Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction

• Published in: Cancer imaging 2019-05, Vol.19 (1), p.22-22, Article 22
• Main Authors: Zhang, Zhen, Jiang, Hanyu, Chen, Jie, Wei, Yi, Cao, Likun, Ye, Zheng, Li, Xin, Ma, Ling, Song, Bin
• Format: Article
• Language: English
• Subjects: Antigens; Biomarkers; Cancer; Cancer recurrence; Carcinoma; Care and treatment; Decision analysis; Deoxyribonucleic acid; Diagnosis; Diagnostic imaging; Diagnostic systems; DNA; Gadoxetic acid-enhanced MRI; Gene expression; Glycoproteins; Hepatitis; Hepatocellular carcinoma; Image enhancement; Liver cancer; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medical prognosis; Nomogram; Nomograms; Patients; Predictions; Qualitative analysis; Radiomics; Recurrence; Regression analysis; Risk analysis; Risk factors; Surgery; Systematic review; Tumors
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-019-0209-5

This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves. In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis. Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery.