Treatment patterns and survival outcomes in patients with nonmetastatic early-onset pancreatic cancer

BACKGROUND The incidence of patients with early-onset pancreatic cancer(EOPC;age≤50 years at diagnosis)is on the rise,placing a heavy burden on individuals,families,and society.The role of combination therapy including surgery,radiotherapy,and chemotherapy in non-metastatic EOPC is not well-defined.AIM To investigate the treatment patterns and survival outcomes in patients with non-metastatic EOPC.METHODS A total of 277 patients with non-metastatic EOPC who were treated at our institution between 2017 and 2021 were investigated retrospectively.Overall survival(OS),disease-free survival,and progression-free survival were estimated using the Kaplan-Meier method.Univariate and multivariate analyses with the Cox proportional hazards model were used to identify prognostic factors.RESULTS With a median follow-up time of 34.6 months,the 1-year,2-year,and 3-year OS rates for the entire cohort were 84.3%,51.5%,and 27.6%,respectively.The median OS of patients with localized disease who received surgery alone and adjuvant therapy(AT)were 21.2 months and 28.8 months,respectively(P=0.007).The median OS of patients with locally advanced disease who received radiotherapy-based combination therapy(RCT),surgery after neoadjuvant therapy(NAT),and chemotherapy were 28.5 months,25.6 months,and 14.0 months,respectively(P=0.002).The median OS after regional recurrence were 16.0 months,13.4 months,and 8.9 months in the RCT,chemotherapy,and supportive therapy groups,respectively(P=0.035).Multivariate analysis demonstrated that carbohydrate antigen 19-9 level,pathological grade,T-stage,N-stage,and resection were independent prognostic factors for non-metastatic EOPC.CONCLUSION AT improves postoperative survival in localized patients.Surgery after NAT and RCT are the preferred therapeutic options for patients with locally advanced EOPC.

Risk factors,prognostic predictors,and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis

BACKGROUND Liver metastasis(LM)remains a major cause of cancer-related death in patients with pancreatic cancer(PC)and is associated with a poor prognosis.Therefore,identifying the risk and prognostic factors in PC patients with LM(PCLM)is essential as it may aid in providing timely medical interventions to improve the prognosis of these patients.However,there are limited data on risk and prognostic factors in PCLM patients.AIM To investigate the risk and prognostic factors of PCLM and develop corresponding diagnostic and prognostic nomograms.METHODS Patients with primary PC diagnosed between 2010 and 2015 were reviewed from the Surveillance,Epidemiology,and Results Database.Risk factors were identified using multivariate logistic regression analysis to develop the diagnostic mode.The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors needed to develop the prognostic model.The performance of the two nomogram models was evaluated using receiver operating characteristic(ROC)curves,calibration plots,decision curve analysis(DCA),and risk subgroup classification.The Kaplan-Meier method with a logrank test was used for survival analysis.RESULTS We enrolled 33459 patients with PC in this study.Of them,11458(34.2%)patients had LM at initial diagnosis.Age at diagnosis,primary site,lymph node metastasis,pathological type,tumor size,and pathological grade were identified as independent risk factors for LM in patients with PC.Age>70 years,adenocarcinoma,poor or anaplastic differentiation,lung metastases,no surgery,and no chemotherapy were the independently associated risk factors for poor prognosis in patients with PCLM.The C-index of diagnostic and prognostic nomograms were 0.731 and 0.753,respectively.The two nomograms could accurately predict the occurrence and prognosis of patients with PCLM based on the observed analysis results of ROC curves,calibration plots,and DCA curves.The prognostic nomogram could stratify patients into prognostic groups and perform well in internal validation.CONCLUSION Our study identified the risk and prognostic factors in patients with PCLM and developed corresponding diagnostic and prognostic nomograms to help clinicians in subsequent clinical evaluation and intervention.External validation is required to confirm these results.

Survival benefits and disparities in radiation therapy for elderly patients with pancreatic ductal adenocarcinoma

BACKGROUND Older patients represent a unique subgroup of the cancer patient population,for which the role of cancer therapy requires special consideration.However,the outcomes of radiation therapy(RT)in elderly patients with pancreatic ductal adenocarcinoma(PDAC)are not well-defined in the literature.AIM To explore the use and effectiveness of RT in the treatment of elderly patients with PDAC in clinical practice.METHODS Data from patients with PDAC aged≥65 years between 2004 and 2018 were collected from the Surveillance,Epidemiology,and End Results database.Multivariate logistic regression analysis was performed to determine factors associated with RT administration.Overall survival(OS)and cancer-specific survival(CSS)were evaluated using the Kaplan–Meier method with the log-rank test.Univariate and multivariate analyses with the Cox proportional hazards model were used to identify prognostic factors for OS.Propensity score matching(PSM)was applied to balance the baseline characteristics between the RT and non-RT groups.Subgroup analyses were performed based on clinical characteristics.RESULTS A total of 12245 patients met the inclusion criteria,of whom 2551(20.8%)were treated with RT and 9694(79.2%)were not.The odds of receiving RT increased with younger age,diagnosis in an earlier period,primary site in the head,localized disease,greater tumor size,and receiving chemotherapy(all P<0.05).Before PSM,the RT group had better outcomes than did the non-RT group[median OS,14.0 vs 6.0 mo;hazard ratio(HR)for OS:0.862,95%confidence interval(CI):0.819–0.908,P<0.001;and HR for CSS:0.867,95%CI:0.823–0.914,P<0.001].After PSM,the survival benefit associated with RT remained comparable(median OS:14.0 vs 11.0 mo;HR for OS:0.818,95%CI:0.768–0.872,P<0.001;and HR for CSS:0.816,95%CI:0.765–0.871,P<0.001).Subgroup analysis revealed that the survival benefits(OS and CSS)of RT were more significant in patients aged 65 to 80 years,in regional and distant stages,with no surgery,and receiving chemotherapy.CONCLUSION RT improved the outcome of elderly patients with PDAC,particularly those aged 65 to 80 years,in regional and distant stages,with no surgery,and who received chemotherapy.Further prospective studies are warranted to validate our results.

A Primal-Dual Algorithm for the Generalized Prize-Collecting Steiner Forest Problem

In this paper,we consider the generalized prize-collecting Steiner forest problem,extending the prize-collecting Steiner forest problem.In this problem,we are given a connected graph G=(V,E)and a set of vertex sets V={V1,V2,…,Vl}.Every edge in E has a nonnegative cost,and every vertex set in V has a nonnegative penalty cost.For a given edge set F⊆E,vertex set Vi∈V is said to be connected by edge set F if Vi is in a connected component of the F-spanned subgraph.The objective is to find such an edge set F such that the total edge cost in F and the penalty cost of the vertex sets not connected by F is minimized.Our main contribution is to give a 3-approximation algorithm for this problem via the primal-dual method.

An Improved Approximation Algorithm for the k-Level Facility Location Problem with Soft Capacities

We consider the k-level facility location problem with soft capacities(k-LFLPSC). In the kLFLPSC, each facility i has a soft capacity u_i along with an initial opening cost f_i ≥ 0, i.e., the capacity of facility i is an integer multiple of u_i incurring a cost equals to the corresponding multiple of f_i. We firstly propose a new bifactor(ln(1/β)/(1-β), 1 + 2/(1-β))-approximation algorithm for the k-level facility location problem(k-LFLP), where β∈(0, 1) is a fixed constant. Then, we give a reduction from the k-LFLPSC to the k-LFLP. The reduction together with the above bifactor approximation algorithm for the k-LFLP imply a5.5053-approximation algorithm for the k-LFLPSC which improves the previous 6-approximation.