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Objective: To evaluate the risk factors for uterine clear‐cell carcinoma (UCCC) and construct nomograms predicting 1‐, 3‐, and 5‐year overall survival rates of patients with UCCC. Methods: The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1‐, 3‐, and 5‐year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C‐index, net classification improvement (NRI) and decision curve analysis (DCA). Results: Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C‐indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance. Conclusion: A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision‐making. Synopsis: We identified factors influencing the prognosis of UCCC and constructed a nomogram for predicting the overall survival rate of UCCC patients. [ABSTRACT FROM AUTHOR]

Abstract Background This study aimed to construct a novel nomogram based on the number of positive lymph nodes to predict the overall survival of patients with pancreatic head cancer after radical surgery. Materials and methods 2271 and 973 patients in the SEER Database were included in the development set and validation set, respectively. The primary clinical endpoint was OS (overall survival). Univariate and multivariate Cox regression analyses were used to screen independent risk factors of OS, and then independent risk factors were used to construct a novel nomogram. The C-index, calibration curves, and decision analysis curves were used to evaluate the predictive power of the nomogram in the development and validation sets. Results After multivariate Cox regression analysis, the independent risk factors for OS included age, tumor extent, chemotherapy, tumor size, LN (lymph nodes) examined, and LN positive. A nomogram was constructed by using independent risk factors for OS. The C-index of the nomogram for OS was 0.652 [(95% confidence interval (CI): 0.639–0.666)] and 0.661 (95%CI: 0.641–0.680) in the development and validation sets, respectively. The calibration curves and decision analysis curves proved that the nomogram had good predictive ability. Conclusions The nomogram based on the number of positive LN can effectively predict the overall survival of patients with pancreatic head cancer after surgery.

Abstract Background Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

Abstract Purpose The most appropriate time of primary tumor radiotherapy in non-small cell lung cancer(NSCLC) with EGFR-TKIs remains unclear. The aim of this study was to investigate the effect of the time factor of primary tumor radiotherapy on long-term overall survival(OS)and provide a theoretical basis for further clinical research. Patients and methods In total, 238 patients with EGFR-TKIs and OS ≥ 12 months were statistically analysed. Patients were grouped: the D group without primary tumor radiotherapy and the R group with it.The R group were divided into three groups according to the interval between the start of EGFR-TKIs and the start of primary tumor radiotherapy: R0 − 30(

Abstract Objectives This study aimed to investigate the intra- and inter-observer consistency of the Visually Accessible Rembrandt Images (VASARI) feature set before and after dichotomization, and the association between dichotomous VASARI features and the overall survival (OS) in glioblastoma (GBM) patients. Methods This retrospective study included 351 patients with pathologically confirmed IDH1 wild-type GBM between January 2016 and June 2022. Firstly, VASARI features were assessed by four radiologists with varying levels of experience before and after dichotomization. Cohen’s kappa coefficient (κ) was calculated to measure the intra- and inter-observer consistency. Then, after adjustment for confounders using propensity score matching, Kaplan-Meier curves were used to compare OS differences for each dichotomous VASARI feature. Next, patients were randomly stratified into a training set (n = 211) and a test set (n = 140) in a 3:2 ratio. Based on the training set, Cox proportional hazards regression analysis was adopted to develop combined and clinical models to predict OS, and the performance of the models was evaluated with the test set. Results Eleven VASARI features with κ value of 0.61–0.8 demonstrated almost perfect agreement after dichotomization, with the range of κ values across all readers being 0.874–1.000. Seven VASARI features were correlated with GBM patient OS. For OS prediction, the combined model outperformed the clinical model in both training set (C-index, 0.762 vs. 0.723) and test set (C-index, 0.812 vs. 0.702). Conclusion The dichotomous VASARI features exhibited excellent inter- and intra-observer consistency. The combined model outperformed the clinical model for OS prediction.