Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.
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....
| Publicado en: | Abdominal Radiology Vol. 47; no. 1; pp. 221 - 232 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154792862&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154792862 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2022 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154792862 152962853 154792862 154792862 10.1007/s00261-021-03289-0 154792862 ppf: 221 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts. aug: au: Awe, Adam M. Vanden Heuvel, Michael M. Yuan, Tianyuan Rendell, Victoria R. Shen, Mingren Kampani, Agrima Liang, Shanchao Morgan, Dane D. Winslow, Emily R. Lubner, Meghan G. affil: Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, 53792, Madison, WI, USA sug: subj: Pancreatic Neoplasms Radiography Neoplasms, Cystic, Mucinous, and Serous Radiography Pancreatic Cyst Radiography Pancreatic Cyst Risk Factors Diagnosis, Computer Assisted Risk Assessment Machine Learning Algorithms Evaluation Noninvasive Procedures Tomography, X-Ray Computed Prediction Models Human Retrospective Design Record Review Pancreatic Cyst Pancreatic Neoplasms Pathology Pancreatic Cyst Surgery Pancreatic Neoplasms Surgery Surgical Patients Comparative Studies Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|