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Background: Preoperative vascular embolization is an effective strategy for managing meningiomas, neck paragangliomas, renal cell carcinomas, and bone metastasis by reducing the intraoperative bleeding volume and operation time. Although hypervascular tumors also occur in the pancreas, preoperative embolization for these tumors is not commonly practiced. We herein present a case of a giant serous cystic neoplasm (SCN) of the pancreas with significant arterial vascularity that was managed with preoperative interventional radiology and subsequently resected via pancreaticoduodenectomy. Case presentation: A 60-year-old man presented with an 8-cm hypervascular tumor located at the head of the pancreas, identified as an SCN on pathologic examination. The tumor had increased by 13 mm over 5 years, necessitating surgical intervention. Computed tomography revealed a substantial blood supply to the tumor from the dorsal pancreatic artery and gastroduodenal artery, both branches of the superior mesenteric artery. To mitigate the risk of severe intraoperative bleeding from this giant hypervascular tumor, branches of the dorsal pancreatic artery and gastroduodenal artery were embolized using metallic coils and further secured using a gelatin sponge 1 day prior to pancreatectomy. During the laparotomy, the tumor appeared to have decreased in size, likely because of reduced distension and congestion. Despite significant adhesions to surrounding tissues secondary to prolonged compression and inflammation, the pancreaticoduodenectomy was completed successfully in 5 h and 15 min with blood loss of 763 mL. The patient was discharged on postoperative day 15 without complications. Conclusions: Preoperative arterial embolization for hypervascular pancreatic tumors might control the risk of massive intraoperative bleeding, contributing to a favorable postoperative outcome. Utilizing interventional radiology for preoperative inflow control is one of the beneficial strategies for pancreatectomy in patients with a giant SCN. [ABSTRACT FROM AUTHOR]

Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package. Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals. SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record. [ABSTRACT FROM AUTHOR]

With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients.

Objective: Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection., Methods: Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package., Results: Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5-160 mm), with a mean follow-up of 72 months (range 3-266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals., Conclusion: SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record., Competing Interests: Declaration of competing interest The authors have no disclosures to report., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Pancreatic serous cystic neoplasm (SCN) is usually benign and is often managed using imaging surveillance if asymptomatic. It has a higher incidence in females but is rare in younger age groups. Acute hemorrhagic complications associated with SCN are infrequent. Whether asymptomatic SCN can cause acute hemorrhage, especially in women of childbearing age, is not well-established. A 30-year-old Japanese female, who was six months postpartum and under surveillance for asymptomatic pancreatic SCN, presented to the emergency department with gradually worsening left lateral abdominal pain. Regular ultrasound revealed no change in SCN size; however, no imaging surveillance had been conducted over the past two years. She had pain in the entire abdomen, which intensified around the navel and elicited guarding. Abdominal contrast-enhanced computed tomography revealed a cystic mass in the pancreatic tail with a contrast blush within the cyst and an adjacent retroperitoneal hematoma. Endovascular embolization was performed to control the hemorrhage. The patient had an uneventful medical recovery and was discharged five days after embolization. Five months after discharge, she underwent laparoscopic distal pancreatectomy and splenectomy as an elective surgery and was discharged uneventfully. Even with periodic imaging surveillance, pancreatic SCN can suddenly cause spontaneous hemorrhage. Clinicians should be aware that pancreatic SCN can potentially cause life-threatening complications, including spontaneous hemorrhage. We report a case of an unexpected complication with spontaneous hemorrhage in a young woman who was under imaging surveillance for pancreatic SCN. The patient was successfully treated with angioembolization and planned laparoscopic surgery. • Pancreatic serous cystic neoplasm (SCN) can cause rare spontaneous hemorrhage, even in younger patients. • Spontaneous hemorrhage of pancreatic SCN can be life threatening. • The effect of pregnancy on pancreatic SCN has not been elucidated but warrants attention. [ABSTRACT FROM AUTHOR]

With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)