ArticleScientific reports2022
CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Diagnostic Performance of Computed Tomography-Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Preoperative Prediction Model for Early Recurrence of Intrahepatic Cholangiocarcinoma after Surgical Resection: Development and External Validation Study.Cancer research and treatment · 2025Article
- Multimodal radiomics fusion for predicting postoperative recurrence in NSCLC patients.Journal of cancer research and clinical oncology · 2025Article
- Vascular penetration sign: dual-phase enhanced CT manifestations of atypical liver abscess caused by Klebsiella pneumoniae.European radiology · 2025Article
- Review
- Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algorithms.Frontiers in oncology · 2025Article
- Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model.Journal of the anus, rectum and colon · 2025Article
- Ultrashort Cell-Free DNA Fragments and Vimentin-Positive Circulating Tumor Cells for Predicting Early Recurrence in Patients with Biliary Tract Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.Frontiers in oncology · 2024Review
- Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images.Scientific reports · 2023Article
- Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.European journal of nuclear medicine and molecular imaging · 2023Article
- Contrast-Enhanced Imaging in the Management of Intrahepatic Cholangiocarcinoma: State of Art and Future Perspectives.Cancers · 2023Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early recurrence was observed in 20 (48.8%). A total of 71,081 patches were extracted from the entire segmented tumor area of each patient. The average accuracy of the ResNet model for predicting early recurrence was 98.2% for the training dataset. In the validation dataset, the average sensitivity, specificity, and accuracy were 97.8%, 94.0%, and 96.5%, respectively. Furthermore, the area under the receiver operating characteristic curve was 0.994. Our CT-based DL model exhibited high predictive performance in projecting postoperative early recurrence, proposing a novel insight into iCCA management.
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