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Pancreatic cystic neoplasms are lesions comprised of cystic components that show different biological behaviors, epidemiology, clinical manifestations, imaging features, and malignant potential and management. Benign cystic neoplasms include serous cystic neoplasms (SCAs). Other pancreatic cystic lesions have malignant potential, such as intraductal papillary mucinous neoplasms and mucinous cystic neoplasms. SCAs can be divided into microcystic (classic appearance), honeycomb, oligocystic/macrocystic, and solid patterns based on imaging appearance. They are usually solitary but may be multiple in von Hippel-Lindau disease, which may depict disseminated involvement. The variable appearances of SCAs can mimic other types of pancreatic cystic lesions, and cross-sectional imaging plays an important role in their differential diagnosis. Endoscopic ultrasonography has helped in improving diagnostic accuracy of pancreatic cystic lesions by guiding tissue sampling (biopsy) or cyst fluid analysis. Immunohistochemistry and newer techniques such as radiomics have shown improved performance for preoperatively discriminating SCAs and their mimickers., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Pancreatic serous cystic neoplasm (SCN) is a benign cystic neoplasm that is likely to be surgically resected due to preoperative misdiagnosis or tentative diagnosis even using endoscopic ultrasonography (EUS). We aimed to analyze EUS findings of SCN associated with misdiagnosis., Methods: Between January 2012 and September 2023, histologically confirmed pancreatic SCN were included and EUS features were reviewed., Results: Overall, 294 patients with 300 surgically resected SCNs were included. The median age of the patients was 51 years and 75.9% were females. The lesions were predominantly located in the body/neck/tail of the pancreas (63.0%). The overall preoperative diagnostic rate of SCN was 36.3%, with the most common misdiagnosis being intraductal papillary mucinous neoplasm (IPMN) (31.3%), while 16.3% remained undefined. The preoperative diagnostic rate of SCN varied across different endosonographic morphologies, with oligocystic, macrocystic, microcystic, and solid patterns yielding rates of 12.8%, 37.9%, 76.5%, and 19.2%, respectively. Notably, the presence of central scar and vascularity improved the diagnostic accuracy and correctly identified 41.4% and 52.3% of the lesions. While mucus or pancreatic duct (PD) communication significantly increased the likelihood of misdiagnosis, particularly as IPMN. Multivariate analysis revealed a morphological pattern, mucin-producing signs, wall thickening, vascularity, and PD communication were independent factors related to preoperative misdiagnosis, with an overall accuracy of 82.3%., Conclusions: Preoperative diagnosis of SCN remains challenging. The microcystic pattern emerged as a reliable feature, while mucin-producing signs, including mural nodules, mucus, and PD communication, pose diagnostic pitfalls despite the presence of typical central scar or vascularity commonly in SCN., (© 2024 Chinese Medical Association Shanghai Branch, Chinese Society of Gastroenterology, Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest.

Introduction: The rising incidence of incidental detection of pancreatic cystic neoplasms has compelled radiologists to determine new diagnostic methods for the differentiation of various kinds of lesions. We aim to demonstrate the utility of texture features extracted from ADC maps in differentiating intraductal papillary mucinous neoplasms (IPMN) from serous cystadenomas (SCA)., Methods: This retrospective study was performed on 136 patients (IPMN = 87, SCA = 49) split into testing and training datasets. A total of 851 radiomics features were extracted from volumetric contours drawn by an expert radiologist on ADC maps of the lesions. LASSO regression analysis was used to determine the most predictive set of features and a radiomics score was developed based on their respective coefficients. A hyper-optimized support vector machine was then utilized to classify the lesions based on their radiomics score., Results: A total of four Wavelet features (LHL/GLCM/LCM2, HLL/GLCM/LCM2, /LLL/First Order/90percent, /LLL/GLCM/MCC) were selected from all of the features to be included in our classifier. The classifier was optimized by altering hyperparameters and the trained model was applied to the validation dataset. The model achieved a sensitivity of 92.8, specificity of 90%, and an AUC of 0.97 in the training data set, and a sensitivity of 83.3%, specificity of 66.7%, and AUC of 0.90 in the testing dataset., Conclusion: A support vector machine model trained and validated on volumetric texture features extracted from ADC maps showed the possible beneficence of these features in differentiating IPMNs from SCAs. These results are in line with previous regarding the role of ADC maps in classifying cystic lesions and offers new evidence regarding the role of texture features in differentiation of potentially neoplastic and benign lesions., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)