Scientific publications
Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients
Sylvia Saalfeld 1 2 , Robert Kreher 1 2 , Georg Hille 1 2 , Uli Niemann 3 , Mattes Hinnerichs 4 , Osman Öcal 5 , Kerstin Schütte 6 7 , Christoph J Zech 8 , Christian Loewe 9 , Otto van Delden 10 , Vincent Vandecaveye 11 , Chris Verslype 12 , Bernhard Gebauer 13 , Christian Sengel 14 , Irene Bargellini 15 , Roberto Iezzi 16 17 , Thomas Berg 18 , Heinz J Klümpen 19 , Julia Benckert 20 , Antonio Gasbarrini 21 , Holger Amthauer 22 , Bruno Sangro 23 , Peter Malfertheiner 24 , Bernhard Preim 1 2 , Jens Ricke 5 , Max Seidensticker 5 , Maciej Pech 4 , Alexey Surov 25
Background: Parameters 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).
Methods: Radiomics 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.
Results: We 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).
Conclusions: Parameters 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
CITATION J Cachexia Sarcopenia Muscle. 2023 Oct;14(5):2301-2309. doi: 10.1002/jcsm.13315. Epub 2023 Aug 17
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