ArticleQuantitative imaging in medicine and surgery2022
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Diagnostic performance of FDG PET/CT radiomics in predicting microvascular invasion in hepatocellular carcinoma compared to conventional metabolic parameters: a systematic review and meta-analysis.Annals of nuclear medicine · 2025Pooled it
- AI-Driven Radiomics Assisted Prognostic Modeling for Hepatocellular Carcinoma with Portal Vein Invasion: A Retrospective Study.Biomedicines · 2026Article
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Progress in the application of fludeoxyglucose positron emission tomography computed tomography in biliary tract cancer.World journal of hepatology · 2025Review
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- A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma.Frontiers in oncology · 2024Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Perineural invasion confers poorer clinical outcomes in patients with T1/T2 intrahepatic cholangiocarcinoma: a single center, retrospective cohort study.Journal of gastrointestinal oncology · 2023Article
- Predicting the microvascular invasion and tumor grading of intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance imaging radiomics and morphological features.Quantitative imaging in medicine and surgery · 2023Article
- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics.Cancers · 2023Article
- Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection.Quantitative imaging in medicine and surgery · 2023Article
- The application ofQuantitative imaging in medicine and surgery · 2023Review
- Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: anQuantitative imaging in medicine and surgery · 2023Article
- Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms.Journal of cancer research and clinical oncology · 2023Article
- Contrast-Enhanced Imaging in the Management of Intrahepatic Cholangiocarcinoma: State of Art and Future Perspectives.Cancers · 2023Review
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of Methods: In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models. Results: For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively). Conclusions:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.