Evaluación del papel del conducto pancreático principal en la gradación de neoplasias mucinosas papilares intraductales: un enfoque de aprendizaje automático basado en radiómica multiestructura
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Published on: February 1, 2020
Emir A Syailendra1, Felipe Lopez-Ramirez1, Florent Tixier1
1Russel H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.
Purpose:
The purpose of this study was to evaluate the contribution of radiomics features extracted from various pancreatic structures on computed tomography (CT) images, including the main pancreatic duct and cystic lesion, for predicting the pathological grade of intraductal papillary mucinous neoplasms (IPMNs) using machine learning models.
Materials And Methods:
A retrospective study using preoperative CT images obtained during the venous phase of enhancement in patients with pathologically confirmed IPMNs (2003-2024) was conducted. Main pancreatic ducts and cysts were manually segmented. Machine learning models were trained to classify IPMNs into high-grade/associated invasive carcinoma (HG/I) IPMNs or low-grade (LG) IPMNs using radiomics features from three structures (i.e., cysts, main pancreatic duct, and a combination of both structures). Model performance was evaluated using area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) values were used to interpret feature importance.
Results:
A total of 274 patients with IPMNs were included. There were 149 patients with HG/I IPMNs (70 women [47 %]; median age, 71.0 years; age range: 29-92) and 125 patients with LG IPMNs (73 women [58.4 %]; median age, 68.0 years; range: 35-86). HG/I IPMNs were predominantly mixed-type IPMNs (51.7 %; 77/149). LG IPMNs were unspecified (51/125; 40.8 %), main/mixed (40/125; 32 %), or branch-duct type (34/125; 27.2 %). A support vector machine trained on combined features achieved the largest AUC (0.85; 95 % confidence interval [CI]: 0.85-0.87; P < 0.001), with 90 % sensitivity (95 % CI: 90-93), and 60 % specificity (95 % CI: 58-62). SHAP analysis identified main pancreatic duct radiomics features as having the largest contribution to model output.
Conclusion:
Integrating CT-based radiomics features from pancreatic ducts and cysts improves classification performance, with main pancreatic duct features being the most contributive predictor of IPMN grade.
