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Cited by 143Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology ResearchMRI in cancer diagnosis

Quantitative imaging to evaluate malignant potential of IPMNs

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Hanania, Alexander N., Bantis, Leonidas E., Feng, Ziding, Wang, Huamin, Tamm, Eric P., Katz, Matthew H., Maitra, Anirban, Koay, Eugene J.·2016·Oncotarget
DOI 10.18632/oncotarget.11769PMC PMC5349873
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Abstract

// Alexander N. Hanania 1,2 , Leonidas E. Bantis 3 , Ziding Feng 3 , Huamin Wang 4 , Eric P. Tamm 5 , Matthew H. Katz 6 , Anirban Maitra 4 and Eugene J. Koay 2 1 University of Texas Medical School, Houston, TX, USA 2 Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA 3 Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA 4 Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA 5 Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA 6 Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA Correspondence to: Eugene J. Koay, email: // Keywords : IPMN, quantitative imaging, radiomics, fukuoka, pancreatic cyst Received : July 06, 2016 Accepted : August 18, 2016 Published :August 31, 2016 Abstract Objective: To investigate using quantitative imaging to assess the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) in the pancreas. Background: Pancreatic cysts are identified in over 2% of the population and a subset of these, including intraductal papillary mucinous neoplasms (IPMNs), represent pre-malignant lesions. Unfortunately, clinicians cannot accurately predict which of these lesions are likely to progress to pancreatic ductal adenocarcinoma (PDAC). Methods: We investigated 360 imaging features within the domains of intensity, texture and shape using pancreatic protocol CT images in 53 patients diagnosed with IPMN (34 “high-grade” [HG] and 19 “low-grade” [LG]) who subsequently underwent surgical resection. We evaluated the performance of these features as well as the Fukuoka criteria for pancreatic cyst resection. Results: In our cohort, the Fukuoka criteria had a false positive rate of 36%. We identified 14 imaging biomarkers within Gray-Level Co-Occurrence Matrix (GLCM) that predicted histopathological grade within cyst contours. The most predictive marker differentiated LG and HG lesions with an area under the curve (AUC) of .82 at a sensitivity of 85% and specificity of 68%. Using a cross-validated design, the best logistic regression yielded an AUC of 0.96 (σ = .05) at a sensitivity of 97% and specificity of 88%. Based on the principal component analysis, HG IPMNs demonstrated a pattern of separation from LG IPMNs. Conclusions: HG IPMNs appear to have distinct imaging properties. Further validation of these findings may address a major clinical need in this population by identifying those most likely to benefit from surgical resection.

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  • These models had false positive rates of 5%, while the Fukuoka criteria had a 36% false positive rate in their cohort.

    “These models had false positive rates of 5%, while the Fukouka criteria had a 36% false positive rate in their cohort [54].”

    From Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma (2023) · cites this paper as [54]

    The Results section reports a 36% false-positive rate for the Fukuoka criteria in this cohort. It reports 88% specificity for the best cross-validated model, equivalent to a 12% false-positive rate at that operating point. The 5% rate in Materials and Methods was a fixed threshold used to assess candidate markers, not a reported false-positive rate for the models. (LLM, conf=0.91)

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  • Hanania et al. reported a logistic regression model achieving an AUC of 0.96 (sensitivity 97% and specificity 88%) for identifying malignant IPMNs.

    “Hanania et al. reported similarly promising findings, with a logistic regression model achieving an AUC of 0.96 (sensitivity 97% and specificity 88%).”

    From Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma (2023) · cites this paper as [54]

  • The Fukuoka criteria incorrectly direct benign lesions to surgery one-third of the time (false-positive rate 36%).

    “The current approach to preoperative identification of potentially malignant IPMNs (Fukuoka criteria) incorrectly directs benign lesions to surgery one-third of the time (a false-positive rate of 36%).207”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [207]

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Cited by

Sorted by citations

Authors
Hanania, A. N., Bantis, L. E., Feng, Z., Wang, H., Tamm, E. P., Katz, M. H., Maitra, A., & Koay, E. J.
Affiliations
The University of Texas MD Anderson Cancer Center (US)
ORCID
Venue
Oncotarget
Publisher
Impact Journals, LLC
Published
2016-08-31
Volume
7
Issue
52
Pages
85776-85784
ISSN
1949-2553 (ISSN-L)
DOI
10.18632/oncotarget.11769
PMC
PMC5349873
OpenAlex
W2515797801
Cited by
143
References
6
Citations by year
2026: 8 · 2025: 5 · 2024: 10 · 2023: 12 · 2022: 13 · 2021: 16 · 2020: 27 · 2019: 24 · 2018: 15 · 2017: 9 · 2016: 2
FWCI
14.47
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Oncotarget (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Pancreatic Cancer Action Network (14-20-25-KOAY, 16‐65‐SING)
  • Radiological Society of North America (RSD1429)
  • National Institutes of Health (CA- 016672)
  • University of Texas MD Anderson Cancer Center ((CA016672), grant CA016672)
  • National Cancer Institute (Grant CA016672, CA 016672, U01CA196403, P30CA016672)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords
intraductal papillary mucinous neoplasm, pancreatic cysts, quantitative imaging, radiomics, gray level co-occurrence matrix, texture analysis, pancreatic ductal adenocarcinoma, malignant potential prediction, histopathological grade, CT imaging biomarkers, false positive rate
MeSH terms
Aged, Aged, 80 and over, Adenocarcinoma, Mucinous, Carcinoma, Papillary, Female, Humans, Male, Middle Aged, Pancreatic Neoplasms, Retrospective Studies, Tomography, X-Ray Computed, Logistic Models, Carcinoma, Pancreatic Ductal

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