AI Summary & Key Takeaways
AI InsightCONCLUSION Shape_Sphericity in combination with age, sex, and location could help to distinguish MCN from SOA.
📌 3 Key Takeaways
Abstract
PURPOSE We aimed to assess the performance of quantitative 3D shape analysis in the differential diagnosis of pancreatic serous oligocystic adenoma (SOA) and mucinous cystic neoplasm (MCN). METHODS Four hundred thirty-two patients diagnosed with serous cystic neoplasms (SCNs) or MCNs were retrospectively reviewed from August 2014 to July 2019 and finally 87 patients with MCNs (n = 45) and SOAs (n = 42) were included. Clinical data and magnetic resonance morphologic features with 3D shape analysis of lesions (shape sphericity, compacity, and volume) were recorded and compared between MCNs and SOAs according to the pathology. Univariable and multivariable regression analyses were used to identify independent impact factors for differentiating MCN from SOA. RESULTS The age of MCN patients was younger than SOAs (43.02 ± 10.83 years vs. 52.78 ± 12.31 years; OR = 0.275; 95% CI: 0.098-0.768; P = .014). MCN has a higher female/male ratio than SOA (43/2 vs. 27/15; OR = 40.418; 95% CI: 2.704-604.171; P = .007) and was more often located in the distal of pancreas (OR = 31.403; 95% CI: 2.985-330.342; P = .004). Shape_Sphericity derived from 3D shape analysis was a significant independent factor in the multivariable analysis and the value of MCN was closer to 1 than SOA (OR = 35.153; 95% CI: 5.301-237.585; P < .001). Area under the receiver operating characteristic curve (AUC) of Shape_Sphericity was 0.923 (optimal cutoff value was 0.964876). CONCLUSION Shape_Sphericity in combination with age, sex, and location could help to distinguish MCN from SOA.