Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging–based habitat imaging

• Published in: European radiology 2024-05, Vol.34 (5), p.3215-3225
• Main Authors: Zhang, Yunfei, Chen, Jiejun, Yang, Chun, Dai, Yongming, Zeng, Mengsu
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Aged; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - pathology; Diagnostic Radiology; Diagnostic systems; Diffusion coefficient; Diffusion Magnetic Resonance Imaging - methods; Female; Habitats; Hepatobiliary-Pancreas; Hepatocellular carcinoma; Heterogeneity; Humans; Imaging; Internal Medicine; Interventional Radiology; Kurtosis; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Male; Medical imaging; Medicine; Medicine & Public Health; Microvasculature; Microvessels - diagnostic imaging; Microvessels - pathology; Middle Aged; Neoplasm Invasiveness; Neuroradiology; Nomograms; Perfusion; Prospective Studies; Radiology; Risk factors; Strategy; Tumors; Ultrasound; α-Fetoprotein
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10339-2

s Objectives Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)–based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Methods Sixty-five patients were prospectively included and underwent multi- b DWI examinations. Based on the true diffusion coefficient ( D t ), perfusion fraction ( f ), and mean kurtosis coefficient ( MK ), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). Results MVI-positive HCC exhibited a significantly higher fraction of habitat 4 ( f 4 ) and a significantly lower fraction of habitat 2 ( f 2 ) ( p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. Conclusions DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. Clinical relevance statement The proposed strategy, diffusion-weighted imaging–based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. Key Points • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging–derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting