Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

[PURPOSE] Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-enhanced ultrasound, as both exhibit similar arterial-phase hyperenhancement, precluding reliable visual distinction. Crucially, the hypervascular PDAC subtype is associated with a more favorable prognosis, rendering its accurate identification from its benign inflammatory mimic (MFP) a clinically significant priority for early and appropriate intervention. This study aimed to develop and validate a small-sample-oriented machine learning framework leveraging quantitative time-intensity curve (TIC) features to achieve this precise differentiation.

[MATERIALS AND METHODS] We retrospectively included 152 patients with pathologically confirmed lesions who underwent CEUS between September 2017 and April 2024 (85 hypervascular PDAC, 67 MFP). A temporally separated split was used: 122 patients (2017-2022) formed the training cohort and 30 patients (2023-2024) served as the internal test cohort. Paired TICs were generated from the lesion and adjacent normal pancreatic parenchyma, and 22 quantitative difference/ratio features describing enhancement amplitude, temporal kinetics and curve morphology were extracted. Based on independent-samples t-tests and clinical interpretability, five representative TIC features were selected to train six classical classifiers (logistic regression, support vector machine, k-nearest neighbors, random forest, naïve Bayes, and decision tree). Model performance was assessed by stratified 10-fold cross-validation on the training cohort and by testing on the temporally separated cohort.

[RESULTS] In nested cross-validation, all models achieved AUCs of approximately 0.89-0.92. On the test cohort, AUCs ranged from about 0.83 to 0.92, with logistic regression performing best (AUC 0.915). Overall, the six classifiers showed broadly comparable discrimination.

[CONCLUSION] A machine-learning model built on a small set of physiologically interpretable CEUS-TIC features can provide stable and explainable quantitative support for differentiating hypervascular PDAC from MFP, even under limited-sample conditions.