Development and validation of a clinical-radiomics nomogram for the differential diagnosis of focal pancreatic solid lesions: a retrospective cohort study.

Background: Accurate and early differentiation of focal pancreatic solid lesions (FPSLs) in the outpatient setting remains a major clinical challenge. Benign inflammatory conditions, such as focal autoimmune pancreatitis (fAIP) and mass-forming chronic pancreatitis (MFCP), often appear similar to pancreatic ductal adenocarcinoma (PDAC) in clinical features and conventional imaging findings, leading to diagnostic uncertainty and potential unnecessary pancreaticoduodenectomy. Current serum biomarkers lack accuracy, and invasive diagnostic procedures are limited by sampling variability, highlighting the need for a reliable, non-invasive triage tool suitable for outpatient care. Venous phase contrast-enhanced computed tomography (CECT) best captures pancreatic parenchymal and lesional enhancement patterns, and radiomics from this phase can quantify subtle, visually imperceptible differences in enhancement homogeneity, tissue heterogeneity, and periductal parenchymal remodelling. Therefore, this study aimed to develop and temporally validate an integrated model that combines venous phase CECT radiomic features with key clinical and laboratory variables to better differentiate FPSLs in an outpatient population. Methods: In this retrospective study, outpatients with FPSLs who underwent venous-phase CECT from May 2013 to May 2024 were consecutively enrolled, and diagnoses were based on international consensus criteria (fAIP), or cytology/surgery (MFCP and PDAC). The cohort was randomly divided into training and internal validation sets at a 7:3 ratio. Additionally, 11 fAIP patients and 19 PDAC patients were included in the independent temporal validation analysis. Clinical variables, including demographics, symptoms and laboratory parameters, were collected concurrently with imaging. Quantitative radiomics features were extracted from manually segmented lesions on CECT images. Model discrimination was assessed using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Results: The mean age of the three groups of FPSLs patients was 57.21±10.76 (fAIP), 48.25±12.14 (MFCP), and 60.55±9.66 (PDAC) years, respectively. The majority of patients were male, and the pancreatic head was the most common lesion location across all groups (P<0.01). For differentiating fAIP from PDAC, the combined clinical-radiomics nomogram demonstrated strong diagnostic performance, achieving an area under the curve (AUC) of 0.95, 0.91 and 0.88 in the training, internal validation, and temporal validation cohorts, respectively. Similar results were seen in distinguishing MFCP from PDAC. However, although the radiomics model showed initial promise in differentiating fAIP from MFCP in the training set, its performance declined in the validation set. Conclusions: Integrating CECT-based radiomic features with clinical data results in a compelling, non-invasive tool for characterizing FPSLs. Future investigations should prioritize the integration of multi-modal data streams to enhance diagnostic precision.