Affiliations 

  • 1 Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China
  • 2 Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 3 Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
  • 4 Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
  • 5 Philips Healthcare, Shanghai, China
  • 6 Diagnostic Imaging and Radiotherapy Program, Faculty of Health Sciences, The National University of Malaysia, Kuala Lumpur, Malaysia
  • 7 Yingran Medicals Co., Ltd., Hong Kong SAR, China
Quant Imaging Med Surg, 2024 Dec 05;14(12):8064-8082.
PMID: 39698640 DOI: 10.21037/qims-24-1837

Abstract

BACKGROUND: Liver hemangiomas (HGs) are characterized by cavernous venous spaces delineated by a lining of vascular endothelial cells and interspersed with connective tissue septa. Typically, a liver HG has higher apparent diffusion coefficient (ADC) and T2 values than those of hepatocellular carcinomas (HCCs) and liver metastases, and lower ADC and T2 values than those of liver simple cysts. However, a portion of HGs shows ADC and T2 overlapping with those of HCC, liver metastasis, and simple cyst. When MRI is the first line examination for the liver, contrast enhanced imaging is commonly used to confirm the diagnosis of liver HG. Magnetic resonance diffusion-derived vessel density (DDVD) is a physiological surrogate of the area of microvessels per unit tissue area. DDVD is calculated according to: DDVD(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the tissue signal when b is 0 or 2 (s/mm2). Sb2 and ROIarea2 can also be approximated by other low b-values (such as b=10) diffusion-weighted imaging (DWI). In this study, we conducted a preliminary evaluation of magnetic resonance DDVD pixelwise map (DDVDm) for liver HG diagnosis.

METHODS: Three testing datasets were included. All imaging data were acquired at 3.0T. Dataset-1 consisted of 16 HGs (lesion diameter: 1.5-8.85 cm), 4 focal nodular hyperplasia (FNHs, lesion diameter: 1.72-5.7 cm), and 24 HCCs (lesion diameter: 1.83-12.77 cm), and DDVDm was reconstructed with b=0 and b=2 images. Dataset-2 consisted of 6 HGs (lesion diameter: 1.14-6.2 cm), and DDVDm was reconstructed with b=0 and b=10 images. Dataset-3 consisted of 28 HCCs (lesion diameter: 1.91-3.52 cm), and DDVDm was reconstructed with b=0 and b=2 images. For dataset-1 and dataset-2, a trained reader was required to make a diagnosis for a lesion solely based on DDVDm with 4 choices: (I) HG with confidence; (II) HG without confidence; (III) solid mass-forming lesion (MFL) with confidence; (IV) solid MFL without confidence. Then, three readers attempted to confirm whether DDVDm features summarized from dataset-1 and dataset-2 would be generalizable to dataset-3.

RESULTS: For dataset-1 and dataset-2 together, the correct diagnosis was made by the trained reader in 90.9% (20/22) of the HGs (77.7% with confidence) and 96.4% (27/28) of the MFLs (85.7% with confidence). HG generally showed substantially higher DDVD signal relative to background liver parenchyma. Though not necessarily, HG DDVD signals could be similar to those of blood vessels. Some HGs showed DDVD signals higher or similar to that of kidneys which have a higher perfusion than the liver. MFL generally showed DDVD signals only slightly higher, similar to, or even slightly lower, than that of background liver parenchyma. The DDVDm features of dataset-3 were all consistent with MFL.

CONCLUSIONS: When DDVDm is used to evaluate the liver, HG can be diagnosed with confidence in a substantial portion of patients without the need for a contrast enhanced scan. Our result will be relevant for HG confirmation when MRI is the first line examination for the liver.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.