Microvascular Invasion Evaluation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent primary liver malignancies worldwide, and its prognosis is heavily influenced by the presence of microvascular invasion (MVI). MVI denotes the histological detection of tumour cells within the small vascular channels at the periphery of the neoplasm and is a recognised predictor of early recurrence and poor overall survival. Traditional diagnosis requires postoperative examination of surgical specimens, limiting its utility in preoperative planning. In recent years, non‐invasive strategies have emerged, encompassing advanced imaging techniques, quantitative radiomics, urinary and serum biomarker assays, and integrative multi‐omic analyses. These approaches seek to stratify patients according to MVI risk before resection or ablation, thereby guiding decisions on surgical margins, anatomical resections and the need for adjuvant therapies. By combining clinical variables with novel computational models, clinicians can now estimate the likelihood of angioinvasion with increasing accuracy, facilitating personalised treatment pathways and improved long‐term outcomes for those with HCC.

Research from Nature Portfolio

A comprehensive meta‐analysis evaluated the diagnostic value of irregular tumour margins on preoperative imaging for predicting MVI. Aggregating data across multiple studies, it demonstrated that a non‐smooth tumour boundary on CT or MRI carries a high diagnostic odds ratio, equivalent to or exceeding many multivariable scoring systems. Subgroup analyses revealed particularly robust performance in older patient cohorts and when computed tomography was employed. The findings underscore the merit of simple imaging markers in preoperative risk assessment and support their inclusion in future predictive nomograms.

Microvascular Invasion Evaluation in Hepatocellular Carcinoma publication trend

The graph below shows the total number of articles in microvascular invasion evaluation in hepatocellular carcinoma across all publications each year (not limited to Nature Index journals).

Technical terms

Microvascular invasion (MVI): The histological presence of tumour cells within small blood vessels at the tumour periphery, indicating aggressive behaviour and poor prognosis.

Radiomics: The extraction and analysis of large numbers of quantitative features from medical images to characterise tumour phenotype.

Nomogram: A graphical tool representing a statistical predictive model that computes the probability of a clinical event based on multiple variables.

Transcriptomics: The comprehensive study of RNA transcripts produced by the genome, often using bulk, single‐cell or spatial sequencing techniques.

Machine learning: A set of computational algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed for the task.

References

1. A non-smooth tumor margin on preoperative imaging assesses microvascular invasion of hepatocellular carcinoma: A systematic review and meta-analysis. Scientific Reports (2017).
2. A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients. Biomarker Research (2023).
3. Noninvasive urinary protein signatures combined clinical information associated with microvascular invasion risk in HCC patients. BMC Medicine (2023).
4. Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma. Frontiers in Immunology (2024).