Study population and inclusion criteriaA retrospective analysis of patients with IPMNs who underwent surgical treatment at the First Hospital of Jilin University from January 2013 to January 2023 was conducted. Patients were categorized into benign and malignant groups on the basis of postoperative pathological results. The data collected included demographic, clinical, and radiological parameters. The exclusion criteria were not receiving neoadjuvant therapy prior to surgery and not undergoing contrast-enhanced CT scanning within our facility. A total of 121 patients were deemed suitable for inclusion in the study.We confirm that all methods employed in this study strictly adhered to relevant guidelines and regulations. The research protocols had been previously reviewed and approved by the Ethics Committee of the First Hospital of Jilin University, ensuring compliance with established ethical standards. Furthermore, informed consent was obtained from all individual participants and/or their legal guardians prior to their involvement in the study.Image acquisitionIn this study, preoperative pancreatic contrast-enhanced CT examinations were conducted with three different models of spiral CT machines: PHILIPS Ict 64/256, SIEMENS Cardiac 64, and GE Revolution 64 (the choice of machine was likely based on the availability and performance characteristics of these machines to ensure the acquisition of the most accurate images). The specific details regarding image acquisition are as follows: Scanning range extended from the xiphoid process level to the umbilical level, covering the entire abdomen, particularly the region containing the pancreas. This comprehensive coverage ensured a complete dataset for diagnostic purposes. Scan parameter settings: Tube voltage (influencing image contrast), 120 kVp; tube current, adjusted automatically to accommodate variations in patient size; pitch (determining the distance the CT machine moves per complete rotation), 5; matrix (defining image resolution), 521 × 521; and reconstructed slice thickness, between 1 and 1.5 mm, with thinner slices providing higher image resolution. All images were processed to create 1-mm-thick, thin-slice CT images to increase image clarity. The images were archived in DICOM format, the standard for medical images in diagnosis and research. These detailed parameters and processing steps are crucial to ensuring the accuracy and repeatability of the study, especially in analysing patients with IPMNs who are undergoing hepato-pancreato-biliary surgical treatment. Accurate image data are fundamental for diagnosis and research in this context.Collection of radiological features in CT imagesTo ensure the accuracy and reliability of the radiological data, each CT image underwent meticulous independent review by two radiologists, each with over a decade of experience in clinical image analysis. Discrepancies between their initial assessments prompted a structured discussion, complemented by an additional review from a senior physician, to achieve a consensus on the evaluations. This rigorous procedure was imperative for the precise identification, classification, and localization of IPMN lesions, as well as for evaluating critical indicators such as dilation of the main pancreatic duct (MPD). The incorporation of such detailed and expert-driven analysis guarantees the robustness and reliability of the radiological features collected for subsequent analyses.Radiomic analysis of CT imagesAll patient images in DICOM format were imported into RIAS software and meticulously processed by two radiologists with two and ten years of clinical reading experience. These radiologists carefully delineated the complete and surrounding boundaries of the pancreas and the lesion, layer by layer, to generate three-dimensional volumes of interest (VOIs). This manual process ensured a precise depiction of the pancreas and lesions while avoiding major blood vessels and dilated bile ducts adjacent to the lesion. In cases of evaluative discrepancies, the radiologists engaged in discussions to reach a consensus. After completing the delineation, the image regions of interest (ROIs) were saved in an independent “nii” file format and categorized on the basis of their benign or malignant characteristics for further analysis.RIAS software uses standardization technology to extract radiological features within the region of interest.These feature typically encompass four categories: (1) shape-based characteristics; (2) first-order intensity attributes; (3) second-order texture properties; and (4) high-order texture features. Shape-based characteristics primarily include tumour volume, maximum diameter, surface area, tumour density, and sphericity. The first-order intensity feature is derived from the histogram, with evaluation of the ROI on the basis of the mean, median, maximum, minimum, kurtosis, entropy, and skewness values of the voxel intensity. Texture features are computed via various grey matrices, such as the grey co-occurrence matrix (GLCM), and grey dependence matrix (GLDM). Higher-order features are typically generated via wavelet or Laplacian Gaussian filtering. After extraction, a standardization process was applied to ensure consistent scaling across all the features. The LASSO algorithm was employed for dimensionality reduction, selecting the most significant parameters pertinent to pancreatic lesions.The comprehensive dataset was divided into a cross-validation set and a test set at a 7:3 ratio. Selected radiomic features were utilized to construct predictive models via a random forest classifier (RFC), support vector machine (SVM), and logistic regression (LR) on the RIAS platform, ensuring efficient data classification and prediction. The SVM model, which demonstrated superior performance, was selected for validation. A fivefold cross-validation method involving receiver operating characteristic (ROC) curve plotting and evaluation of metrics such as the area under the curve (AUC), accuracy, sensitivity, specificity, and other relevant indicators was applied to the cross-validation set toassess model performance.Separate radiomic models for the arterial and venous phases were developed and compared against various evaluation indicators to determine the optimal predictive model. The model exhibiting the best prediction performance was subsequently integrated with selected clinical features to increase diagnostic accuracy and efficacy.Statistical analysisIn this study, statistical analysis was performed on data collected from 121 patients using IBM SPSS 26.0 software. Continuous variables, including patient attributes such as height, weight, and lesion size, were evaluated. Differences in categorical variables, such as sex, lesion type, pancreatic duct dilation, and the solid-cystic nature of the lesion, were also evaluated. For normally distributed metric data (e.g., patient age, lesion diameter, and volume), mean values and standard deviations are reported. Nonnormally distributed data, are reported as median values along with upper and lower quartiles. Independent sample t tests were used to compare two sets of data for differences, whereas Mann-Whitney U tests were used for nonnormally distributed data. ROC curve analysis and the Delong test were employed to compare the diagnostic performance of radiomic models from different phases and models combining selected clinical features. The chi-square test and corrected chi-square test were used to validate the results, with a p-value less than 0.05 indicating significant differences between groups.
