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An Ordinal Radiomic Model to Predict the Differentiation Grade of Invasive Non-Mucinous Pulmonary Adenocarcinoma Based on Low-Dose Computed Tomography in Lung Cancer Screening
27 Pages Posted: 15 Jul 2022
More...Abstract
Background: A novel differentiation grading system for invasive non-mucinous pulmonary adenocarcinoma was proposed by the International Association for the Study of Lung Cancer Pathology Committee. This study aimed to construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma and compare its diagnostic performance with quantitative-semantic model and radiologists.
Methods: A total of 682 pulmonary nodules were divided into primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic curve and accuracy.
Findings: The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.898 for Grade 1 vs. Grade 2/Grade 3; AUC, 0.924 for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.820). No significant difference of diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 for Grade 1 vs. Grade 2/Grade 3, P = 0.140; AUC, 0.852, for Grade 1/Grade 2 vs. Grade 3, P = 0.158; accuracy, 0.743, P = 0.056), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all P < 0.05).
Interpretation: The radiomic model of LDCT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.
Funding Information: This work was supported by Sichuan Science and Technology Program (2021YFS0075, 2021YFS0225) and Chengdu Science and Technology Program (2021-YF05-01507-SN).
Declaration of Interests: The authors declare no potential conflicts of interest.
Ethics Approval Statement: This retrospective study was approved by the Institutional Review Board of Sichuan Cancer Hospital, and the requirement for written informed consent was waived.
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