Best imaging signs identified by radiomics could outperform the model: application to differentiating lung carcinoid tumors from atypical hamartomas

ConclusionsA radiomics signature can distinguish hamartomas from carcinoids with an AUC  = 0.76. Median density <  10 HU and >  60 HU on 3D or 2D-ROIs may be useful in clinical practice to diagnose these tumors with confidence, but 3D is more reproducible.Critical relevance statementRadiomic features help to identify the most discriminating imaging signs using random forest. ‘Median’ attenuation value (Hounsfield units), extracted from 3D-segmentations on contrast-enhanced chest-CTs, could distinguish carcinoids from atypical hamartomas (AUC = 0.85), was reproducible (ICC = 0.97), and generalized to an external dataset.Key points• 3D-‘Median’ was the best feature to differentiate carcinoids from atypical hamartomas (AUC = 0.85).• 3D-‘Median’ feature is reproducible (ICC = 0.97) and was generalized to an external dataset.• Radiomics signature from 3D-segmentations differentiated carcinoids from atypical hamartomas with an AUC = 0.76.• 2D-ROI value reached similar performance to 3D-‘median’ but was less reproducible (ICC = 0.90).Graphical Abstract
Source: Insights into Imaging - Category: Radiology Source Type: research