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Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism.
Mansour, Nabeel; Mittermeier, Andreas; Walter, Roman; Schachtner, Balthasar; Rudolph, Jan; Erber, Bernd; Schmidt, Vanessa F; Heinrich, Daniel; Bruedgam, Denise; Tschaidse, Lea; Nowotny, Hanna; Bidlingmaier, Martin; Kunz, Sonja L; Adolf, Christian; Ricke, Jens; Reincke, Martin; Reisch, Nicole; Wildgruber, Moritz; Ingrisch, Michael.
Afiliação
  • Mansour N; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Mittermeier A; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Walter R; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Schachtner B; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Rudolph J; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Erber B; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Schmidt VF; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Heinrich D; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Bruedgam D; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Tschaidse L; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Nowotny H; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Bidlingmaier M; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Kunz SL; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Adolf C; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Ricke J; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Reincke M; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Reisch N; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany.
  • Wildgruber M; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Ingrisch M; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany.
Front Endocrinol (Lausanne) ; 14: 1244342, 2023.
Article em En | MEDLINE | ID: mdl-37693351
ABSTRACT

Objectives:

The aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).

Methods:

269 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.

Results:

Radiomics 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.

Conclusion:

Integration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aldosterona / Hiperaldosteronismo Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aldosterona / Hiperaldosteronismo Idioma: En Ano de publicação: 2023 Tipo de documento: Article