Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

Bo Peng; Kaiyu Wang; Ran Xu; Congying Guo; Tong Lu; Yongchao Li; Yiqiao Wang; Chenghao Wang; Xiaoyan Chang; Zhiping Shen; Jiaxin Shi; Chengyu Xu; Linyou Zhang

doi:10.3389/fonc.2023.1131816

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

Front Oncol. 2023 May 3:13:1131816. doi: 10.3389/fonc.2023.1131816. eCollection 2023.

Authors

Bo Peng¹, Kaiyu Wang¹, Ran Xu¹, Congying Guo¹, Tong Lu¹, Yongchao Li¹, Yiqiao Wang², Chenghao Wang¹, Xiaoyan Chang¹, Zhiping Shen¹, Jiaxin Shi¹, Chengyu Xu¹, Linyou Zhang¹

Affiliations

¹ Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
² Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Abstract

Objectives: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.

Methods: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.

Results: Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.

Conclusions: Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.

Keywords: LASSO; computed tomography; non-small cell lung cancer; prognosis; radiomics.