Aim: This study aimed to estimate the cure proportion and effects of related factors on colorectal cancer in Iranian patients after surgery. Background: Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the fourth leading cause of cancer death. The relative survival of CRC varies worldwide given the quality of care, including surgical techniques. Methods: This retrospective cohort study was conducted on 490 patients, aged 20– 94 years, with colorectal cancer. All the colorectal cancer patients undergoing surgery in Faghihi hospital, Shiraz University of Medical Sciences were prospectively followed-up for 8 years from 2008 to March 8, 2016. We used parametric cure model (mixture and non-mixture) to estimate the cure proportion and the adjusted hazard ration (HR) for colorectal cancer mortality after surgery. Data were analyzed by the “ flexsurvcure” package in R software (version 3. 4. 2). Results: The median age of patients was 57. 5 (interquartile range =18) years. Specifically, 56. 33% of the patients were male. The median time of follow-up in patients was 618 days. The cumulative survival proportion varied from 0. 90 to 0. 49 which indicated a reduction followed by a flat line in the probability of survival by sex. The flexible survival for adjusted cure proportion (%) was 68. 3. Only obesity was associated with a decreased risk of mortality (HR=0. 34; 95% CI: 0. 12-0. 97). Conclusion: The overall eight-year survival proportion and adjusted cure proportion for CRC were 49% and 68. 3%, respectively. Knowing the cure proportion and its related factors in patients with CRC, better services can be provided. Thus, early detection and screening strategies are required to reduce mortality and increase survival of patients.

Introduction: Multiple myeloma (MM) is a kind of blood cancer that is caused by the malfunction of plasma cells and their uncontrolled growth, which leads to a decrease in the level of immunity and the formation of bone lesions, especially in the spine, skull, pelvis, and ribs. Common symptoms in MM patients include severe bone pain, kidney problems, anemia, and frequent infections. This study aims to employ appropriate cure models to estimate the cure fraction and prognostic factors affecting overall survival (OS) in MM patients who have undergone transplantation. Materials and Methods: This study has analyzed the medical records of 52 patients with multiple myeloma who were admitted to Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences in Tehran from January 2010 to August 2016 and were followed up until February 2022. Four cure models were applied to the data and it determined the cure fraction in the Inverse Gaussian model is higher than in other models, so prognostic factors affecting the survival of patients were examined using this model. Results: The mean age at diagnosis was 53. 07 (SD =6. 4). The 5-year survival rate for MM patients was 74%, and the long-term survival rate for patients in this study was 54. 7%. Using the Inverse Gaussian model, the cure fraction was estimated at 54. 4% Conclusion: This study applies cure models to find prognostic factors based on pre-transplant CBC test on the survival time of MM patients who have been treated with auto-HSCT, so the number of platelets pre-transplantation and the patient's age are effective predictors for overall survival.

Introduction: Survival analysis including cure fraction subgroups is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function taking into consideration censoring and long-term survival. The analysis can be performed using traditional statistical models or neural networks. Recently, neural network has attracted attention in analyzing lifetime data due to its ability of efficiently estimating the survival function under the existence of complex covariates. To the best of our knowledge, this is the first time a parametric neural network is introduced to analyze mixture cure fraction models. Methods: In this paper, we introduce a novel neural network based on mixture cure fraction Weibull loss function. Results: Alzheimer disease dataset as long as synthetic dataset are used to study the efficiency of the model. We compared the results using goodness of fit methods in both datasets with Weibull regression. Conclusion: The proposed neural network has the flexibility of analyzing continuous data without discretization. Also, it has the advantage of using Weibull distribution properties. For example, it can analyze data with different hazard rates (monotonically decreasing, monotonically increasing and constant). comparing the results with Weibull regression, the proposed neural network performed better.

