Preliminary value of CT radiomics in predicting anaplastic lymphoma kinase fusion gene expression in lung adenocarcinoma
10.3760/cma.j.issn.1005-1201.2019.11.007
- VernacularTitle: CT影像组学在预测肺腺癌ALK融合基因表达中的价值初探
- Author:
Lan SONG
1
;
Zhenchen ZHU
1
;
Lei JIANG
2
;
Lun ZHAO
2
;
Qinglin YANG
3
;
Xin SUI
1
;
Huayang DU
1
;
Huanwen WU
4
;
Ji LI
4
;
Xiuli LI
2
;
Wei SONG
1
;
Zhengyu JIN
1
Author Information
1. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
2. Deepwise Artificial Intelligence Lab, Beijing 100080, China
3. Department of Radiology, Yuhuangding Hospital, Yantai 264000, China
4. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
- Publication Type:Journal Article
- Keywords:
Lung neoplasm;
Radiomics;
Tomography, X-ray computed;
Anaplastic lymphoma kinase
- From:
Chinese Journal of Radiology
2019;53(11):963-967
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of quantitative CT radiomics features in predicting the anaplastic lymphoma kinase (ALK) mutation status in lung adenocarcinoma patients.
Methods:This retrospective study reviewed one hundred and ninety-five lung adenocarcinoma patients (including 60 patients with ALK mutation) whose ALK genetic test results were available from Nov 2015 to May 2018 in PUMCH. VOIs were labeled by an automatic pulmonary nodule detection and segmentation algorithm and were later revised and confirmed by two senior radiologists. The PyRadiomics tools were used to resample the labeled regions, followed by image pre-processing (Wavelet filter or Laplacian of Gaussian (LoG) filter) and feature extraction. Normalized features were selected based on their representativeness on Dr. Wise research platform. Multivariate logistic regression was performed to develop prediction models of ALK mutation gene based on different image pre-processing techniques and different radiomics feature types. The results were validated by ten runs of five-fold cross validation. ROC curve analysis and Delong test were used to compare the predictive performance among models.
Results:Fifteen radiomics features with the highest representativeness were selected from the original 1 232 features. The prediction model based on these radiomics features showed good performance (AUC=0.88 in the training set and 0.78 in the validation set) and was not significantly different from the prediction models based on radiomics features of different pre-processing images (AUC=0.76, P=0.1, original CT images; AUC=0.75, P=0.3, Wavelet-filtered images; AUC=0.76, P=0.2, LoG-filtered images). Among the models built with radiomics features of different types, the one based on GLCM feature (a subtype of texture feature) showed the best performance in predicting ALK genetic status (AUC=0.83, accuracy=0.74, sensitivity=0.85 and specificity=0.69). The model based on first-order statistic features had an AUC of 0.80.
Conclusion:Quantitative CT radiomics features have a good potential to anticipate the expression of ALK fused gene in patients with lung adenocarcinoma.
