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

Autor:	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.
Předmět:	PANCREATIC cysts MACHINE learning RADIOMICS BOOSTING algorithms ROOT-mean-squares SURGICAL pathology PANCREATIC tumors ACQUISITION of data methodology RETROSPECTIVE studies SURGERY PATIENTS RISK assessment COMPARATIVE studies MEDICAL records DESCRIPTIVE statistics TUMORS COMPUTER-aided diagnosis COMPUTED tomography PREDICTION models ALGORITHMS DISEASE risk factors
Zdroj:	Abdominal Radiology; Jan2022, Vol. 47 Issue 1, p221-231, 11p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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