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Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts

  • Pancreas
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics.

Methods

A retrospective, single-institution analysis of patients with non-pseudocystic PCs, contrast-enhanced computed tomography scans within 1 year of resection, and available surgical pathology were included. A quantitative imaging software platform was used to extract radiomics. An extreme gradient boosting (XGBoost) machine learning algorithm was used to create mucinous classifiers using texture features only, or radiomic/radiologic and clinical combined models. Classifiers were compared using performance scoring metrics. Shapely additive explanation (SHAP) analyses were conducted to identify variables most important in model construction.

Results

Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model.

Conclusion

Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis.

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Data availability

Data was collected with HealthMyne (v5.0, Madison, WI).

Code availability

Git Hub Repository, https://github.com/uw-cmg/MeghanPanccystmachine.

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Funding

ML4MI internal funding.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI, 53792, USA

    Adam M. Awe & Meghan G. Lubner

  2. Department of Surgery, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA

    Adam M. Awe & Victoria R. Rendell

  3. Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, USA

    Michael M. Vanden Heuvel, Tianyuan Yuan, Agrima Kampani & Shanchao Liang

  4. Department of Materials Science and Engineering, University of Wisconsin - Madison, Madison, WI, USA

    Mingren Shen & Dane D. Morgan

  5. Medstar Georgetown Transplant Institute, Medstar Georgetown University Hospital, Washington, DC, USA

    Emily R. Winslow

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Corresponding author

Correspondence to Meghan G. Lubner.

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Conflict of interest

M Lubner: Prior Grant funding from Ethicon, Philips. The remaining authors do not have any disclosures. This data was previously presented as a scientific abstract at the virtual American Roentgen Ray Society meeting 2021; and as a scientific poster at the virtual Society of Abdominal Radiology meeting 2021.

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This study was approved by our Institutional Review board and was found to be HIPAA complaint. Thus, requirement for informed consent was waived.

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Awe, A.M., Vanden Heuvel, M.M., Yuan, T. et al. Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts. Abdom Radiol 47, 221–231 (2022). https://doi.org/10.1007/s00261-021-03289-0

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  • DOI: https://doi.org/10.1007/s00261-021-03289-0

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