Frontiers in Oncology (Aug 2021)

Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy

  • Qiaoyou Weng,
  • Junguo Hui,
  • Hailin Wang,
  • Chuanqiang Lan,
  • Jiansheng Huang,
  • Chun Zhao,
  • Liyun Zheng,
  • Shiji Fang,
  • Minjiang Chen,
  • Chenying Lu,
  • Yuyan Bao,
  • Peipei Pang,
  • Min Xu,
  • Weibo Mao,
  • Zufei Wang,
  • Jianfei Tu,
  • Yuan Huang,
  • Jiansong Ji

DOI
https://doi.org/10.3389/fonc.2021.590937
Journal volume & issue
Vol. 11

Abstract

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ObjectivesTo develop and validate a radiomic feature-based nomogram for preoperative discriminating the epidermal growth factor receptor (EGFR) activating mutation from wild-type EGFR in non-small cell lung cancer (NSCLC) patients.MaterialA group of 301 NSCLC patients were retrospectively reviewed. The EGFR mutation status was determined by ARMS PCR analysis. All patients underwent nonenhanced CT before surgery. Radiomic features were extracted (GE healthcare). The maximum relevance minimum redundancy (mRMR) and LASSO, were used to select features. We incorporated the independent clinical features into the radiomic feature model and formed a joint model (i.e., the radiomic feature-based nomogram). The performance of the joint model was compared with that of the other two models.ResultsIn total, 396 radiomic features were extracted. A radiomic signature model comprising 9 selected features was established for discriminating patients with EGFR-activating mutations from wild-type EGFR. The radiomic score (Radscore) in the two groups was significantly different between patients with wild-type EGFR and EGFR-activating mutations (training cohort: P<0.0001; validation cohort: P=0.0061). Five clinical features were retained and contributed as the clinical feature model. Compared to the radiomic feature model alone, the nomogram incorporating the clinical features and Radscore exhibited improved sensitivity and discrimination for predicting EGFR-activating mutations (sensitivity: training cohort: 0.84, validation cohort: 0.76; AUC: training cohort: 0.81, validation cohort: 0.75). Decision curve analysis demonstrated that the nomogram was clinically useful and surpassed traditional clinical and radiomic features.ConclusionsThe joint model showed favorable performance in the individualized, noninvasive prediction of EGFR-activating mutations in NSCLC patients.

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