Application value of radiomics-based machine learning model in identifying the degree of pulmonary ground-glass nodule infiltration
- VernacularTitle:基于影像组学的机器学习模型辅助肺磨玻璃结节浸润程度鉴别的应用价值
- Author:
Hua HE
1
;
Delun YANG
2
;
Shuo SUN
1
;
Li HE
1
;
Xiang MA
1
;
Mengmeng ZHAO
2
;
Jiajun DENG
2
;
Minjie MA
3
,
4
,
5
;
Biao HAN
3
,
4
,
5
;
Chang CHEN
4
,
6
,
7
,
8
Author Information
1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730030, P. R. China
2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Shanghai, 200433, P. R. China
3. 3. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, P. R. China
4. 4. The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Lanzhou, 730030, P. R. China
5. 5. Gansu Provincial Quality Control Center of Thoracic Surgery, Lanzhou, 730030, P. R. China
6. 1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730030, P. R. China
7. 2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Shanghai, 200433, P. R. China
8. 3. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, P. R. China
- Publication Type:Journal Article
- Keywords:
Ground-glass nodules;
radiomics;
support vector machine;
invasive degree
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2023;30(04):522-531
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). Methods We retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the mode. Results A total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an ……
