A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Abstract

Ran Zhang,1,2 Ranran Zhang,3 Ting Luan,4,5 Biwei Liu,6 Yimei Zhang,6 Yaping Xu,1 Xiaorong Sun,5 Ligang Xing6 1Department of Radiation Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 2Tongji University, Shanghai, People’s Republic of China; 3Department of Medical Imaging, Linyi Cancer Hospital, Linyi, Shandong, People’s Republic of China; 4Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People’s Republic of China; 5Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People’s Republic of China; 6Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People’s Republic of ChinaCorrespondence: Xiaorong SunDepartment of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, Shandong, 250000, People’s Republic of ChinaTel/Fax [phone]Email [email protected] XuDepartment of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Road 507, Shanghai, 200443, People’s Republic of ChinaTel/Fax [phone]Email [email protected]: Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.Methods: A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.Results: The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768– 0.796) in the primary cohort and 0.813 (95% CI, 0.787– 0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.Conclusion: This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.Keywords: solid lung adenocarcinoma, prediction, clinical occult lymph node, computed tomography, radiomics

Keywords

• solid lung adenocarcinoma
• prediction
• clinical occult lymph node
• computed tomography
• radiomics