scholarly journals O14: RANDOM FOREST MODELS FOR PREDICTING SURVIVAL AFTER OESOPHAGECTOMY

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
SA Rahman ◽  
RC Walker ◽  
T Crosby ◽  
N Maynard ◽  
DA Cromwell ◽  
...  
Keyword(s):  
Overall Survival ◽  
Random Forest ◽  
Prediction Models ◽  
Cox Regression ◽  
Cox Model ◽  
Neoadjuvant Treatment ◽  
Lymph Node Involvement ◽  
Oesophageal Adenocarcinoma ◽  
Survival Models ◽  
Post Surgery

Abstract Introduction For patients with oesophageal cancer, producing accurate prediction models for survival after oesophagectomy has proved challenging. We investigated whether Random Survival Forests (RSF), a novel machine learning method, could produce an accurate prognostic model for overall survival after oesophagectomy. Method The study used data from the National Oesophago-Gastric Cancer Audit and included patients diagnosed with oesophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales and who underwent a curative oesophagectomy with adequate lymphadenectomy (15 LN) and survived to discharge (n=6198). Missing data was handled using multiple imputation and the data was split into training and validation cohorts. 13 variables were selected for inclusion using Random Forest variable importance and used to train the final model. The same variables were used to develop a traditional Cox regression model. Result Median survival was 53 months in both cohorts. The final RSF model had good discrimination in the validation cohort with a C-index of 0.757(0.755-0.759), exceeding the Cox model; 0.748(0.746-0.750). At 3 years post-surgery, overall survival was 56.2%. The RSF yielded a mean predicted survival of 55.8%(IQR 29.5%-81.7%) compared to 55.4%(40.0%-77.7%) for the Cox model. The most important variables were lymph node involvement and pT/ypT stage, however other variables including neoadjuvant treatment completion and surgical complications were also found to be important. Conculsion A Random Forest survival model provided better performance in predicting survival after curative oesophagectomy. This will allow more personalised predictions to be delivered clinicians and patients. Take-home message Random Forest survival models can accurately predict post-operative prognosis after oesophagectomy.

321 RANDOM SURVIVAL FOREST FOR PERSONALISED PROGNOSTICATION AFTER ESOPHAGECTOMY

2021 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Saqib Rahman ◽  
Robert Walker ◽  
Tom Crosby ◽  
Nicholas Maynard ◽  
Nigel Trudgill ◽  
...  
Keyword(s):  
Overall Survival ◽  
Random Forest ◽  
Prediction Models ◽  
Cox Regression ◽  
Cox Model ◽  
Neoadjuvant Treatment ◽  
Net Benefit ◽  
Term Survival ◽  
Post Surgery ◽  
Curative Esophagectomy

Abstract   For patients with esophageal cancer, producing accurate prediction models for long-term survival after esophagectomy has proved challenging. We investigated whether Random Survival Forests (RSF), a machine learning method, could produce an accurate prognostic model for overall survival after esophagectomy. Methods The study used data from the 'National Oesophago-Gastric Cancer Audit' (NOGCA) and included patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales and who underwent a curative esophagectomy with adequate lymphadenectomy (15 lymph nodes) and survived to discharge (n = 6838). Missing data was handled using multiple imputation. 15 variables were selected for inclusion using Random Forest variable importance and used to train the final model. The same variables with non-linearity transformations were used to develop a traditional Cox regression model for comparison. Results Median survival was 50 months. The final RSF model had good discrimination on internal validation with a C-index of 0.7627 (0.7625–0.7629), exceeding the cox model 0.7539 (0.7541–0.7537). At 3 years post-surgery, overall survival was 56.2%. The RSF yielded a mean predicted survival of 59.3% (IQR 33.3–87.1%) with good calibration (Figure 1) compared to 57.4% (38.4%–79.8%) for the cox model. The most influential variables were lymph node involvement and pT/ypT stage, however other variables including neoadjuvant treatment completion and surgical complications were also important. Decision curve analysis was undertaken which also showed an increased net benefit with the RSF model. Conclusion A Random Forest survival model provided better performance in predicting survival after curative esophagectomy. This will allow more personalised predictions to be delivered clinicians and patients. An online web app is provided at https://uoscancer.shinyapps.io/NOGCA/

Nomograms Predicting Overall Survival and Cancer Special Survival for Cervical Cancer in stage IIIC1

2021 ◽  
Author(s):  
Yifan Feng ◽  
Ye Wang ◽  
Yangqin Xie ◽  
Shuwei Wu ◽  
Yuyang Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
Tumor Size ◽  
Random Sampling ◽  
Prediction Models ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Training Group ◽  
Data Sets ◽  
External Data

