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.

Keywords: computed tomography; non-small cell lung cancer; pathological response; predictive model; radiomics.

FIGURE 1

Study workflow. The computed tomography (CT) image acquired at baseline and the presurgical timepoint were retrospectively retrieved and segmented for feature extraction. After feature evaluation and modeling, the performance of the models in predicting a pathological response (pCR vs. non‐pCR) was validated. CIT, chemoimmunotherapy; LASSO, least absolute shrinkage and selection operator; LR, logistic regression (a supervised machine learning model for two‐group classification problems); NACIP, neoadjuvant chemoimmunotherapy‐related pathological response prediction model.

FIGURE 2

Patient inclusion flow chart.

FIGURE 3

(a) Feature selection for constructing the radiomics signatures. The blue bars depict the importance of each feature. (b) The prediction performance of model 1, model 2, and neoadjuvant chemoimmunotherapy‐related pathological response prediction model (NACIP) was based on the radiomics features acquired from the baseline (before chemoimmunotherapy), and preoperative (after chemoimmunotherapy) computed tomography and both, respectively.

FIGURE 4

Prognostic ability of pathological complete response (pCR) and predicted pCR. (a) The Kaplan–Meier survival curves of disease‐free survival (DFS) in 91 follow‐up patients. (b) DFS of patients with pCR and patients without pCR. (c) The DFS relative to the predictive pCR status in the validation set.