Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients

Sylvia Saalfeld, Robert Kreher, Georg Hille, Uli Niemann, Mattes Hinnerichs, Osman Öcal, Kerstin Schütte, Christoph J. Zech, Christian Loewe, Otto Van Delden, Vincent Vandecaveye, Chris Verslype, Bernhard Gebauer, Christian Sengel, Irene Bargellini, Roberto Iezzi, Thomas Berg, Heinz J. Klümpen, Julia Benckert, Antonio GasbarriniHolger Amthauer, Bruno Sangro, Peter Malfertheiner, Bernhard Preim, Jens Ricke, Max Seidensticker, Maciej Pech, Alexey Surov

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

BackgroundParameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC). MethodsRadiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups. ResultsWe used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134). ConclusionsParameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaJournal of Cachexia, Sarcopenia and Muscle
Volume14
DOI
Stato di pubblicazionePubblicato - 2023

Keywords

  • HCC
  • sarcopenia
  • radiomics
  • body composition

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