Introduction: Analyzing long term survivors such as diabetic patients can't be done using the usual survival models. One approach to analyze it is using defective distribution that doesn't force a pre-assumption of cure fraction to the model. To study more than one random variable interacting together, multivariate distributions may be used. However, most of multivariate distributions have complicated forms, which make the computations difficult. Besides, it may be hard to find a multivariate distribution that fits the data properly, especially in health care field. To get over this problem, one can use copula approach. In literature, to the best of our knowledge, only one paper handled copula defective models and didn't consider the effect of covariates. In this paper, we take into consideration not only existed covariates but also unobserved ones by including frailty term. Methods: Two new models are introduced. The first model, used Gumbel copula to take the dependence into consideration together with the observed covariates. The second one take into consideration not only the dependence but also the unobserved covariates by integrating frailty term in to the model. Results: A diabetic retinopathy data is analyzed. The two models indicated the existence of long-term survivals through negative parameters without the need of pre-assuming the existence of it. Including frailty term to the model helped in capturing more dependence between the variables. We compared the results using goodness of fit methods, and the results suggested that the model with frailty term is the best to be used. Conclusion: The two introduced models correctly detected the existence of cure fraction with less estimated parameters than that in mixture cure fraction models. Also, it has the advantage of not pre-assuming the existence of cure fraction to the model. comparing both models, the model with frailty term fitted the data better.

Statement of Problem: Composite reins have recently become popular for posterior teeth restorations.Gap formation and subsequent microleakage are of the complications resulting from such restorations.One of the techniques to overcome polymerization shrinkage of composite resins is sandwich technique (application of glass ionomer as a base beneath the composite resin). Since polymerization patterns in two types of composite resins (light cure and self cure) differ from each other, various effects on the bond strength between glass ionomer and dentin are expected.Purpose: The aim of this in vitro study was to evaluate the effects of self- cure and light- cure composite resins in sandwich technique on the bond strength of light cure glass ionomer and dentin.Materials and Methods: 40 extracted human premolars were selected and divided into four groups:Group 1: Light cure glass ionomer of 1mm thickness was placed on dentin.Group 2: 1mm thickness of light cure glass ionomer plus a mass of self cure composite resin of 2mm thickness were placed.Group 3: 1mm thickness of light cure glass ionomer plus light cure composite resin as two separate 1mm layer were placed.Group 4: 1mm thickness of light cure glass ionomer with 37% phosphoric acid etching followed by two separate layers of light cure composite resin of 1mm thickness were placed.SEM was used to determine gap size at GI- dentin and GI- composite interfaces. The findings were analyzed by ANOVA and t-student tests.Results: Groups 1 and 2 showed no gap at GI-dentin interface and also cracks were not observed in all these specimens. In group 3, there was gap between light cure GI and light cure composite resin and cracks were seen in GI, too. Group 4 showed gap at both interfaces and more cracks were seen in GI. Groups 1 and 2 showed the least gap formation and group 4 showed the most. Statistically significant difference was found between groups 3, 4 and group 1 (control), 2.Conclusion: Base on this study, the application of self-cure composite resin on light cure GI showed no gap and crack formation on GI-dentin and GI-composite interfaces and GI itself. However, light cure composite resins and glass ionomer etching aggregated crack and gap formation.

Background: Bayesian mixture cure rate frailty model is a model used in survival analysis by controlling frailty when the fraction of cured individuals exists. The present study was performed as the first systematic review in survival analysis with cure fraction. The aim of this systematic review was to study and evaluate the related studies on Bayesian mixture cure rate frailty model. Also, this model was used to demonstrate its importance and applicability in determining the variables affecting the survival of patients with gastric cancer. Methods: This systematic review was done based on the PRISMA guideline by considering related searching keywords in PubMed, Scopus, Science Direct, Web of Science, and Google Scholar. Also, Bayesian mixture cure rate frailty model was used to analyze gastric cancer data. Results: In the beginning, 882 studies related to survival analysis of cure rate model were found. Finally, by reading the full-text, only 4 related studies were found based on the inclusion and exclusion criteria. In these studies, semi-parametric models and parametric model with Weibull distribution were used for time-to-event data. Also, based on the results of the model, significant and affective variables on the survival of patients with gastric cancer were found. Conclusion: According to the results of this study, in the cure model, choice of proper distribution for the frailty variable and baseline distribution can influence the results. It was also found that place of residence, chemotherapy, morphology, and metastasis are effective variables on survival of patients with gastric cancer.