Machine Learning-Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision-Making and Nursing Management.

Background: The noninvasive diagnosis of pancreatic lesions is a critical clinical challenge. This study aims to create machine learning (ML) radiomic models for differentiating pancreatic lesions and an integrated model for pancreatic ductal adenocarcinoma (PDAC) detection. Methods: 640 patients with pathologically confirmed malignant ( Results: All ML models effectively differentiated the three tumor types. The random forest algorithm showed the best performance, achieving an area under the curve (AUC) of 0.99 and 0.95 in the training and validation sets, respectively. CA19-9 was identified as an independent diagnostic factor for PDAC. The nomogram integrating radiomics and CA19-9 achieved an AUC of 0.89 and accuracy of 0.85 in the training set, with corresponding values of 0.85 and 0.82 in the validation set. Conclusions: Radiomics-based ML models effectively differentiated benign, borderline, and malignant pancreatic tumors. The nomogram combining radiomic features with CA19-9 demonstrated robust performance, showing considerable potential to streamline the diagnostic process and facilitate timely care planning for patients with suspected pancreatic cancer.