Prognostic factors for survival after pancreaticoduodenectomy for patients with carcinoma of the pancreatic head region

ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.

Standard vs extended lymphadenectomy associated with pancreaticoduodenectomy for periampullary adenocarcinoma: A meta-analysis

AIM: To review and evaluate the therapeutic efficacy of extended lymph node dissection (ELND) associated with pancreaticoduodenectomy for periampullary adenocarcinoma compared to conventional standard lymph node dissection (SLND).

METHODS: The Cochrane Library and Controlled Trials Registry (1991-2010), Medline Ovid (1956-2010), PubMed (1977-2010), Science Direct (1956-2010), and Web of Knowledge (1956-2010) were electronically searched for randomized clinical trials (RCTs) that compared the therapeutic endpoints of SLND vs ELND associated with pancreaticoduodenectomy for periampullary adenocarcinoma patients, with English language as a limit. Meta-analysis was performed using RevMan 5.0.22 software.

RESULTS: Five RCTs involving 673 patients were included for pooled analysis. Meta-analysis showed that ELND were associated with longer operative time and higher number of dissected lymph nodes than SLND. The postoperative morbidity and mortality were similar when the two groups were compared except a significant lower frequency of delayed gastric emptying in the SLND group. There was no significant difference between two groups regarding long-term survival.

CONCLUSION: ELND appears not to improve the long-term survival of patients with periampullary cancer although a significant higher number of lymph nodes dissected were observed in the ELND group.