Irish journal of medical science · 2022
A machine learning approach to distinguishing between non-functioning and autonomous cortisol secreting adrenal incidentaloma on magnetic resonance imaging using texture analysis.
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Piskin Ferhat Can, Akkus Gamze, Yucel Sevinc Puren, Unal Ilker, Balli Huseyin Tugsan, Olgun Mehtap Evran, Sert Murat, Tetiker Bekir Tamer, Aikimbaev Kairgeldy
Аннотация
PURPOSE: To investigate the possibility of distinguishing between nonfunctioning adrenal incidentalomas (NFAI) and autonomous cortisol secreting adrenal incidentalomas (ACSAI) with a model created with magnetic resonance imaging (MRI)-based radiomics and clinical features.
METHODS: In this study, 100 adrenal lesions were evaluated. The lesions were segmented on unenhanced T1-weighted in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3Tesla MRIs. The LASSO regression model was used to select potential predictors from 108 texture features for each sequence. Subsequently, a combined radiomics score and clinical features were created and compared.
RESULTS: A significant difference was found between median rad-scores for ACSAI and NFAI in training and test sets (p < 0.05 for all sequences). Multivariate logistic regression analysis revealed that the length of the tumor (OR = 1.09, p = 0.007) was an independent risk factor related to ACSAI. Multivariate logistic regression analysis was used for building clinical-radiomics (combined) models. The Op, IP, and IP plus T2-W model had a higher performance with area under curve (AUC) 0.758, 0.746, and 0.721 on the test dataset, respectively.
CONCLUSION: ACSAI can be distinguished from NFAI with high accuracy on unenhanced MRI. Radiomics analysis and the model constructed by machine learning algorithms seem superior to another radiologic assessment method. The inclusion of chemical shift MRI and the length of the tumor in the radiomics model could increase the power of the test.
METHODS: In this study, 100 adrenal lesions were evaluated. The lesions were segmented on unenhanced T1-weighted in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3Tesla MRIs. The LASSO regression model was used to select potential predictors from 108 texture features for each sequence. Subsequently, a combined radiomics score and clinical features were created and compared.
RESULTS: A significant difference was found between median rad-scores for ACSAI and NFAI in training and test sets (p < 0.05 for all sequences). Multivariate logistic regression analysis revealed that the length of the tumor (OR = 1.09, p = 0.007) was an independent risk factor related to ACSAI. Multivariate logistic regression analysis was used for building clinical-radiomics (combined) models. The Op, IP, and IP plus T2-W model had a higher performance with area under curve (AUC) 0.758, 0.746, and 0.721 on the test dataset, respectively.
CONCLUSION: ACSAI can be distinguished from NFAI with high accuracy on unenhanced MRI. Radiomics analysis and the model constructed by machine learning algorithms seem superior to another radiologic assessment method. The inclusion of chemical shift MRI and the length of the tumor in the radiomics model could increase the power of the test.