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.
Highlights
In recent years, with the development of tumor immunology research, many breakthroughs have been made in tumor immunotherapy
The patients were divided into non-progressive disease 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)
Summary
With the development of tumor immunology research, many breakthroughs have been made in tumor immunotherapy. Solid tumor immunotherapy is currently being widely carried out clinically and has achieved some exciting results, there are still many unresolved problems, such as the lack of effective methods for immunotherapy to find individual tumor-specific targets [5]. Among these problems, how to accurately evaluate the efficacy of immunotherapy at an early stage is still a difficult problem for clinicians when making clinical treatment decisions. The research results of radiomics for the evaluation of efficacy [6] and prognosis [7] have a potentially great value for guiding and optimizing clinical decisions and achieving individualized and precise treatment of lung cancer
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