ArticleHepatology international2022
Image-matching digital macro-slide-a novel pathological examination method for microvascular invasion detection in hepatocellular carcinoma.
Article in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 12 citations in OpenAlex.
- Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma.JHEP reports : innovation in hepatology · 2025Article
- Machine learning-based multiparametric CT radiomics for predicting microvascular invasion before nephrectomy in clear cell renal cell carcinoma.Abdominal radiology (New York) · 2025Article
- Histopathological diagnosis of microvascular invasion in hepatocellular carcinoma: Is it reliable?World journal of gastroenterology · 2025Article
- Grading severity of microvascular invasion in hepatocellular carcinoma: More details, more significance.Clinical and molecular hepatology · 2025Article
- Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.Cancer medicine · 2024Article
- Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: anQuantitative imaging in medicine and surgery · 2023Article
- Reflections on prediction of microvascular invasion in hepatocellular carcinoma by pathology images.Hepatology international · 2023Article
- A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy.Hepatology international · 2022Article
- Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system.Frontiers in oncology · 2022Article
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12 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMicrovascular invasion (MVI) is a prominent risk factor of postoperative recurrence for hepatocellular carcinoma (HCC). The MVI detection rate of conventional pathological examination approaches is relatively low and unsatisfactory.
methodsBy integrating pathological macro-slide with whole-mount slide imaging, we first created a novel pathological examination method called image-matching digital macro-slide (IDS). Surgical samples from eligible patients were collected to make IDS. The MVI detection rates, tumor recurrence rates and recurrence-free survival were compared among conventional 3-Point and 7-Point baseline sampling protocols and IDS. Additionally, biomarkers to recognize MVI false negative patients were probed via combining conventional pathological sampling protocols and IDS. Receiver operating characteristic curve (ROC) analysis was used to obtain the optimal cutoff of biomarkers to distinguish MVI false negative patients.
resultsThe MVI detection rates were 21.98%, 32.97% and 63.74%, respectively, in 3-Point, 7-Point baseline sampling protocols and IDS (p < 0.001). Tumor recurrence rate of patients with MVI negative status in IDS (6.06%) was relatively lower than that of patients with MVI negative status in 3-Point (16.90%) and 7-Point (16.39%) sampling protocols. Alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were selected as potential biomarkers to distinguish MVI false negative patients.
conclusionsOur study demonstrated that IDS can help enhance the detection rate of MVI in HCC and refine the prediction of HCC prognosis. Alpha-fetoprotein is identified as a suitable and robust biomarker to recognize MVI false-negative patients in conventional pathological protocols.
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