To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non‐small cell lung cancer (NSCLC).

Methods

Patients with stage IB–III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy‐related pathological response prediction model (NACIP).

Results

This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty‐nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set.

Conclusion

NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.

Abstract

Chemoimmunotherapy‐related pathological response prediction model (NACIP) was based on CT images from patients with stage IB–III NSCLC who received neoadjuvant or conversion CIT. We evaluated the accuracy and reliability of the prediction model (NACIP). The follow‐up of patients indicated robust association of pCR with survival. Moreover, we explored model extension application in prognostic prediction.