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

Abstract

Newsha Nikzad,1– 3,* David Thomas Fuentes,4,* Millicent Roach,2 Tasadduk Chowdhury,2 Matthew Cagley,2 Mohamed Badawy,4 Ahmed Elkhesen,5 Manal Hassan,6 Khaled Elsayes,7 Laura Beretta,8 Eugene Jon Koay,2 Prasun Kumar Jalal1 1Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA; 2Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 3Department of Internal Medicine, The University of Chicago Medical Center, Chicago, IL, USA; 4Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 5Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA; 6Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 7Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 8Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA*These authors contributed equally to this workCorrespondence: Newsha Nikzad; Prasun Kumar Jalal, Email [email protected]; [email protected] 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%.Plain Language Summary: Current surveillance and diagnostic methods in hepatocellular carcinoma are suboptimal. Enhancement pattern mapping is an imaging technique that quantifies lesion signals and may be useful in diagnostic and surveillance methods. Enhancement pattern mapping describes quantifiable differences between malignant and benign liver tissue on contrast-enhanced MRI. It amplifies lesion signal and distinguishes malignancy in a surveillance population. The novel imaging technique was investigated at single institution and analyzed lesions compared to cirrhotic parenchyma. Future efforts will include further risk stratification across LI-RADS group categories. The results provide evidence that enhancement pattern mapping uses available imaging data to distinguish hepatocellular carcinoma from non-cancerous parenchyma with and without benign lesions on scans six months prior to diagnosis with standard MRI. The technique introduces a prospective modality to improve diagnostic accuracy and early detection with the goal of improving clinical outcomes.Keywords: LI-RADS, MRI, radiomics, artificial intelligence, liver cancer, machine learning

Keywords

• li-rads
• mri
• radiomics
• artificial intelligence
• liver cancer
• machine learning