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

Abstract

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.

Keywords: HCC; body composition; radiomics; sarcopenia.

Figure 1

Pipeline illustrating our workflow. AT, adipose tissue subdivided into intramuscular adipose tissue (IMAT), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT); CC, correlation coefficient; CT, computed tomography; HCC, hepatocellular carcinoma; SIRT, selective internal radiation therapy; SMA, skeletal muscle area.

Figure 2

Labelled ground truth data with skeletal muscle mass (SMA) and adipose tissue (left) and SMA, intramuscular adipose tissue, subcutaneous adipose tissue and visceral adipose tissue (right).

Figure 3

(A, B) Depiction of class versus prediction including true positive, false positive, false negative, true negative, sensitivity and specificity. Positive class (1) indicates that 1‐year survival is positive, that is, the patients live, and negative class (2) indicates that 1‐year survival is negative, that is, the patients are deceased.

Figure 4

Depiction of the receiver operating characteristic (ROC) curve for Subgroup 1, that is, patients who underwent sorafenib monotherapy. AUC, area under the curve.

Figure 5

Depiction of the receiver operating characteristic (ROC) curve for Subgroup 2, that is, patients who underwent selective internal radiation therapy (SIRT) and sorafenib treatment. AUC, area under the curve.