Publications by authors named "P Spaggiari"

The Esophageal Adenocarcinoma Study Group Europe (EACSGE) recently proposed a granular histologic classification of esophageal-esophago-gastric junctional adenocarcinomas (EA-EGJAs) based on the study of naïve surgically resected specimens that, when combined with the pTNM stage, is an efficient indicator of prognosis, molecular events, and response to treatment. In this study, we compared histologic classes of endoscopic biopsies taken before surgical resection with those of the surgical specimen, to evaluate the potential of the EACSGE classification at the initial diagnostic workup. A total of 106 EA-EGJA cases with available endoscopic biopsies and matched surgical resection specimens were retrieved from five Italian institutions.

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Article Synopsis
  • The study aimed to develop and validate an AI prediction system for assessing the risk of lymph node metastasis (LNM) in patients with T2 colorectal cancer (CRC), as traditional surgical approaches struggle with risk stratification.!* -
  • Data from over 700 patients was analyzed, revealing that the AI model had a moderate prediction performance with a sensitivity of 97.8% but a low specificity of 15.6%, indicating many false positives in LNM predictions.!* -
  • While the AI model shows promise for predicting LNM using basic clinical and pathologic data, improvements in accuracy could be achieved by training it with a larger and more diverse patient population from both Eastern and Western medical centers.!*
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Background/objectives: Inflammatory bowel disease (IBD) and eosinophilic gastrointestinal diseases (EGIDs) are complex, multifactorial chronic inflammatory disorders affecting the gastrointestinal tract. Their epidemiology, particularly for eosinophilic esophagitis (EoE), is increasing worldwide, with a rise in the co-diagnosis of IBD and EGIDs. Both disorders share common risk factors, such as early exposure to antibiotics or specific dietary habits.

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Introduction: Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities.

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