Value of Intravoxel Incoherent Motion (IVIM) Imaging for Differentiation between Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Wang, Jinhua; Yang, Zhongxian; Luo, Min; Chen, Xu; Du, Mu; Liu, Yubao

doi:10.1155/2022/1504463

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Effective diagnosis is an important prerequisite for formulating treatment measures and evaluating curative effect. Therefore, it is particularly necessary to conduct timely, accurate, and effective examinations and study the patient ‘s disease-related indicators [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

Wang

2022

Computational and Mathematical Methods in Medicine

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Background. Hepatocellular carcinoma is one of the malignant tumors with the highest incidence in the world. According to the latest statistics of the National Cancer Center, the incidence of liver cancer ranks fifth in malignant tumors and its mortality rate ranks second in China, which seriously threatens people’ s life and health. Aim. To investigate the value of CT perfusion parameters and apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) in the diagnosis of hepatocellular carcinoma. Methods. 43 patients with hepatocellular carcinoma and 40 patients with hepatic hemangioma treated in our hospital from August 2018 to August 2021 were selected for CT perfusion imaging and MRI examination. Results. The liver blood flow (BF), liver blood volume (BV), and hepatic artery perfusion (HAP) in the hepatocellular carcinoma group were (267.38 ± 35.59) ml/(min·100 g), (30.20 ± 8.82) ml/100 g, and (0.64 ± 0.10) ml/(min·ml), respectively, which were significantly higher than those in the hepatic hemangioma group ( p < 0.05 ). The ADC value of hepatocellular carcinoma DWI sequence was (1.20 ± 0.17) ×10-3 mm2, which was significantly lower than that of hepatic hemangioma ( p < 0.05 ). The area under ROC curve of BF, BV, HAP, and ADC values for hepatocellular carcinoma was 0.860, 0.754, 0.804, and 0.890, respectively. The area under ROC curve of the four groups was compared ( p > 0.05 ). Conclusion. CT perfusion parameters BF, BV, HAP, and DWI sequence ADC values have certain application value in the diagnosis of hepatocellular carcinoma, and there is no significant difference between the diagnostic value of each parameter.

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Section: Discussionmentioning

confidence: 99%

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

Wang

2022

Computational and Mathematical Methods in Medicine

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“…13,35,36,61 Additionally, IVIM-DWI holds potential value in the evaluation of recurrence risk and therapeutic effect of liver tumors. 83,84 However, IVIM-DWI may not be reliable in the stage of liver fibrosis, 54,55 HCC histological classification, and the distinction of malignant hepatic tumors with diverse pathological types, 29,72,76 which may be due to the inherent drawbacks of the IVIM model. First, IVIM assumes that microcapillary perfusion follows a random pattern similar to water diffusion.…”

Section: Discussionmentioning

confidence: 99%

“…IVIM‐derived parameters can also be used to differentiate hepatic malignancies with distinct pathological characteristics. The correlation of PF and arterial enhancement degree, as well as the extent of hepatic nodule enhancement allow for differentiation of different types of focal hepatic lesions 29,67,72 . Prior studies have suggested the utility of IVIM‐derived parameters for distinguishing between intrahepatic cholangiocarcinoma (ICC) and HCC 65,67,73 (Fig.…”

Section: Liver Focal Lesionsmentioning

confidence: 99%

Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review

Wang,

Yu,

Qiu

et al. 2023

Magnetic Resonance Imaging

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Intravoxel incoherent motion (IVIM) modeling is a widely used double‐exponential model for describing diffusion‐weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM‐derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM‐DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM‐derived parameters for different fibrotic stages. In the end, the future direction of IVIM‐DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM‐derived parameters, particularly perfusion‐related parameters, in order to promote the clinical practice of IVIM‐DWI.Level of Evidence5Technical EfficacyStage 3

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“…Thus, assessing the degree of ICC differentiation through preoperative imaging can help physicians select an appropriate treatment and improve patients' survival rates. Current research on ICC is dominated by hepatocellular carcinoma (HCC) differentiation [4][5][6][7] . Studies on machine learning based on radiomic features to preoperatively predict histological differentiation of mass-forming ICC are scarce [8] .…”

Section: Introductionmentioning

confidence: 99%

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

Zhou,

Zhong

2024

Preprint

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Objective We aim to establish a machine learning model by extracting the radiomic features from CT images and integrating clinical features to preoperatively predict the histological differentiation of mass-forming ICC. Material and Method: Clinical data and CT images were retrospectively collected from 109 ICC patients (male to female = 63:46) in our hospital from January 2013 to October 2023. The machine learning classification algorithms used in this study were random forest (RF), XGBoost (Extreme Gradient Boosting), k-nearest neighbors (KNN), and logistics regression (LR). The area under the curve of the receiver operating characteristic (AUROC) of the model on each class, as well as the macro and micro averages were calculated to comprehensively evaluate the model performance.SHapley Additive exPlanations (SHAP) was used to explain the output of the optimal model. Results Concomitant cirrhosis was more likely to occur in poorly differentiated ICC (p < 0.01), while elevated ALT and AST were more common in moderately differentiated ICC (p = 0.02). The RF model constructed based on radiomic features had moderate performance, with a macro-averaged AUC of 0.72, and an AUC of 0.69 for poorly differentiated ICC, 0.7 for moderately poorly differentiated ICC, and 0.71 for moderately differentiated ICC. Both the clinical features model and the fusion model of clinical + radiomic features performed relatively poorly, with a macro-averaged AUC of 0.51 and 0.57, respectively. Conclusion We directly classified the three histological differentiations of 109 ICC cases and found that the radiomics model performed moderately well. This suggested that a radiomics feature model alone might perform better in classification than a fusion model and that the addition of clinical features.

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Value of Intravoxel Incoherent Motion (IVIM) Imaging for Differentiation between Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Cited by 5 publications

References 41 publications

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

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