Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs

Abstract

Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs), pancreatic cysts requiring surgery, from low-risk lesions remains a clinical challenge, often resulting in unnecessary procedures due to limited specificity of current methods. While radiomics and deep learning (DL) have been explored for pancreatic cancer, cyst-level malignancy risk stratification of IPMNs remains untapped. We conducted a multi-institutional study (seven centers, 359 T2W MRI images) to assess the feasibility of AI for predicting IPMN dysplasia grade using cyst-level image features. We developed and compared 2D and 3D radiomics-only, deep learning (DL)-only, and radiomics-DL fusion models, using expert radiologist scoring as a baseline reference. Model performance was evaluated using held-out test data. The radiomics-DL fusion model showed the highest discriminatory ability on the test set (AUC 0.692), outperforming the radiomics-only model (AUC 0.665). Expert accuracy varied widely (37.4%-66.7%). The fusion model integrating deep learning and radiomics features from routine T2W MRI (AUC: 0.692) demonstrates potential for objective, cyst-level risk stratification of IPMNs in a multi-center cohort, outperforming both radiomics-only models and expert radiologists. While performance requires improvement for standalone clinical use, this approach offers a scalable, non-invasive method to potentially improve diagnostic accuracy and reduce unnecessary surgical interventions.

Figure 1

UMAP of quality indicators (projected into x and y axes from 21 quality indicators) per center using different normalization methods. Centers: Mayo Clinic Florida (MCF), Mayo Clinic Arizona (MCA), Northwestern Memorial Hospitals (NMH), New York University (NYU), Allegheny Health Network (AHN), Istanbul University (IU) Hospital, and Erasmus Medical Center (EMC).

Figure 2

Representative T2-weighted (T2W) images and reference segmentations of high-grade and low-grade IPMNs. The first row shows T2W MRI images, and the second row shows reference segmentations of high-grade and low-grade, main-duct (MD)-IPMN and branch-duct (BD)-IPMN cases.

Figure 3

Receiver operating characteristic curve of 2D and 3D radiomics predictions distinguishing between Low and High-Risk groups in the testing set.

Figure 4

Low vs High Risk classification comparison in cross-validation set’s mean (%) AUC, accuracy, and F1 for 2D and 3D radiomic analyses. Means are written above the error bars, and error bars show standard deviation.

Figure 5

Low vs High Risk classification comparison of mean (%) AUC, accuracy, and F1 between cross-validation and testing trials for 2D and 3D radiomic analyses. Values above each bar represent the mean value. Error bars show standard deviation.

Figure 6

Diagram of patient selection, data set curation, and radiomics and deep learning (DL) pipelines. (A) 746 patients were selected and received MRI imaging from seven centers between three countries; images were then preprocessed and manually segmented. (B) 2D and 3D radiomic features were extracted and classified using a random forest algorithm. (C) A DL-only analysis was conducted, then we developed a radiomics-DL fusion algorithm.

Figure 7

Flowchart of the subject selection and classification.