Pre-Immunotherapy Contrast-Enhanced CT Texture-Based Classification: A Useful Approach to Non-Small Cell Lung Cancer Immunotherapy Efficacy Prediction

Abstract

Objective: To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment. Methods: Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software. Three different methods, namely, Fisher coefficient, mutual information measure (MI), and minimization of classification error probability combined average correlation coefficients (POE + ACC), were performed to select 10 optimal texture feature sets, respectively. The patients were divided into non-progressive disease (non-PD) and progressive disease (PD) groups. t-test or Mann-Whitney U-test was performed to test the differences in each texture feature set between the above two groups. Each texture feature set was analyzed by principal component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA). The area under the curve (AUC) was used to quantify the predictive accuracy of the above three analysis models for each texture feature set, and the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were also calculated, respectively. Results: Among the three texture feature sets, the texture parameter differences of kurtosis (2.12 ± 3.92 vs. 0.78 ± 1.10, p = 0.047), "S(2,2)SumEntrp" (1.14 ± 0.31 vs. 1.24 ± 0.12, p = 0.036), and "S(1,0)SumEntrp" (1.18 ± 0.27 vs. 1.28 ± 0.11, p = 0.046) between the non-PD and PD group were statistically significant (all p < 0.05). The classification result of texture feature set selected by POE + ACC and analyzed by NDA was identified as the best model (AUC = 0.812, 95% CI: 0.706-0.919) with a sensitivity, specificity, accuracy, PPV, and NPV of 88.2, 76.3, 81.9, 76.9, and 87.9%, respectively. Conclusion: Pre-immunotherapy contrast-enhanced CT-based texture provides a new method for clinical evaluation of the NSCLC immunotherapy efficacy prediction.

Keywords: immunotherapy; non-small cell lung cancer; radiomics; response prediction; texture.

Figure 1

Radiomic features of baseline contrast-enhanced CT: box plot of Kurtosis (A), “S(2,2)SumEntrp” (B), and “S(1,0)SumEntrp” (C). o stands for outlier.

Figure 2

Right lower lobe nodule, NSCLC. (A) Pre-treatment contrast-enhanced; (B) contrast-enhanced CT 6 weeks later after treatment, the efficacy evaluation was partial response (PR); (C) kurtosis; (D) S(1,0) SumEntrp map; (E) S(2,2) SumEntrp map.

Figure 3

Right upper lobe mass, NSCLC. (A) Pre-treatment contrast-enhanced CT; (B) contrast-enhanced CT 8 weeks later after treatment, the efficacy evaluation was progression (PD); (C) kurtosis map; (D) S(1,0) SumEntrp map; (E) S(2,2) SumEntrp map.

Figure 4

ROC curve of the three classification subtypes under each classifier model.