Abstract
Abstract
Objectives
To develop and validate a PET/CT nomogram for preoperative estimation of lymph node (LN) staging in patients with non-small cell lung cancer (NSCLC).
Methods
A total of 263 pathologically confirmed LNs from 124 patients with NCSLC were retrospectively analyzed. Positron-emission tomography/computed tomography (PET/CT) examination was performed before treatment according to the clinical schedule. In the training cohort (N = 185), malignancy-related features, such as SUVmax, short-axis diameter (SAD), and CT radiomics features, were extracted from the regions of LN based on the PET/CT scan. The Minimum-Redundancy Maximum-Relevance (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model were used for feature selection and radiomics score building. The radiomics score (Rad-Score) and SUVmax were incorporated in a PET/CT nomogram using the multivariable logistic regression analysis. The performance of the proposed model was evaluated with discrimination, calibration, and clinical application in an independent testing cohort (N = 78).
Results
The radiomics scores consisting of 14 selected features were significantly associated with LN status for both training cohort with AUC of 0.849 (95% confidence interval (CI), 0.796–0.903) and testing cohort with AUC of 0.828 (95% CI, 0.782–0.919). The PET/CT nomogram incorporating radiomics score and SUVmax showed moderate improvement of the efficiency with AUC of 0.881 (95% CI, 0.834–0.928) in the training cohort and AUC of 0.872 (95% CI, 0.797–0.946) in the testing cohort. The decision curve analysis indicated that the PET/CT nomogram was clinically useful.
Conclusion
The PET/CT nomogram, which incorporates Rad-Score and SUVmax, can improve the diagnostic performance of LN metastasis.
Key Points
• The PET/CT nomogram (Int-Score) based on lymph node (LN) PET/CT images can reliably predict LN status in NSCLC.
• Int-Score is a relatively objective diagnostic method, which can play an auxiliary role in the process of clinicians making treatment decisions.
Funder
Department of Finance of Jilin Province
Scientific and Technological Developing Scheme of Jilin Province
Jilin Province Science and Technology Department Science and Technology Innovation Talents Cultivation Program
Department of Science and Technology of Jilin Province
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,General Medicine
Cited by
22 articles.
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