Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism

Frontiers in Endocrinology

Endocrinology

Denise Bruedgam¹  Lea Tschaidse¹  Christian Adolf¹  Nicole Reisch¹  Daniel Heinrich¹  Martin Bidlingmaier¹  Sonja L. Kunz¹  Hanna Nowotny¹  Martin Reincke¹  Roman Walter²  Andreas Mittermeier²  Vanessa F. Schmidt²  Michael Ingrisch²  Jan Rudolph²  Bernd Erber²  Moritz Wildgruber²  Nabeel Mansour²  Balthasar Schachtner²  Jens Ricke² 

[1] Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany;Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany;

关键词: machine learning;    hyperaldosteronism;    adrenal venous sampling;    integrated diagnostics;    venous interventions;   

DOI  :  10.3389/fendo.2023.1244342

 received in 2023-06-22, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectivesThe aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).Methods269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features.ResultsRadiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features.ConclusionIntegration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA.

【 授权许可】

Unknown   
Copyright © 2023 Mansour, Mittermeier, Walter, Schachtner, Rudolph, Erber, Schmidt, Heinrich, Bruedgam, Tschaidse, Nowotny, Bidlingmaier, Kunz, Adolf, Ricke, Reincke, Reisch, Wildgruber and Ingrisch