A predictive model for immunotherapy efficacy in non-small cell lung cancer constructed based on CT image-weighted radiomics score
10.3760/cma.j.cn371439-20240618-00034
- VernacularTitle:基于CT影像加权组学评分构建非小细胞肺癌的免疫治疗疗效预测模型
- Author:
Haiyan LIU
1
;
Chao ZHANG
Author Information
1. 徐州医科大学医学影像学院，徐州 221004
- Keywords:
Carcinoma, non-small-cell lung;
Immunotherapy;
Forecasting;
Radiomics
- From:
Journal of International Oncology
2025;52(4):202-208
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a predictive model for the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) based on CT image-weighted radiomics score.Methods:A total of 185 patients with NSCLC who received immunotherapy in Affiliated Shuyang Hospital of Xuzhou Medical University from January 2021 to December 2023 were selected as the study objects. All patients underwent 3 consecutive cycles of nivolumab (240 mg) treatment, and therapeutic efficacy was evaluated using the Response Evaluation Criteria in Solid Tumors version 1.1. The patients were divided into treatment-effective group and treatment-ineffective group based on therapeutic outcomes. Based on pre-treatment arterial phase CT images, all volume regions of interest were resampled using PyRadiomics tool, and image preprocessing was performed (including methods such as Wavelet transform and Laplacian filtering) . The multi-focal imaging radiomics features of the two groups of patients were extracted, and standardized processing of the extracted features was carried out on the Dr. Wise research platform. At the same time, a CT image-weighted radiomics score model was constructed using an attention-based multi-instance learning (a-MIL) algorithm, the least absolute shrinkage and selection operator (LASSO) regression, and logistic regression, and the CT image-weighted radiomics score of each patient was calculated. The nomogram was plotted using R software (version R4.3.3) and the rms package. The concordance index (C-index) was used to evaluate the concordance of the model. Receiver operator characteristic (ROC) curve was used to evaluate the performance of the nomogram model in predicting immunotherapy efficacy in NSCLC. The calibration curve was used to evaluate the consistency of the predicted probabilities with the actual outcomes, while the decision curve was used to evaluate net benefit of the model across different thresholds.Results:There were statistically significant differences in sex ( χ2=3.86, P=0.049) , pathological type ( χ2=8.41, P=0.015) , smoking history ( χ2=5.70, P=0.017) , and pre-treatment pulmonary metastasis ( χ2=5.88, P=0.015) between the treatment-effective group ( n=66) and treatment-ineffective group ( n=119) . The original multi-focal radiomics features were weighted and summated by a-MIL algorithm at case level, and a total of 342 imaging radiomics features were extracted, 162 features among which had good consistency (intra-class correlation coefficient >0.80) . After variance analysis, 134 features were eliminated, 28 features remained. After dimensionality reduction by LASSO regression, 7 imaging radiomics features were obtained. They were GLCMEnergy_angle45_offset, ShortRunEmphasis_angle90_offset1, maximum gray value, Spiculation, GLCMEnergy_angle45_offset7, Sphericity, and Vessel. Based on the above imaging radiomics features, the weighted radiomics score model was constructed as follows: Radscore=0.624+0.022×GLCMEnergy_angle45_offset-0.227×ShortRunEmphasis_angle90_offset1+0.395×maximum gray value-8.687×Spiculation+0.384×GLCMEnergy_angle45_offset7-0.012×Sphericity-0.284×Vessel. The CT image-weighted radiomics score in the treatment-effective group (0.75±0.10) was significantly higher than that in the treatment-ineffective group (0.43±0.14) , with a statistically significant difference ( t=18.00, P<0.001) . ROC curve analysis showed that the area under the curve (AUC) of CT image-weighted radiomics score for predicting immunotherapy efficacy of NSCLC was 0.96 (95% CI: 0.92-0.98) , and the optimal cutoff value was 0.62. Multivariate analysis showed CT image-weighted radiomics score ≥0.62 ( OR=14.77, 95% CI: 3.25-22.35, P<0.001) , pathological type (squamous cell carcinoma) ( OR=1.74, 95% CI: 1.35-3.52, P=0.035) , smoking history ( OR=4.01, 95% CI: 1.05-15.30, P=0.042) , and pre-treatment pulmonary metastasis ( OR=1.20, 95% CI: 1.01-1.38, P=0.010) were all independent predictors of immunotherapy effectiveness in NSCLC. Based on the above 4 variables, a nomogram model was constructed to predict the immunotherapy efficacy of NSCLC, and the model validation results showed that the C-index was 0.96 (95% CI: 0.93-0.99) . Calibration curve analysis showed good consistency of the predicted probabilities with the actual probabilities, closely aligning with the ideal curve. ROC curve analysis showed that AUC was 0.97 (95% CI: 0.94-0.99) . Decision curve analysis showed that the model had a net benefit within the prediction range of 2% to 100%. Conclusion:The nomogram model based on CT image-weighted radiomics score is effective in predicting immunotherapy efficacy of NSCLC patients.
