Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

胰腺癌病理类型和影像学特征的回顾性分析:一项综合研究

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Abstract

BACKGROUND: Pancreatic cancer remains one of the most lethal malignancies worldwide, with a poor prognosis often attributed to late diagnosis. Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning. AIM: To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features. METHODS: We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution. Pathological types were determined by histopathological examination of the surgical specimens or biopsy samples. The imaging features were assessed using computed tomography, magnetic resonance imaging, and endoscopic ultrasound. Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics. RESULTS: There were 320 (64%) cases of pancreatic ductal adenocarcinoma, 75 (15%) of intraductal papillary mucinous neoplasms, 50 (10%) of neuroendocrine tumors, and 55 (11%) of other rare types. Distinct imaging features were identified in each pathological type. Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography, whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules. Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging. Statistical analysis revealed significant correlations between specific imaging features and pathological types (P < 0.001). CONCLUSION: This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features. These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

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