ArticleBioMed research international2019
Noninvasive Evaluation of the Pathologic Grade of Hepatocellular Carcinoma Using MCF-3DCNN: A Pilot Study.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Long-term outcomes of thermal ablationWorld journal of clinical oncology · 2025Article
- Harnessing the Power of AI for Enhanced Diagnosis and Treatment of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Review
- Advanced Imaging of Hepatocellular Carcinoma: A Review of Current and Novel Techniques.Journal of gastrointestinal cancer · 2024Review
- [Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.Frontiers in genetics · 2023Review
- Clinical and imaging features preoperative evaluation of histological grade and microvascular infiltration of hepatocellular carcinoma.BMC gastroenterology · 2022Article
- Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?Frontiers in oncology · 2022Article
- Assessment of Liver Function With MRI: Where Do We Stand?Frontiers in medicine · 2022Review
- Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study.Abdominal radiology (New York) · 2021Article
- Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives.World journal of gastroenterology · 2021Review
- Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.Japanese journal of radiology · 2021Article
- State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2021Review
- A novel computer-aided diagnostic system for accurate detection and grading of liver tumors.Scientific reports · 2021Article
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6 authors.
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Abstract
purposeTo evaluate the diagnostic performance of deep learning with a multichannel fusion three-dimensional convolutional neural network (MCF-3DCNN) in the differentiation of the pathologic grades of hepatocellular carcinoma (HCC) based on dynamic contrast-enhanced magnetic resonance images (DCE-MR images). METHODS AND MATERIALS: Fifty-one histologically proven HCCs from 42 consecutive patients from January 2015 to September 2017 were included in this retrospective study. Pathologic examinations revealed nine well-differentiated (WD), 35 moderately differentiated (MD), and seven poorly differentiated (PD) HCCs. DCE-MR images with five phases were collected using a 3.0 Tesla MR scanner. The 4D-tensor representation was employed to organize the collected data in one temporal and three spatial dimensions by referring to the phases and 3D scanning slices of the DCE-MR images. A deep learning diagnosis model with MCF-3DCNN was proposed, and the structure of MCF-3DCNN was determined to approximate clinical diagnosis experience by taking into account the significance of the spatial and temporal information from DCE-MR images. Then, MCF-3DCNN was trained based on well-labeled samples of HCC lesions from real patient cases by experienced radiologists. The accuracy when differentiating the pathologic grades of HCC was calculated, and the performance of MCF-3DCNN in lesion diagnosis was assessed. Additionally, the areas under the receiver operating characteristic curves (AUC) for distinguishing WD, MD, and PD HCCs were calculated.
resultsMCF-3DCNN achieved an average accuracy of 0.7396±0.0104 with regard to totally differentiating the pathologic grade of HCC. MCF-3DCNN also achieved the highest diagnostic performance for discriminating WD HCCs from others, with an average AUC, accuracy, sensitivity, and specificity of 0.96, 91.00%, 96.88%, and 89.62%, respectively.
conclusionsThis study indicates that MCF-3DCNN can be a promising technology for evaluating the pathologic grade of HCC based on DCE-MR images.
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