ArticleWorld journal of gastroenterology2020
Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy.
Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 47 citations in OpenAlex.
- Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
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- Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features.European radiology · 2024Article
- Review
- Radiomics model versus 2017 revised international consensus guidelines for predicting malignant intraductal papillary mucinous neoplasms.European radiology · 2024Article
- 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
- Incremental Improvements in the Ability to Distinguish High-Risk Intraductal Papillary Mucinous Neoplasms.Annals of surgical oncology · 2023Article
- Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential.La Radiologia medica · 2023Article
- Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence.Cancers · 2023Review
- Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer.Artificial intelligence surgery · 2023Article
- 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
- Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.BMC cancer · 2022Article
- Pancreatic Incidentaloma.Journal of clinical medicine · 2022Review
- Review
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Radiomics in surgical oncology: applications and challenges.Computer assisted surgery (Abingdon, England) · 2021Review
- Narrative review of intraductal papillary mucinous neoplasms: pathogenesis, diagnosis, and treatment of a true precancerous lesion.Gland surgery · 2021Review
- Review
- Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm.Frontiers in oncology · 2021Article
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundIntraductal papillary mucinous neoplasms (IPMNs) are non-invasive pancreatic precursor lesions that can potentially develop into invasive pancreatic ductal adenocarcinoma. Currently, the International Consensus Guidelines (ICG) for IPMNs provides the basis for evaluating suspected IPMNs on computed tomography (CT) imaging. Despite using the ICG, it remains challenging to accurately predict whether IPMNs harbor high grade or invasive disease which would warrant surgical resection. A supplementary quantitative radiological tool, radiomics, may improve diagnostic accuracy of radiological evaluation of IPMNs. We hypothesized that using CT whole lesion radiomics features in conjunction with the ICG could improve the diagnostic accuracy of predicting IPMN histology.
aimTo evaluate whole lesion CT radiomic analysis of IPMNs for predicting malignant histology compared to International Consensus Guidelines.
methodsFifty-one subjects who had pancreatic surgical resection at our institution with histology demonstrating IPMN and available preoperative CT imaging were included in this retrospective cohort. Whole lesion semi-automated segmentation was performed on each preoperative CT using Healthmyne software (Healthmyne, Madison, WI). Thirty-nine relevant radiomic features were extracted from each lesion on each available contrast phase. Univariate analysis of the 39 radiomics features was performed for each contrast phase and values were compared between malignant and benign IPMN groups using logistic regression. Conventional quantitative and qualitative CT measurements were also compared between groups,
resultsTwenty-nine subjects (15 males, age 71 ± 9 years) with high grade or invasive tumor histology comprised the "malignant" cohort, while 22 subjects (11 males, age 70 ± 7 years) with low grade tumor histology were included in the "benign" cohort. Radiomic analysis showed 18/39 precontrast, 19/39 arterial phase, and 21/39 venous phase features differentiated malignant from benign IPMNs (
conclusionMulti-phase CT radiomics evaluation could play a role in improving predictive capability in diagnosing malignancy in IPMNs. Future larger studies may help determine the clinical significance of our findings.
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