Computational protocol: Nomograms to Predict Individual Prognosis of Patients with Primary Small Cell Carcinoma of the Bladder

Similar protocols

Protocol publication

[…] Patients diagnosed with small cell carcinoma of the bladder from 2004 to 2014 were identified from Surveillance, Epidemiology, and End Results (SEER) database. Only patients who met the following criteria were included: 1) age > 18 years; 2) microscopically diagnosed with primary SCCB as the first malignancy; 3) histological type limited to small cell carcinoma or combined small cell carcinoma (ICD-O-3 codes: 8002, 8041-8045); 4) active follow-up with complete date and known survival months and known cause of death; 5) adequate/consistent information on the TNM stages and other variables including age, gender, number of regional lymph node removed, number of regional lymph node positive, surgery of the primary tumor, radiation and chemotherapy. Patients were excluded if controversial information was recorded (e.g. patients at M0 stage with visceral metastases recorded). Covariates of interest extracted for each case are age, gender, marriage, pathology, TNM stage, surgery of the primary tumor, radiation, chemotherapy, sites of metastases and metastasectomy. Cancer stages reported using the 6th AJCC stages were converted based on the 7th edition . Besides, for patients received pelvic lymphadenectomy, the number of regional lymph nodes dissected and the number of positive lymph nodes were retrieved and lymph node ratio (LNR) was calculated by dividing the positive node number by the examined nodes number. For the purpose of properly assessing the prognostic value of lymph node ratio in SCCB patients, positive LNR was stratified into two categories (cut-off point 0.46) by X-tile program, a practical tool for cut-point optimization, according to the minimal p-value approach . Hence, the variable LNR was finally divided into four categories: patients didn't receive lymphadenectomy; LNR=0; 00.46. Using the similar approach, we also identified 8.3 cm as the cut-off point for patients with definite size of tumor. The follow-up information including survival status, survival months and cause of death were all extracted from the dataset. The primary endpoints of the study were overall survival (OS) and cancer specific survival (CSS). Survival time was calculated from the date of diagnosis to the date of 1) death from any cause (OS); 2) death from SCCB (CSS); or 3) the last follow-up.After patient identification, 582 eligible patients were enrolled and made up the primary cohort of SCCB. The primary cohort was randomized into a training cohort (n=482) and a validation cohort (n=100) in order to develop and validate the nomograms. [...] Continuous variable (age) was presented as median with range, while categorical variables were shown as the number of patient with respective percentages. Univariate and multivariate Cox proportional hazards regression analyses were employed to evaluate the prognostic factors. Nomograms for 1- and 3- year OS and 1- and 3-year CSS were formulated based on the results of the multivariate Cox regression analyses. The backward step-down process based on Akaike information criterion (AIC) was used to finally recruit independent prognostic factors into the constructions of the nomograms . Based on the training cohort and validation cohort, both internal and external validations of the nomograms were completed. The performances of the nomograms as well as the AJCC staging system were assessed by Harrell's concordance-index (C-index), which is similar to the area under curve (AUC) of the receiver operating characteristic (ROC) curve, but proved to be more suitable for censored data . Comparisons between nomogram models and the AJCC staging system were performed with the rcorrp.cens function in the Hmisc package in R . Calibration curves of the nomograms were applied to evaluate the consistency between predicted survival and observed survival and bootstraps with 1000 resamples were used for the validation . In the external validation of the nomograms, the total scores of each case in the validation cohort were calculated according to the established nomograms, and the scores were then used as factors in the Cox regression model, from which the validation C-index and calibration curves were derived .To further compare the nomograms with the present AJCC stating system, the primary cohort was divided in to four quartiles (four nomogroups) (for OS: nomogroup I: 0-20; nomogroup II: 20-24; nomogroup III:24-29; and nomogroup IV: >29; for CSS: nomogroup 1:0-18; nomogroup 2:18-23; nomogroup 3:23-28; nomogroup 4:>28) using quartile.exc function of Microsoft Excel. After grouping, the prognostic discrimination of the nomograms as well as the AJCC stages were assessed by Kaplan-Meier analysis. Due to the very limited number of patients (only 3 patients) with stage 0is (Tis patients), they were not included in the Kaplan-Meier curves. Furthermore, decision curve analysis (DCA) was employed using the rmda package of R to outline the ranges of threshold probabilities within which the nomograms were clinically applicable .The primary cohort was randomly allocated using the SPSS version 22.0 software (IBM SPSS Statistics, Chicago, IL, US). The other analyses were processed with the R program (v 3.4.0) using rms and the above-mentioned packages and Kaplan-Meier curves were drawn using the SPSS 22.0. Two-sided P values of less than 0.05 were considered statistically significant. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. […]

Pipeline specifications

Software tools X-tile, SPSS
Applications Miscellaneous, Tissue array analysis
Organisms Homo sapiens