Background The efficacy of surgery for invasive mucinous neoplasms is unclear. We examined the na... more Background The efficacy of surgery for invasive mucinous neoplasms is unclear. We examined the natural history of invasive mucinous cystic neoplasms (MCN) and invasive intraductal papillary mucinous neoplasms (IPMN) in patients who underwent pancreatic resection. Methods The Surveillance, Epidemiology, and End Results (SEER) database (1996–2006) was queried for cases of resected invasive MCN and IPMN. Demographics, tumor characteristics, and overall survival were examined using log-rank analysis and multivariate Cox regression model. Results Of 185 MCN cases and 641 IPMN cases, 73% and 48%, respectively, were women (P P P P = 0.0005), and it was better for patients with node-negative MCN vs. node-negative IPMN (P = 0.0061). There was no significant difference in survival of patients with stage IIA MCN vs. stage IIA IPMN (P = 0.5964), stage IIB MCN vs. stage IIB IPMN (P = 0.2262), or node-positive MCN vs. node-positive IPMN (P = 0.2263). Age older than 65 years (hazards ratio (HR) 1.71, P = 0.0046), high tumor grade (HR 2.68, P P P = 0.0040) predicted worse outcome in node-negative patients. Conclusions Our findings suggest that survival is better after resection of invasive MCN versus invasive IPMN when disease is localized within the pancreas, but this difference disappears in the presence of nodal metastasis or extrapancreatic extension.
In recent years, clustering with constraints has become a topic of significant interest for many ... more In recent years, clustering with constraints has become a topic of significant interest for many researchers because it allows to take into account the knowledge from the domain, expressed as a set of constraints, and thus to improve the efficiency of the analysis. For example, these approaches can take place in an interactive process where a user iteratively expresses new constraints to refine previous clustering results. In this paper, we propose three new variants of the leader ant clustering with constraint algorithm (MCLA, MELA and CELA) that implements the following constraints: the must-link, cannot-link constraints and epsiv-constraints. These algorithms have been compared to other constraint based clustering algorithms such as K-means clustering with constraints and the original leader ant clustering algorithm. Our experiments show that, on UCI machine learning and artificial data sets, our approach compares well to the other algorithms.
Background The efficacy of surgery for invasive mucinous neoplasms is unclear. We examined the na... more Background The efficacy of surgery for invasive mucinous neoplasms is unclear. We examined the natural history of invasive mucinous cystic neoplasms (MCN) and invasive intraductal papillary mucinous neoplasms (IPMN) in patients who underwent pancreatic resection. Methods The Surveillance, Epidemiology, and End Results (SEER) database (1996–2006) was queried for cases of resected invasive MCN and IPMN. Demographics, tumor characteristics, and overall survival were examined using log-rank analysis and multivariate Cox regression model. Results Of 185 MCN cases and 641 IPMN cases, 73% and 48%, respectively, were women (P P P P = 0.0005), and it was better for patients with node-negative MCN vs. node-negative IPMN (P = 0.0061). There was no significant difference in survival of patients with stage IIA MCN vs. stage IIA IPMN (P = 0.5964), stage IIB MCN vs. stage IIB IPMN (P = 0.2262), or node-positive MCN vs. node-positive IPMN (P = 0.2263). Age older than 65 years (hazards ratio (HR) 1.71, P = 0.0046), high tumor grade (HR 2.68, P P P = 0.0040) predicted worse outcome in node-negative patients. Conclusions Our findings suggest that survival is better after resection of invasive MCN versus invasive IPMN when disease is localized within the pancreas, but this difference disappears in the presence of nodal metastasis or extrapancreatic extension.
In recent years, clustering with constraints has become a topic of significant interest for many ... more In recent years, clustering with constraints has become a topic of significant interest for many researchers because it allows to take into account the knowledge from the domain, expressed as a set of constraints, and thus to improve the efficiency of the analysis. For example, these approaches can take place in an interactive process where a user iteratively expresses new constraints to refine previous clustering results. In this paper, we propose three new variants of the leader ant clustering with constraint algorithm (MCLA, MELA and CELA) that implements the following constraints: the must-link, cannot-link constraints and epsiv-constraints. These algorithms have been compared to other constraint based clustering algorithms such as K-means clustering with constraints and the original leader ant clustering algorithm. Our experiments show that, on UCI machine learning and artificial data sets, our approach compares well to the other algorithms.
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