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Background To develop a nomogram to predict cancer-specific survival (CSS) in patients with metastatic testicular germ cell tumors (mTGCTs). Methods Data were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression models were used to identify factors associated with CSS. Survival times between different groups were compared using Kaplan-Meier survival curves and the log-rank test. A nomogram visualization model was established using the R language to predict survival rates. Harrell’s concordance index (C-index), the area under the receiver operating characteristic curve (AUC) and calibration plots were used to assess the performance of the model. Results We analyzed the data of 949 patients. The median follow-up time was 32 months (range 0 to 83 months), and 224 (23.60%) patients died before the last follow-up, of whom 193 (20.33%) died of mTGCTs. The site of distant metastases was an independent prognostic factor for CSS. Compared to patients without involvement of the corresponding organ, patients with bone, brain, liver, and lung involvement had worse CSS. We also found that age, histological type, surgery, radiation therapy, chemotherapy, metastatic site and insurance status affected the CSS of patients with mTGCTs. We used these prognostic factors to construct our nomogram. Harrell’s C-index for CSS was 0.739. The AUC and calibration plots indicated good performance of the nomogram. Conclusions A nomogram for predicting CSS in patients with mTGCTs has been developed, which can help patients and clinicians accurately predict mortality risk and recommend personalized treatment modalities.

Background To develop a nomogram for predicting cancer-specific survival (CSS) of patients with non-metastatic primary adenocarcinoma of the bladder (NMACB). Methods We used a retrospective cohort study design. Patient data were obtained from the SEER database, univariate and multivariate Cox regression analyses were performed to identify factors associated with CSS. A nomogram visualization model was established using R language software to predict survival rate. Harrell’s concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC) in addition to calibration plots were used to assess the performance of the model. Results A total of 1,635 patients were included in the study. A multivariate Cox regression model indicated that age, histological type, grade, stage, and surgery were independent covariates associated with CSS. Using these prognostic factors, a nomogram was constructed. Harrell’s C indices for CSS were 0.729 in the training cohort and 0.716 in the validation cohort. AUC values were 0.769, 0.735 and 0.724 for 1, 3, and 5-year in the training cohort, and 0.738, 0.727 and 0.713 for 1, 3 and 5-year in the validation cohort, respectively. The AUC values and calibration plots indicated that the nomogram provided good predictive performance. Conclusions A nomogram for predicting CSS in patients with NMACB was developed to assist clinicians in the accurate prediction of mortality risk to allow them to recommend a personalized treatment modality.

BACKGROUND: To examine the association between age at diagnosis and cancer-specific mortality (CSM) in primary urachal adenocarcinoma. METHODS: The data was obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results program (SEER). A total of 393 patients were included in the study. Smooth curve fitting and two-piecewise Cox proportional hazards models were used to identify the nonlinearity between the age at initial diagnosis and cancer-specific survival rate. Survival time between different groups was compared using Kaplan-Meier survival curves and the log-rank test. RESULTS: Using smooth curve fitting we found that the relationship between age at diagnosis and cancer-specific survival takes on a U-shaped curve. The inflection point that we identified for the age at initial diagnosis was 60 years. The log-likelihood ratio test (P60 years, increased risk of CSM was significantly associated with increased age (HR: 1.05, P=0.0499). CONCLUSIONS: In summary, our study suggested that the relationship between age at diagnosis and cancer-specific survival is nonlinear, and takes on a U-shaped curve. Both younger and older age at initial diagnosis age were associated with increased CSM.