HE Hua 1 , YANG Delun 2 , SUN Shuo 1 , HE Li 1 , MA Xiang 1 , ZHAO Mengmeng 2 , DENG Jiajun 2 , MA Minjie 3,4,5 , HAN Biao 3,4,5 , CHEN Chang 1,2,3,4
  • 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. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, P. R. China;
  • 4. The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Lanzhou, 730030, P. R. China;
  • 5. Gansu Provincial Quality Control Center of Thoracic Surgery, Lanzhou, 730030, P. R. China;
CHEN Chang, Email: changchenc@tongji.edu.cn
Ground-glass nodules; radiomics; support vector machine; invasive degree
10.7507/1007-4848.202209015
Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables References Cited by

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 model. 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 average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. Conclusion The SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.

Citation： HE Hua, YANG Delun, SUN Shuo, HE Li, MA Xiang, ZHAO Mengmeng, DENG Jiajun, MA Minjie, HAN Biao, CHEN Chang. Application value of radiomics-based machine learning model in identifying the degree of pulmonary ground-glass nodule infiltration. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(4): 522-531. doi: 10.7507/1007-4848.202209015 Copy

  • Previous Article

    Heart sound model based on DenseNet121 architecture for diagnosis of aortic stenosis: A prospective clinical trial

  • Next Article

    Safety and efficacy of hemoperfusion in cardiopulmonary bypass for postoperative inflammatory response in patients with acute Stanford type A aortic dissection: A randomized controlled trial