Application value of CT and MRI radiomics based on machine-learning method in diagnosing pancreatic cancer
10.3760/cma.j.cn115667-20230322-00040
- VernacularTitle:基于CT和MRI影像组学的机器学习方法在胰腺癌诊断中的应用价值
- Author:
Qingguo WANG
1
;
Jiang LONG
;
Wei TANG
;
Tao CHEN
;
Chuntao WU
;
Haitao GU
;
Zihao QI
;
Jiuliang YAN
;
Beiyuan HU
;
Yan ZHENG
;
Hanguang DONG
Author Information
1. 上海交通大学医学院附属第一人民医院放射科，上海 200080
- Keywords:
Pancreatic neoplasms;
Tomography, X-ray computed;
Magnetic resonance imaging;
Radiomics
- From:
Chinese Journal of Pancreatology
2023;23(2):128-133
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the application value of CT and MRI imageomics based on machine learning method in the diagnosis of pancreatic cancer.Methods:The clinical data of 62 patients with surgically resected and pathologically confirmed pancreatic cancer, who underwent enhanced CT scan, MRI plain or enhanced scan in Shanghai General Hospital between January 2014 and December 2021 were collected. According to the chronological order of surgery, 49 patients from January 2014 to December 2020 were enrolled in the training set and 13 patients from January 2021 to December 2021 were enrolled in the validation set. 3D-slicer 4.8.1 software was used to draw the region of interest in each layer of CT and MRI images for cancerous and paracancerous tissue segment. Image features were extracted by Python and the optimal feature set from the training set data was obtained by using Lasso regression model. The machine learning decision tree model was constructed. The receiver operating characteristic curve(ROC) curve was drawn, and the area under the curve (AUC) was calculated to evaluate the value of these three kinds of imageomics models in the diagnosis of pancreatic cancer.Results:The 1 767 CT features and 1 674 MRI features were obtained from enhanced CT scan, MRI plain scan and enhanced MRI scan, respectively. For the differential diagnosis model of cancerous tissue and paracancerous tissue, the enhanced CT scan data model obtained the optimal feature set involving 6 features, the MRI plain scan model obtained the optimal feature set involving 16 features, and the enhanced MRI scan model obtained the optimal feature set involving 15 features. The diagnostic model based on enhanced CT scan had an AUC of 0.98 in the training set and 1 in the verification group. The AUC of the MRI plain scan and enhanced MRI scan models in both the training set and the validation set was 1. The specificity and sensitivity of machine learning decision tree model based on the three kinds of imageomics models in the diagnosis of cancerous tissue and paracancerous tissue were 100%. For the differential diagnosis model of splenic artery wrapping, the enhanced CT scan model didn′t obtain the optimal features and had no diagnostic efficacy. The MRI plain scan model and enhanced MRI scan model obtained the optimal feature set involving 5 and 4 features, respectively. The AUC of the MRI plain scan model in the training set and the validation set were 0.862 and 0.750, respectively, with diagnostic sensitivity of 93.8% and 50.0%, and specificity of 78.6% and 100%, respectively. The AUC of the enhanced MRI scan model in the training set and the validation set were 0.950 and 0.861, respectively, with diagnostic sensitivity of 90.0% and 93.6%, and specificity of 100% and 78.6%, respectively.Conclusions:Based on the radiomics of CT enhanced, MRI plain scan and enhanced MRI scan, the machine learning diagnostic model has an accuracy of more than 90% in differentiating pancreatic cancer from paracancerous tissue. For the differentiation of splenic artery wrapping in pancreatic cancer, the diagnostic model based on enhanced MRI scan haS the best diagnostic efficiency.
