Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma

Abstract

Rong Hong,1,2,* Xiaoxia Ping,2,3,* Yuanying Liu,2 Feiwen Feng,2 Su Hu,2,3 Chunhong Hu2,3 1Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, 215100, People’s Republic of China; 2Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, 215006, People’s Republic of China; 3Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, 215006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chunhong Hu; Su Hu, Department of Radiology, The First Affiliated Hospital of Soochow University; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, People’s Republic of China, Email [email protected]; [email protected]: The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model.Methods: In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models’ performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility.Results: Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664– 0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781– 0.906) and 0.836 (95% CI: 0.732– 0.940) in the training and testing cohorts, respectively.Conclusion: The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.Keywords: computed tomography, invasive mucinous adenocarcinoma, radiomics, machine learning

Keywords

• computed tomography
• invasive mucinous adenocarcinoma
• radiomics
• machine learning