Purpose: Although there has been considerable effort to define pre-operative features to predict the malignant potential of intraductal papillary mucinous neoplasms (IPMNs), the prognostic value of pre-operative clinical and MRI features has not been assessed. The aim of this study was to determine pre-operative clinical and MRI features that are predictive of disease-specific death or recurrence in patients undergoing pancreatic resection for IPMNs.
Methods: We performed a retrospective analysis of 167 patients (mean age, 65 years; 114 men and 53 women) who underwent pre-operative MRI and surgical resection of IPMN of pancreas between 2009 and 2019. We evaluated disease-specific survival (DSS) and recurrence-free survival (RFS). Prognostic factor analysis was performed using clinical and MRI features according to the 2017 international consensus guidelines.
Results: Of 167 patients, 86 (51.5%) had benign IPMNs and 81 (48.5%) had malignant IPMNs (48 [28.7%] invasive carcinoma and 33 [19.8%] high grade). On multivariable analysis, mural nodule size (hazard ratio [HR], 1.11; 95% confidence interval [CI], 1.04-1.18 and HR 1.07; 95% CI 1.03-1.12) and obstructive jaundice (HR 5.01; 95% CI 1.44-17.46 and HR 5.60; 95% CI 2.42-12.99) were the significant variables that were associated with DSS and RFS. The presence of lymphadenopathy (HR 50.7; 95% CI 4.0-643.0; P = 0.002) was the significant factor for DSS. IPMNs with mural nodule showed a significantly lower 5-year DSS (83.7% vs. 100%, P value < 0.01) and RFS (73.1% vs. 95.0%, P value < 0.01) compared with IPMNs with no mural nodule.
Conclusions: Mural nodule size on MRI and obstructive jaundice were prognostic markers in the pre-operative evaluation of patients with IPMN of pancreas.
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http://dx.doi.org/10.1007/s00261-020-02627-y | DOI Listing |
J Integr Neurosci
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Laboratory for the Study of Tactile Communication, Pushkin State Russian Language Institute, 117485 Moscow, Russia.
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J Integr Neurosci
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School of Medicine, University of Galway, Galway, Ireland.
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J Clin Med
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Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
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