Application of Feature Extraction Combined with Machine Learning in Pathological Typing
Diagnosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma
HUANG Zhicheng, REN Shiyi, LI Danguang, YE Dingli
Department of Radiology, Jilin Cancer Hospital, Changchun Jilin 130012, China
Abstract：Objective To explore the application value of feature extraction combined with machine learning in the differential
diagnosis of pathological typing of lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SCC). Methods The data of
1013 patients with lung ADC or SCC confirmed by surgery and pathology were retrospective analyzed. According to the pathological
results, patients with lung ADC were divided into group 1 (n=515) and patients with lung SCC were divided into group 2 (n=498).
The gender and age differences of patients between the two groups were compared. The feature extraction software MaZda (Version
4.6) was used to extract the texture feature parameters at the largest layer of the lesion on CT images, and the data was standardized
by Standardization. The Univariate_Logistic, LASSO and MultiVariate_Logistic algorithms were used to reduce the dimensionality
of the data, and the texture features with obvious differences between groups were retained to construct and screen the optimal
machine learning model. The dataset was divided into training and validation groups in a ratio of 7∶3. A total of 6 kinds of machine
learning algorithms were used to process the dataset, and the optimal classifier was selected according to the accuracy, area under
the receiver operating characteristic curve, specificity, and sensitivity of the verification group. Results A total of 306 texture feature
parameters were extracted from the largest layer of lesions, of which 114 image texture features were obvious differences between
feature values. Then 16 optimal radiomics features were retained to construct the prediction model. Logistic regression model had the
highest accuracy in the validation set test and was the optimal classifier. The specific parameters of the model in the training group
were that AUC was 0.826, and its accuracy, specificity, and sensitivity were 75.7%, 72.7% and 78.7% respectively. In the validation
group, the AUC was 0.817 and the accuracy, specificity, and sensitivity were 74.9%, 74.1% and 75.6% respectively.
Conclusion The analytical diagnostic model established by feature extraction combined with machine learning has certain research
value in the prediction of pathological types of lung ADC and SCC.
黄志成，任士义，李丹光，叶钉利. 特征提取联合机器学习在肺腺癌与肺鳞癌病理分型诊断中的应用研究[J]. 中国医疗设备, 2022, 37(8): 42-45.
HUANG Zhicheng, REN Shiyi, LI Danguang, YE Dingli. Application of Feature Extraction Combined with Machine Learning in Pathological Typing
Diagnosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma. China Medical Devices, 2022, 37(8): 42-45.
