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 pa...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.展开更多
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 patien...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.展开更多
The discovery of chaos in the sixties of last century was a breakthrough in concept,revealing the truth that some disorder behavior,called chaos,could happen even in a deterministic nonlinear system under barely deter...The discovery of chaos in the sixties of last century was a breakthrough in concept,revealing the truth that some disorder behavior,called chaos,could happen even in a deterministic nonlinear system under barely deterministic disturbance.After a series of serious studies,people begin to acknowledge that chaos is a specific type of steady state motion other than the conventional periodic and quasi-periodic ones,featuring a sensitive dependence on initial conditions,resulting from the intrinsic randomness of a nonlinear system itself.In fact,chaos is a collective phenomenon consisting of massive individual chaotic responses,corresponding to different initial conditions in phase space.Any two adjacent individual chaotic responses repel each other,thus causing not only the sensitive dependence on initial conditions but also the existence of at least one positive top Lyapunov exponent(TLE) for chaos.Meanwhile,all the sample responses share one common invariant set on the Poincaré map,called chaotic attractor,which every sample response visits from time to time ergodically.So far,the existence of at least one positive TLE is a commonly acknowledged remarkable feature of chaos.We know that there are various forms of uncertainties in the real world.In theoretical studies,people often use stochastic models to describe these uncertainties,such as random variables or random processes.Systems with random variables as their parameters or with random processes as their excitations are often called stochastic systems.No doubt,chaotic phenomena also exist in stochastic systems,which we call stochastic chaos to distinguish it from deterministic chaos in the deterministic system.Stochastic chaos reflects not only the intrinsic randomness of the nonlinear system but also the external random effects of the random parameter or the random excitation.Hence,stochastic chaos is also a collective massive phenomenon,corresponding not only to different initial conditions but also to different samples of the random parameter or the random excitation.Thus,the unique common feature of deterministic chaos and stochastic chaos is that they all have at least one positive top Lyapunov exponent for their chaotic motion.For analysis of random phenomena,one used to look for the PDFs(Probability Density Functions) of the ensemble random responses.However,it is a pity that PDF information is not favorable to studying repellency of the neighboring chaotic responses nor to calculating the related TLE,so we would rather study stochastic chaos through its sample responses.Moreover,since any sample of stochastic chaos is a deterministic one,we need not supplement any additional definition on stochastic chaos,just mentioning that every sample of stochastic chaos should be deterministic chaos.We are mainly concerned with the following two basic kinds of nonlinear stochastic systems,i.e.one with random variables as its parameters and one with ergodical random processes as its excitations.To solve the stochastic chaos problems of these two kinds of systems,we first transform the original stochastic system into their equivalent deterministic ones.Namely,we can transform the former stochastic system into an equivalent deterministic system in the sense of mean square approximation with respect to the random parameter space by the orthogonal polynomial approximation,and transform the latter one simply through replacing its ergodical random excitations by their representative deterministic samples.Having transformed the original stochastic chaos problem into the deterministic chaos problem of equivalent systems,we can use all the available effective methods for further chaos analysis.In this paper,we aim to review the state of art of studying stochastic chaos with its control and synchronization by the above-mentioned strategy.展开更多
文摘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.
文摘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.
基金Project supported by National Natural Science Foundation of China (10872165)Northwestern Polytechnical University (CX200712)
文摘The discovery of chaos in the sixties of last century was a breakthrough in concept,revealing the truth that some disorder behavior,called chaos,could happen even in a deterministic nonlinear system under barely deterministic disturbance.After a series of serious studies,people begin to acknowledge that chaos is a specific type of steady state motion other than the conventional periodic and quasi-periodic ones,featuring a sensitive dependence on initial conditions,resulting from the intrinsic randomness of a nonlinear system itself.In fact,chaos is a collective phenomenon consisting of massive individual chaotic responses,corresponding to different initial conditions in phase space.Any two adjacent individual chaotic responses repel each other,thus causing not only the sensitive dependence on initial conditions but also the existence of at least one positive top Lyapunov exponent(TLE) for chaos.Meanwhile,all the sample responses share one common invariant set on the Poincaré map,called chaotic attractor,which every sample response visits from time to time ergodically.So far,the existence of at least one positive TLE is a commonly acknowledged remarkable feature of chaos.We know that there are various forms of uncertainties in the real world.In theoretical studies,people often use stochastic models to describe these uncertainties,such as random variables or random processes.Systems with random variables as their parameters or with random processes as their excitations are often called stochastic systems.No doubt,chaotic phenomena also exist in stochastic systems,which we call stochastic chaos to distinguish it from deterministic chaos in the deterministic system.Stochastic chaos reflects not only the intrinsic randomness of the nonlinear system but also the external random effects of the random parameter or the random excitation.Hence,stochastic chaos is also a collective massive phenomenon,corresponding not only to different initial conditions but also to different samples of the random parameter or the random excitation.Thus,the unique common feature of deterministic chaos and stochastic chaos is that they all have at least one positive top Lyapunov exponent for their chaotic motion.For analysis of random phenomena,one used to look for the PDFs(Probability Density Functions) of the ensemble random responses.However,it is a pity that PDF information is not favorable to studying repellency of the neighboring chaotic responses nor to calculating the related TLE,so we would rather study stochastic chaos through its sample responses.Moreover,since any sample of stochastic chaos is a deterministic one,we need not supplement any additional definition on stochastic chaos,just mentioning that every sample of stochastic chaos should be deterministic chaos.We are mainly concerned with the following two basic kinds of nonlinear stochastic systems,i.e.one with random variables as its parameters and one with ergodical random processes as its excitations.To solve the stochastic chaos problems of these two kinds of systems,we first transform the original stochastic system into their equivalent deterministic ones.Namely,we can transform the former stochastic system into an equivalent deterministic system in the sense of mean square approximation with respect to the random parameter space by the orthogonal polynomial approximation,and transform the latter one simply through replacing its ergodical random excitations by their representative deterministic samples.Having transformed the original stochastic chaos problem into the deterministic chaos problem of equivalent systems,we can use all the available effective methods for further chaos analysis.In this paper,we aim to review the state of art of studying stochastic chaos with its control and synchronization by the above-mentioned strategy.
