Application of dual-source CT perfusion imaging and MRI for the diagnosis of primary liver cancer

Zhang, Dong-wen; Xu, Anlong

doi:10.3892/ol.2017.6170

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…In recent years, an increasing number of studies have reported that DS-DECT can be used to diagnose malignant and benign tumors (22,(27)(28)(29). The present study showed that quantitative parameters derived from DS-DECT could differentiate benign from malignant thyroid nodules.…”

Section: Discussionsupporting

confidence: 58%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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Background: This study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules.Methods: Patients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy. Results: One hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentration nodule (arterial phase) (P=0.001), normalized iodine concentration (arterial phase) (P=0.002), iodine concentration difference (P<0.001), spectral curve slope (nonenhancement) (P=0.007), spectral curve slope (arterial phase) (P=0.001), effective atomic number (nonenhancement) (P<0.001), and effective atomic number (arterial phase) (P=0.039) than the benign group. The binary logistic regression analysis model had an AUC (area under the curve) of 0.76, a sensitivity of 0.821, and a specificity of 0.667. The machine learning model had an AUC of 0.86, a sensitivity of 0.822, specificity of 0.791 in the training cohort, an AUC of 0.84, a sensitivity of 0.727, and specificity of 0.750 in the testing cohort. Conclusions: Multiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.

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Section: Discussionsupporting

confidence: 58%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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“…According to our results, the CT perfusion imaging-related parameters and serum VEGF and BFGF in patients with primary liver cancer were significantly different from those in patients with benign liver lesions, and the CT perfusion parameters and serum VEGF and BFGF levels in patients with different stages of primary liver cancer were different, which was because blood of foci in primary liver cancer is mainly supplied by the hepatic artery with especially fast flow velocity, thereby increasing the pressure [ 19 , 20 ]. Significantly, Pearson correlation analysis showed that CT perfusion parameters were correlated with serum VEGF and BFGF levels.…”

Section: Discussionmentioning

confidence: 96%

Correlation of CT Perfusion Parameters and Vascular Endothelial Growth Factor (VEGF) and Basic Fibroblast Growth Factor (BFGF) in Patients with Primary Liver Cancer

Chen

Zhang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. To investigate the correlation of CT perfusion-related parameters with serum vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (BFGF) in patients with primary liver cancer. Methods. A total of 100 patients with primary liver cancer who were treated in our hospital from June 2019 to June 2021 were selected as the observation group, and 90 patients with benign liver lesions during the same period were selected as the control group. The CT perfusion-related parameters (perfusion volume and perfusion index) and serum VEGF and BFGF levels were compared between the two groups. Pearson correlation was used to analyze the correlation between CT perfusion-related parameters and serum VEGF and BFGF levels. Results. Compared to the control group, significantly higher HAP and lower HPP and TLP were observed in the observation group. The perfusion volume indexes of patients with different stages of liver cancer in the observation group were statistically different ( P < 0.05 ). Compared to the control group, the observation group witnessed significantly higher HAPI and lower HPPI. There were statistically significant differences in the perfusion index of patients with different stages of primary liver cancer in the observation group ( P < 0.05 ). The serum VEGF and BFGF levels in the observation group were significantly higher than those in the control group, and the serum VEGF and BFGF levels in patients with different stages of primary liver cancer in the observation group were statistically different ( P < 0.05 ). Pearson correlation analysis showed that HAP and HAPI were positively correlated with VEGF and BFGF (r = 0.986, P ≤ 0.001 ; r = 0.983, P ≤ 0.001 ), and HPP, TLP, and HPPI were negatively correlated with VEGF and BFGF (r = −0.992, P ≤ 0.001 ; r = -0.993, P ≤ 0.001 ; r = −0.995, P ≤ 0.001 ). Conclusion. CT perfusion-related parameters and serum VEGF and BFGF levels in patients with primary liver cancer are abnormally expressed, and there is a strong correlation between the two, which might aid clinical diagnosis and treatment.

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“…Brain death Clinical death lesions (8). Studies (9,10) have shown that during MRI, gadobenate dimeglumine enhancement of the hepatobiliary phase effectively helps identify focal nodular hyperplasia, atypical nodular hyperplasia cirrhosis, and liver cancer.…”

Section: Discussionmentioning

confidence: 99%

A case of gadobenate dimeglumine-induced anaphylactic shock: a case report

et al. 2021

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A 70-year-old man was admitted to our hospital due to "liver cirrhosis; grade 3 hypertension; pulmonary infection". On May 27, 2019, during upper abdomen plain and enhanced magnetic resonance imaging (MRI) and magnetic resonance cholangiopancreatography (MRCP), the patient experienced anaphylactic shock, manifested as sudden unconsciousness and lack of response, after intravenous administration of gadobenate dimeglumine (Multihance ® ). Gadobenate dimeglumine is a paramagnetic contrast used during diagnostic MRI. It has hepatobiliary specificity with very good imaging performance.A small amount is absorbed by normal liver cells after intravenous injection and excreted via the bile ducts while maintaining the chemical structure of gadobenate dimeglumine. It allows the visualization of local angiogenesis and perfusion, which reflect the hepatic blood supply and recent liver function, thereby providing a reference for clinical diagnosis. Gadobenate dimeglumine intravenous injection may cause adverse reactions such as nausea, dizziness, and anaphylactic shock. Anaphylactic shock is a known serious adverse reaction of gadobenate dimeglumine injection. In this paper, we report a case of gadobenate dimeglumine-induced anaphylactic shock based on the temporal relationship between the onset of symptoms and the injection. The patient received chest compressions and balloon-assisted ventilation in addition to rehydration and volume expansion and vasoactive drugs to maintain blood pressure, etc. The patient died despite treatments. In the clinical, physicians, nurses, and clinical pharmacists should closely monitor patients and promptly discontinue drug administration and provide symptomatic care in case of adverse reactions.

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Application of dual-source CT perfusion imaging and MRI for the diagnosis of primary liver cancer

Cited by 11 publications

References 19 publications

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Correlation of CT Perfusion Parameters and Vascular Endothelial Growth Factor (VEGF) and Basic Fibroblast Growth Factor (BFGF) in Patients with Primary Liver Cancer

A case of gadobenate dimeglumine-induced anaphylactic shock: a case report

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