ArticleHPB : the official journal of the International Hepato Pancreato Biliary Association2019
Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis.
Article in HPB : the official journal of the International Hepato Pancreato Biliary Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation.Abdominal radiology (New York) · 2019Trial
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- Pancreatic Cancer Detection in Intraductal Papillary Mucinous Neoplasm (IPMN)-New Insights.Cancers · 2025Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Assessing Follow-Up Duration in Intraductal Papillary Mucinous Neoplasm Patients.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans.Research square · 2024Article
- Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans.Frontiers in oncology · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review.The British journal of surgery · 2023Article
- Intraductal Papillary Mucinous Neoplasm of the Pancreas: A Challenging Diagnosis.Diagnostics (Basel, Switzerland) · 2023Review
- Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists.Abdominal radiology (New York) · 2022Article
- Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2022Article
- Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach.Healthcare (Basel, Switzerland) · 2022Article
- Recent advances in the diagnostic evaluation of pancreatic cystic lesions.World journal of gastroenterology · 2022Review
- Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.Abdominal radiology (New York) · 2022Article
- Radiomics in surgical oncology: applications and challenges.Computer assisted surgery (Abingdon, England) · 2021Review
- Cystic pancreatic lesions: MR imaging findings and management.Insights into imaging · 2021Review
- Assessment of malignant potential in intraductal papillary mucinous neoplasms of the pancreas using MR findings and texture analysis.European radiology · 2021Article
- Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
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13 authors.
Funding
Abstract
backgroundIntraductal papillary mucinous neoplasms (IPMNs) are radiographically identifiable potential precursor lesions of pancreatic adenocarcinoma. While resection is recommended when main duct dilation is present, management of branch duct IPMN (BD-IPMN) remains controversial. This study sought to evaluate whether preoperative quantitative imaging features of BD-IPMNs could distinguish low-risk disease (low- and intermediate-grade dysplasia) from high-risk disease (high-grade dysplasia and invasive carcinoma).
methodsPatients who underwent resection between 2005 and 2015 with pathologically proven BD-IPMN and a preoperative CT scan were included in the study. Quantitative image features were extracted using texture analysis and a novel quantitative mural nodularity feature developed for the study. Significant features on univariate analysis were combined with clinical variables to build a multivariate prediction model.
resultsWithin the study group of 103 patients, 76 (74%) had low-risk disease and 27 (26%) had high-risk disease. Quantitative imaging features were prognostic of low-vs. high-risk disease. The model based only on clinical variables achieved an AUC of 0.67 and 0.79 with the addition of quantitative imaging features.
conclusionQuantitative image analysis of BD-IPMNs is a novel method that may enable risk stratification. External validation may provide a reliable non-invasive prognostic tool for clinicians.
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