Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm

Zheng, Ruiying; Zhang, Xiaoer; Liu, Baoxian; Zhang, Yi; Shen, Hui; Xie, Xiaoyan; Li, Shurong; Huang, Guangliang

doi:10.1007/s00330-023-09789-5

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…In terms of the correlation between Ki-67 expression level and tumor thrombus, one study ( 26 ) found that Ki-67 can synergistically enhance the vascular density in HCC cells to promote the occurrence of tumor thrombus, while another study ( 27 ) confirmed that a high expression of Ki-67 is linked to a greater likelihood of HCC recurrence and metastasis. Therefore, there is an urgent need to examine the correlation between Ki-67 expression level and the occurrence of tumor thrombus, as this may provide a key reference for clinical treatment, but no related studies regarding this correlation yet exist.…”

Section: Discussionmentioning

confidence: 99%

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma

Ma,

Li,

Zhu

2024

J Gastrointest Oncol

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Background Microvascular invasion (MVI) is considered to be an important factor in the early invasion and metastasis of liver cancer, and the survival rate of patients with MVI is much lower than that of patients without MVI. Therefore, it is crucial to accurately predict the independent predictors of tumor thrombus formation. This study aimed to assess the risk factors for tumor thrombus grades in patients with hepatocellular carcinoma (HCC). Methods Between August 2011 and December 2022, the data of 231 patients diagnosed with HCC were collected and divided into the following three groups: an MVI-negative group, an MVI-positive group, and a portal vein tumor thrombus (PVTT) group. Univariate analysis was used to compare the differences between the three groups in terms of clinical features, pathology, and imaging features. Multiple logistic regression analysis was used to analyze the risk factors associated with tumor thrombus grades, and the cutoff value was finally calculated by using the receiver operating characteristic (ROC) curve. Results The incidence of MVI and PVTT in the patients with HCC were 10.0% and 6.1%, respectively; univariate analysis revealed statistically significant differences in tumor diameter, alpha fetoprotein level, Ki-67 expression level, gender, tumor quantity, arteriovenous fistula, peritumoral enhancement, and satellite nodules among the three groups (P<0.05). Multiple logistic regression analysis showed that Ki-67 expression level, tumor diameter, and peritumoral enhancement were independent risk factors for tumor thrombus grades (P<0.05). The area under the curve (AUC) of Ki-67 expression level and tumor diameter was 0.713 [95% confidence interval (CI): 0.626–0.800] and 0.753 (95% CI: 0.669–0.837), respectively, and the AUC of the combination analysis was 0.805 (95% CI: 0.723–0.888), with a cutoff value of 17.5% and 4.1 cm, respectively (P<0.05). Conclusions Tumor diameter, Ki-67 expression level, and peritumoral enhancement can be used as independent predictors of tumor thrombus in patients with HCC. The combination of tumor diameter and Ki-67 expression level can further improve diagnostic efficacy.

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Section: Discussionmentioning

confidence: 99%

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma

Ma,

Li,

Zhu

2024

J Gastrointest Oncol

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“…predict microvascular invasion in HCC and mass-forming CC (36). The AUCs ranged from 0.75 to 0.98 with most of the studies achieving AUCs higher than 0.85 (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47). Notably, studies focused on peritumoral areas within the 5 cm to 10 cm range.…”

Section: Early Detection and Accurate Tumor Classificationmentioning

confidence: 99%

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Haghshomar,

Rodrigues,

Kalyan

et al. 2024

Front. Oncol.

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Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

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“…This method combines medical imaging and computational science to extract quantitative features from ultrasound images, revealing subtle patterns and spatial relationships that are not easily visible to the human eye 9 , 11 . Analyzing the wealth of information contained within ultrasound images has the potential to transform clinical decision-making and improve patient care in HCC research 12 .…”

Section: Introductionmentioning

confidence: 99%

Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics

Qian,

Huang,

et al. 2024

Sci Rep

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Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683–0.948) in the differentiation subgroup, 0.922 (0.785–1) in the CK7 subgroup, 0.762 (0.618–0.906) in the Ki67 subgroup, and 0.849 (0.667–1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.

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Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm

Cited by 11 publications

References 39 publications

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma

The influencing factors for tumor thrombus in patients with hepatocellular carcinoma

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics

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