Prognostic factors and outcomes in primary urethral cancer: results from the international collaboration on primary urethral carcinoma.

PURPOSE To evaluate risk factors for survival in a large international cohort of patients with primary urethral cancer (PUC). METHODS A series of 154 patients (109 men, 45 women) were diagnosed with PUC in ten referral centers between 1993 and 2012. Kaplan-Meier analysis with log-rank test was used to investigate various potential prognostic factors for recurrence-free (RFS) and overall survival (OS). Multivariate models were constructed to evaluate independent risk factors for recurrence and death. RESULTS Median age at definitive treatment was 66 years (IQR 58-76). Histology was urothelial carcinoma in 72 (47 %), squamous cell carcinoma in 46 (30 %), adenocarcinoma in 17 (11 %), and mixed and other histology in 11 (7 %) and nine (6 %), respectively. A high degree of concordance between clinical and pathologic nodal staging (cN+/cN0 vs. pN+/pN0; p < 0.001) was noted. For clinical nodal staging, the corresponding sensitivity, specificity, and overall accuracy for predicting pathologic nodal stage were 92.8, 92.3, and 92.4 %, respectively. In multivariable Cox-regression analysis for patients staged cM0 at initial diagnosis, RFS was significantly associated with clinical nodal stage (p < 0.001), tumor location (p < 0.001), and age (p = 0.001), whereas clinical nodal stage was the only independent predictor for OS (p = 0.026). CONCLUSIONS These data suggest that clinical nodal stage is a critical parameter for outcomes in PUC.

Prognostic Relevance of Palliative Primary Tumor Removal in 37,793 Metastatic Colorectal Cancer Patients: A Population-Based, Propensity Score-Adjusted Trend Analysis

OBJECTIVE To assess whether palliative primary tumor resection in colorectal cancer patients with incurable stage IV disease is associated with improved survival. BACKGROUND There is a heated debate regarding whether or not an asymptomatic primary tumor should be removed in patients with incurable stage IV colorectal disease. METHODS Stage IV colorectal cancer patients were identified in the Surveillance, Epidemiology, and End Results database between 1998 and 2009. Patients undergoing surgery to metastatic sites were excluded. Overall survival and cancer-specific survival were compared between patients with and without palliative primary tumor resection using risk-adjusted Cox proportional hazard regression models and stratified propensity score methods. RESULTS Overall, 37,793 stage IV colorectal cancer patients were identified. Of those, 23,004 (60.9%) underwent palliative primary tumor resection. The rate of patients undergoing palliative primary cancer resection decreased from 68.4% in 1998 to 50.7% in 2009 (P < 0.001). In Cox regression analysis after propensity score matching primary cancer resection was associated with a significantly improved overall survival [hazard ratio (HR) of death = 0.40, 95% confidence interval (CI) = 0.39-0.42, P < 0.001] and cancer-specific survival (HR of death = 0.39, 95% CI = 0.38-0.40, P < 0.001). The benefit of palliative primary cancer resection persisted during the time period 1998 to 2009 with HRs equal to or less than 0.47 for both overall and cancer-specific survival. CONCLUSIONS On the basis of this population-based cohort of stage IV colorectal cancer patients, palliative primary tumor resection was associated with improved overall and cancer-specific survival. Therefore, the dogma that an asymptomatic primary tumor never should be resected in patients with unresectable colorectal cancer metastases must be questioned.

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Erythropoietin induces tumor regression and antitumor immune responses in murine myeloma models

Recombinant human erythropoietin (rHuEpo) has been used successfully in the treatment of cancer-related anemia. Clinical observations with several patients with multiple-myeloma treated with rHuEpo has shown, in addition to the improved quality of life, a longer survival than expected, considering the poor prognostic features of these patients. Based on these observations, we evaluated the potential biological effects of rHuEpo on the course of tumor progression by using murine myeloma models (MOPC-315-IgAλ2 and 5T33 MM-IgG2b). Here we report that daily treatment of MOPC-315 tumor-bearing mice with rHuEpo for several weeks induced complete tumor regression in 30–60% of mice. All regressors that were rechallenged with tumor cells rejected tumor growth, and this resistance was tumor specific. The Epo-triggered therapeutic effect was shown to be attributed to a T cell-mediated mechanism. Serum Ig analysis indicated a reduction in MOPC-315 λ light chain in regressor mice. Intradermal inoculation of 5T33 MM tumor cells followed by Epo treatment induced tumor regression in 60% of mice. The common clinical manifestation of myeloma bone disease in patients with multiple-myeloma was established in these myeloma models. Epo administration to these tumor-bearing mice markedly prolonged their survival and reduced mortality. Therefore, erythropoietin seems to act as an antitumor therapeutic agent in addition to its red blood cell-stimulating activity.

Validation and refinement of survival models for liver retransplantation

Orthotopic liver retransplantation (re-OLT) is highly controversial. The objectives of this study were to determine the validity of a recently developed United Network for Organ Sharing (UNOS) multivariate model using an independent cohort of patients undergoing re-OLT outside the United States, to determine whether incorporation of other variables that were incomplete in the UNOS registry would provide additional prognostic information, to develop new models combining data sets from both cohorts, and to evaluate the validity of the model for end-stage liver disease (MELD) in patients undergoing re-OLT. Two hundred eighty-one adult patients undergoing re-OLT (between 1986 and 1999) at 6 foreign transplant centers comprised the validation cohort. We found good agreement between actual survival and predicted survival in the validation cohort; 1-year patient survival rates in the low-, intermediate-, and high-risk groups (as assigned by the original UNOS model) were 72%, 68%, and 36%, respectively (P < .0001). In the patients for whom the international normalized ratio (INR) of prothrombin time was available, MELD correlated with outcome following re-OLT; the median MELD scores for patients surviving at least 90 days compared with those dying within 90 days were 20.75 versus 25.9, respectively (P = .004). Utilizing both patient cohorts (n = 979), a new model, based on recipient age, total serum bilirubin, creatinine, and interval to re-OLT, was constructed (whole model χ(2) = 105, P < .0001). Using the c-statistic with 30-day, 90-day, 1-year, and 3-year mortality as the end points, the area under the receiver operating characteristic (ROC) curves for 4 different models were compared. In conclusion, prospective validation and use of these models as adjuncts to clinical decision making in the management of patients being considered for re-OLT are warranted.