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Y. Ren



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    MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      MA 14.12 - Detecting Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma Using Radiomics and Random Forest (ID 9772)

      17:00 - 17:05  |  Author(s): Y. Ren

      • Abstract
      • Presentation
      • Slides

      Background:
      We tried a radiomics approach to build a random forest classifier for recognition of epidermal growth factor receptor (EGFR) mutation status in Chinese patients with lung adenocarcinomas using quantitative image features extracted from non-enhanced computed tomography (CT) images

      Method:
      From October 2008 to December 2015, 355 patients diagnosed with lung adenocarcinomas were included in this retrospective study. They all have complete clinical, pathological, and EGFR mutation status information, and their CT images were scanned before any invasive operation. Tumors with ground glass component or diameter smaller than 2 cm were not included. Their pathological phenotypes and EGFR mutation status were gained from surgical resections. Region of tumors on CT images were segmented semi-automatically first then manually modified by experienced clinicians. 440 quantitative image features were extracted from CT images and fall into four groups: first order statistics, shape and size based features, textural features, and wavelet features. Random forest was used to build the classification model which takes all the features into consideration and make an overall probability of mutation based on the vote of decision trees. The random forest classifier was validated using an independent set and its performance was evaluated using area under curve (AUC) values of the receiver operating characteristic

      Result:
      355 patients diagnosed with lung adenocarcinoma were enrolled in this study (170 male, 185 female; 54 smokers, 301 non-smokers). The patients all received surgery based treatment and their tumor stage varied from I to IV. EGFR mutations (mainly 19del and 21L858R) were found in 187/285(65.6%) and 48/70(68.6%) patients in training and validation sets respectively. The random forest model showed an AUC of 0.781 (95% confidence interval: 0.668-0.894, p<0.001) in the validation set. The sensitivity and specificity are 60.4% and 90.9% at best diagnostic decision point. These results were highest among published results of only using images to detect EGFR.

      Conclusion:
      The random forest classifier based on CT images showed potential ability to identify EGFR mutations in patients with lung adenocarcinomas and could be improved in future works.

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