Author:
Yin Wenda,Wang Wei,Zou Chong,Li Ming,Chen Hao,Meng Fanchen,Dong Guozhang,Wang Jie,Yu Qian,Sun Mengting,Xu Lin,Lv Yang,Wang Xiaoxiao,Yin Rong
Abstract
Pulmonary nodules (PNs) shown as persistent or growing ground-glass opacities (GGOs) are usually lung adenocarcinomas or their preinvasive lesions. Tumor mutation burden (TMB) and somatic mutations are important determinants for the choice of strategy in patients with lung cancer during therapy. A total of 93 post-operative patients with 108 malignant PNs were enrolled for analysis (75 cases in the training cohort and 33 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast computed tomography (CT) images of the entire tumor. Using commercial next generation sequencing, we detected TMB status and somatic mutations of all FFPE samples. Here, 870 quantitative radiomics features were extracted from the segmentations of PNs, and pathological and clinical characteristics were collected from medical records. The LASSO (least absolute shrinkage and selection operator) regression and stepwise logistic regressions were performed to establish the predictive model. For the epidermal growth factor receptor (EGFR) mutation, the AUCs of the clinical model and the integrative model validated by the validation set were 0.6726 (0.4755–0.8697) and 0.7421 (0.5698–0.9144). For the TMB status, the ROCs showed that AUCs of the clinical model and the integrative model validated by the validation set were 0.7808 (0.6231–0.9384) and 0.8462 (0.7132–0.9791). The quantitative radiomics signatures showed potential value in predicting the EGFR mutant and TMB status in GGOs. Moreover, the integrative model provided sufficient information for the selection of therapy and deserves further analysis.
Funder
National Science Foundation of China
Six Talent Project of Jiangsu Province
general project of medical scientific research of Jiangsu Provincial Health Commission
Natural Science Fund for Distinguished Young Scholars of Jiangsu Province
Natural Science Fund of Nanjing University Of Chinese Medicine
Cited by
4 articles.
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