Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma

Shao, Chunchun; Zhao, Fang; Yu, Yi-Fan; Zhu, Linlin; Pang, Guodong

doi:10.1097/cm9.0000000000001446

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…CTPI is a noninvasive technique that facilitates reproducible and quantitative assessments of perfusion parameters related to tumorassociated vascularization [8]. Radiometric imaging provides reliable quantitative perfusion data and excellent anatomical imaging ability and easily re ects microstructural changes at the functional level [3,5]. Recent perfusion studies on HCC have focused on this topic; however, very few studies on the surrounding liver parenchyma have been conducted.…”

Section: Discussionmentioning

confidence: 99%

“…Kurucay M[4] used CTPI to evaluate liver cancer and reported that the HPI was signi cantly better than that obtained by MRI for diagnosing liver cancer. Shao CC [5] reported that quantitative hepatic perfusion parameters and corresponding histogram parameters can be used to predict the histological grade of HCC. Choi's liver perfusion imaging with cone-beam computed tomography (CBCT) provides supplementary information on tissue perfusion in TACE, which makes it easier to identify residual active lesions and effectively evaluate treatment response [6].…”

Section: Introductionmentioning

confidence: 99%

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Perfusion changes in hepatocellular carcinoma and surrounding liver parenchyma based on perfusion CT imaging

Li,

Ji,

Gao

et al. 2024

Preprint

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Purpose The aim of this study was to use computed tomography perfusion imaging (CTPI) to compare and analyze the differences in perfusion parameters between hepatocellular carcinoma (HCC) and surrounding tumor tissue before and after transarterial chemoembolization (TACE) to diagnose HCC and evaluate its curative effect. Methods Twenty-one HCC patients were examined by CTPI before and four weeks after TACE. Regions of interest (ROIs) were placed in the tumor (T1), peritumoral-1 cm (T2), peritumoral-2 cm (T3), and background liver parenchymal (T4) areas before TACE and in the residual tumor (T1a)/lipiodol-retaining (T1b) areas after TACE. The perfusion parameters included hepatic arterial perfusion (HAP), portal venous perfusion (PVP), total liver perfusion (TLP), the hepatic perfusion index (HPI), and the time to peak (TTP). Results A total of 32 lesions were detected, 11 lesions contained residual lesions, and 21 patients had good lipiodol deposition after TACE. The HAP and HPI signals demonstrated a decreasing trend (T1/T1a > T2 > T3 > T4), whereas the PVP and TLP signals showed an increasing trend (T1/T1a < T2 < T3 < T4) in the preoperative and postoperative residual groups, except for HAP and TLP in the T2 vs. T3, or HAP, HPI and TTP inT3 vs. T4 in postoperative residual, the remaining parameters are statistically significant. (p < 0.05). No blood perfusion was detected at T1b in the postoperative lipiodol-retaining group, and both the HAP and HPI signals reached their highest levels at T2, whereas the PVP, TLP, and TTP signals were highest at T4. The parameters of the T4 group were significantly different from those of the T2 and T3 groups (p < 0.05). Compared with those in the preoperative and residual groups, in the postoperative lipiodol group, the HAP and HPI were significantly lower, the PVP was significantly greater in T2 and T3, and TLP were significantly greater in T2 (p < 0.05). Conclusion CTPI can be used to evaluate the blood flow of hepatocellular carcinoma patients and surrounding areas before and after treatment. After TACE, the difference in perfusion in each area of the residual tumor group exhibited little change, while the difference in blood flow between the tumor and its surrounding area with dense lipiodol deposition greatly changed, which decreased the difference in perfusion between the tumor and the adjacent area. These findings provide reference data for the selection of treatment methods and postoperative evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Perfusion changes in hepatocellular carcinoma and surrounding liver parenchyma based on perfusion CT imaging

Li,

Ji,

Gao

et al. 2024

Preprint

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“…Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed cancer and the third cause of cancer-related mortality worldwide[ 1 , 2 ]. HCC accounts for 75%-90% of primary liver cancers and constitutes a major global health problem[ 3 ]; moreover, HCC is difficult to treat.…”

Section: Introductionmentioning

confidence: 99%

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

Zhang,

Zhong,

Zhao

et al. 2024

World J Gastrointest Oncol

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BACKGROUND Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC. METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model’s performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram. RESULTS The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models. CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.

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Perfusion parameters of triphasic computed tomography hold preoperative prediction value for microvascular invasion in hepatocellular carcinoma

Zhang

Pang

Zhang

et al. 2023

Sci Rep

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The purpose of this study was to evaluate perfusion parameters of triphasic computed tomography (CT) scans in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). All patients were pathologically diagnosed as HCC and underwent triple-phase enhanced CT imaging, which was used to calculate the blood perfusion parameters of hepatic arterial supply perfusion (HAP), portal vein blood supply perfusion (PVP), hepatic artery perfusion Index (HPI), and arterial enhancement fraction (AEF). Receiver operating characteristic (ROC) curve was used to evaluate the performance. The mean values of PVP(Min), AEF(Min), the difference in PVP, HPI and AEF related parameters, the relative PVP(Min) and AEF(Min) in MVI negative group were significantly higher than those in MVI positive group, while for the difference in HPI(Max), the relative HPI(Max) and AEF(Max), the value of MVI positive group significantly higher than that of negative group. The combination of PVP, HPI and AEF had the highest diagnostic efficacy. The two parameters related to HPI had the highest sensitivity, while the combination of PVP related parameters had higher specificity. A combination of perfusion parameters in patients with HCC derived from traditional triphasic CT scans can be used as a preoperative biomarker for predicting MVI.

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Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma

Cited by 3 publications

References 37 publications

Perfusion changes in hepatocellular carcinoma and surrounding liver parenchyma based on perfusion CT imaging

Perfusion changes in hepatocellular carcinoma and surrounding liver parenchyma based on perfusion CT imaging

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

Perfusion parameters of triphasic computed tomography hold preoperative prediction value for microvascular invasion in hepatocellular carcinoma

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