Radiomic Detection of EGFR Mutations in NSCLC

无线电技术 癌症影像学 表皮生长因子受体 医学 接收机工作特性 人工智能 T790米 机器学习 肿瘤科 内科学 计算机科学 吉非替尼 肺癌 癌症
作者
Giovanni Rossi,Emanuele Barabino,Alessandro Fedeli,Gianluca Ficarra,Simona Coco,Alessandro Russo,Vincenzo Adamo,Francesco Buemi,Lodovica Zullo,Mariella Dono,Giuseppa De Luca,Luca Longo,Maria Giovanna Dal Bello,Marco Tagliamento,Angela Alama,Giuseppe Cittadini,Paolo Pronzato,Carlo Genova
出处
期刊:Cancer Research [American Association for Cancer Research]
卷期号:81 (3): 724-731 被引量:56
标识
DOI:10.1158/0008-5472.can-20-0999
摘要

Abstract Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non–small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A “test–retest” approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. Significance: These findings demonstrate that data normalization and “test–retest” methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.
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