AUTHOR=Shen Jing , Du Hai , Wang Yadong , Du Lina , Yang Dong , Wang Lingwei , Zhu Ruiping , Zhang Xiaohui , Wu Jianlin 

TITLE=A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1035307

DOI=10.3389/fonc.2022.1035307

ISSN=2234-943X

ABSTRACT=Objective: To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.
Methods: A total of 107 pulmonary nodules were prospectively recruited, and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all the cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical features model alone to test its diagnostic validity, and ROC curves were plotted to assess diagnostic validity. 
Results: The Nomogram was performed using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) and calibration curve, which showed the best performance AUC=0.982, 0.940-1.000 95%CI compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using decision curve analysis (DCA) using the nomogram for benign and malignant pulmonary nodules, preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.
Conclusion: This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.