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

Author:

Saalfeld Sylvia12ORCID,Kreher Robert12,Hille Georg12,Niemann Uli3,Hinnerichs Mattes4,Öcal Osman5,Schütte Kerstin67,Zech Christoph J.8,Loewe Christian9,van Delden Otto10,Vandecaveye Vincent11,Verslype Chris12,Gebauer Bernhard13,Sengel Christian14,Bargellini Irene15,Iezzi Roberto1617,Berg Thomas18,Klümpen Heinz J.19,Benckert Julia20,Gasbarrini Antonio21,Amthauer Holger22,Sangro Bruno23,Malfertheiner Peter24,Preim Bernhard12,Ricke Jens5,Seidensticker Max5,Pech Maciej4,Surov Alexey25

Affiliation:

1. Research Campus STIMULATE at the University of Magdeburg Magdeburg Germany

2. Department of Simulation and Graphics University of Magdeburg Magdeburg Germany

3. University Library University of Magdeburg Magdeburg Germany

4. Department of Radiology and Nuclear Medicine OvGU Magdeburg Magdeburg Germany

5. Department of Radiology LMU University Hospital Munich Germany

6. Department of Internal Medicine and Gastroenterology Niels‐Stensen‐Kliniken Marienhospital Osnabrück Germany

7. Klinik für Gastroenterologie, Hepatologie und Endokrinologie Medizinische Hochschule Hannover (MHH) Hannover Germany

8. Department of Radiology and Nuclear Medicine University Hospital Basel, University of Basel Basel Switzerland

9. Section of Cardiovascular and Interventional Radiology, Department of Bioimaging and Image‐Guided Therapy Medical University of Vienna Vienna Austria

10. Department of Radiology and Nuclear Medicine Academic University Medical Centers Amsterdam The Netherlands

11. Department of Radiology University Hospitals Leuven Leuven Belgium

12. Department of Digestive Oncology University Hospitals Leuven Leuven Belgium

13. Department of Radiology Charité – University Medicine Berlin Berlin Germany

14. Department of Radiology Grenoble University Hospital La Tronche France

15. Diagnostic and Interventional Radiology Candiolo Cancer Institute Turin Italy

16. Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Radiologia d'Urgenza e Interventistica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Rome Italy

17. Università Cattolica del Sacro Cuore Rome Italy

18. Klinik und Poliklinik für Gastroenterologie, Sektion Hepatologie Universitätsklinikum Leipzig Leipzig Germany

19. Department of Medical Oncology Amsterdam University Medical Centers Amsterdam The Netherlands

20. Department of Hepatology and Gastroenterology Campus Virchow Klinikum, Charité – Universitätsmedizin Berlin Berlin Germany

21. Fondazione Policlinico Universitario Gemelli IRCCS, Università Cattolica del Sacro Cuore Rome Italy

22. Department of Nuclear Medicine Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Berlin Germany

23. Liver Unit Clínica Universidad de Navarra and CIBEREHD Pamplona Spain

24. Department of Medicine II University Hospital, LMU Munich Munich Germany

25. Department of Radiology, Neuroradiology and Nuclear Medicine Johannes Wesling University Hospital, Ruhr University Bochum Bochum Germany

Abstract

AbstractBackgroundParameters 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.

Funder

Bundesministerium für Bildung und Forschung

Forschungszentrum Dynamische Systeme, Otto-von-Guericke-Universität Magdeburg

Sirtex Medical

Bayer HealthCare

Publisher

Wiley

Subject

Physiology (medical),Orthopedics and Sports Medicine

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