Publicaciones científicas

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

01-oct-2023 | Revista: Journal of Cachexia, Sarcopenia and Muscle

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

CITA DEL ARTÍCULO  J Cachexia Sarcopenia Muscle. 2023 Oct;14(5):2301-2309.  doi: 10.1002/jcsm.13315. Epub 2023 Aug 17

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