Detection of the gene mutation of epidermal growth factor receptor in lung adenocarcinoma by radiomic features from a small amount of PET data

Author:

Zhang Tianyou1,Liu Zefeng2,Lin Liying3,Han Tao4,Long Fenghua5,Guo Hongyu5,Han Li56

Affiliation:

1. Department of Radiology, Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital

2. Department of Radiology, Tianjin Medical University General Hospital

3. First Central Clinical College, Tianjin Medical University, Tianjin

4. Peking University, Beijing

5. School of Medical Imaging, Tianjin Medical University, Tianjin, China

6. Department of Radiology, University of Michigan, Ann Arbor Michigan, USA

Abstract

ObjectiveThe purpose of this work was to identify the potential mutation of epidermal growth factor receptor in nonsmall cell adenocarcinoma by noninvasive method, and to explore whether the same or better effect can be achieved using a small amount of single-mode PET image data.MethodA total of 115 patients were recruited and the results of their 18F-FDG PET images and gene detection after resection were obtained; 117 original radiation features and 744 wavelet transform features were extracted from PET images. Several methods were used to reduce the dimension of the data, and four classifier models were established to classify it. The above process was repeated to reduce the total amount of data and the area under the receiver operating characteristic curve (AUC) value that changed with the reduction of the data and the stability of the results was recorded.ResultsThe classifier with the best comprehensive performance under this dataset was logistic regression, whose AUC value is 0.843. And similar results can be obtained from only 30 cases of data.ConclusionA similar or better result could be achieved using a small number of single-mode PET images. In addition, significant results could be obtained using only the PET images of 30 patients.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

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