Enhancement Pattern Mapping for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis

Authors

Newsha Nikzad
David Thomas Fuentes
Millicent Roach
Tasadduk Chowdhury
Matthew Cagley
Mohamed Badawy
Ahmed Elkhesen
Manal Hassan
Khaled M Elsayes
Laura Beretta
Eugene Jon Koay
Prasun Kumar Jalal

Publication Date

1-1-2024

Journal

Journal of Hepatocellular Carcinoma

Abstract

BACKGROUND AND AIMS: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions.

METHODS: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels.

RESULTS: With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73-0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82-0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance.

CONCLUSION: With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%.

Keywords

LI-RADS, MRI, radiomics, artificial intelligence, liver cancer, machine learning

Comments

PMID: 38525156