(18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma.

(18)F-氟代脱氧葡萄糖正电子发射断层扫描/计算机断层扫描放射组学特征预测肺腺癌患者的表皮生长因子受体突变状态和预后

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作者:Yang Bin, Ji Heng-Shan, Zhou Chang-Sheng, Dong Hao, Ma Lu, Ge Ying-Qian, Zhu Chao-Hui, Tian Jia-He, Zhang Long-Jiang, Zhu Hong, Lu Guang-Ming
BACKGROUND: To investigate whether radiomic features from ((18)F)-fluorodeoxyglucose positron emission tomography/computed tomography [((18)F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent ((18)F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model's performance. RESULTS: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). CONCLUSIONS: Radiomic features based on ((18)F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

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