Abstract BackgroundThe purpose of this study is to explore the factors that affect the prognosis of overall survival (OS) and cancer special survival (CSS) in cervical cancer with stage IIIC1 and establish nomogram models to predict this prognosis.MethodsData from The Surveil-lance, Epidemiology, and End Results (SEER) Program meeting the inclusion criterions were classified into training group, and data of validation were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan‐Meier curves, OS and CSS of stage IIIC1 were evaluated according to the training group. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell’s C-index and receiver operating characteristic curve (ROC) were calculated to measure the accuracy of the prediction models. Calibration plots show the relationship between the predicted probability and the actual outcome. Decision-curve analysis (DCA) was applied to evaluate the clinical applicability of the constructed nomogram.ResultsThe incidence of pelvic lymph node metastasis, a high-risk factor for prognosis in cervical cancer, decreased slightly over time. There are eight independent prognostic variables for OS, including age, race, histology, differentiation, extension range, tumor size, radiation recode and surgery, but seven for CSS with age excluded. Nomograms of OS and CSS were established based on the results. The C-index for the nomograms of OS and CSS were 0.692, 0.689 respectively when random sampling of SEER data sets, and 0.706, 0.737 respectively when random sampling of external data sets. AUCs for the nomogram of OS were 0.648, 0.644 respectively, and 0.683, 0.675 for the nomogram of CSS. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also proved the value of the two models.ConclusionAge, race, histology, differentiation, extension range, tumor size, radiation recode and surgery were all independent prognosis factors for OS. Only age excepts in CSS. OS and CSS nomograms were established in our study based on the result of multivariate Cox proportional hazard regression, and both own good predictive and clinical application value after validation.

No relationship is indicated between FHIT expression and clinicopathologic prognostic parameters in early stage cervical carcinoma

2003 ◽  
Vol 13 (2) ◽  
pp. 192-196
Author(s):  
C. Baykal ◽  
A. Ayhan ◽  
A. Al ◽  
K. YÜCE ◽  
A. Ayhan
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Lymph Node ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Lymph Node Involvement ◽  
Depth Of Invasion ◽  
Prognostic Parameters ◽  
Node Involvement ◽  
Disease Free

In this study we investigated FHIT (Fragile Histidine Triad) protein alterations in cervical carcinomas to assess the relation of this gene with cervical cancer. Eighty-eight patients with surgically treated FIGO (International Federation of Gynecology and Obstetrics) stage IB carcinomas of the cervix were included in this study. Clinicopathologic prognostic factors were compared with FHIT expression status. Disease-free and overall survival was evaluated according to prognostic factors and FHIT expression. The FHIT gene was found to be depressed in 53% (47/88) of the tumors. None of the clinicopathologic prognostic parameters showed a correlation with FHIT expression. Univariate survival analysis with the Kaplan-Meier method showed that only the age of the patient is significantly correlated with disease-free survival. Interestingly, when the same analysis was done for 5-year overall survival; diameter of the primary tumor, depth of invasion, occurrence of lymph node involvement, and number of metastatic lymph nodes were found to be statistically significant. Furthermore, multivariate analysis with Cox regression revealed that lymph node involvement was the only independent variable for 5-year overall survival. In the present study there was no statistical correlation between FHIT expression and clinicopathologic prognostic factors or survival figures of the patients. These findings may be explained with the carcinogenic role of FHIT in tumoral progression but not in the tumoral development that takes place after the carcinogenetic period.

scholarly journals GATA binding protein 6 (GATA6) is co-amplified with PIK3CA in patients with esophageal adenocarcinoma and is linked to neoadjuvant therapy

2020 ◽  
Author(s):  
Patrick Sven Plum ◽  
Heike Löser ◽  
Thomas Zander ◽  
Ahlem Essakly ◽  
Christiane J. Bruns ◽  
...  
Keyword(s):  
Overall Survival ◽  
Esophageal Adenocarcinoma ◽  
Cox Regression ◽  
Neoadjuvant Treatment ◽  
Driver Mutations ◽  
Large Tumor ◽  
Cox Regression Analysis ◽  
Entire Cohort ◽  
Independent Prognostic Marker ◽  
Survival Difference

Abstract Purpose Driver mutations are typically absent in esophageal adenocarcinoma (EAC). Mostly, oncogenes are amplified as driving molecular events (including GATA6-amplification in 14% of cases). However, only little is known about its biological function and clinical relevance. Methods We examined a large number of EAC (n = 496) for their GATA6 amplification by fluorescence in situ hybridization (FISH) analyzing both primary resected (n = 219) and neoadjuvant treated EAC (n = 277). Results were correlated to clinicopathological data and known mutations/amplifications in our EAC-cohort. Results GATA6 amplification was detectable in 49 (9.9%) EACs of our cohort. We observed an enrichment of GATA6-positive tumors among patients after neoadjuvant treatment (12,3% amplified tumors versus 6,8% in the primary resected group; p = 0.044). Additionally, there was a simultaneous amplification of PIK3CA and GATA6 (p < 0.001) not detectable when analyzing other genes such as EGFR, ERBB2, KRAS or MDM2. Although we did not identify a survival difference depending on GATA6 in the entire cohort (p = 0.212), GATA6 amplification was associated with prolonged overall survival among patients with primary surgery (median overall-survival 121.1 vs. 41.4 months, p = 0.032). Multivariate cox-regression analysis did not confirm GATA6 as an independent prognostic marker, neither in the entire cohort (p = 0.210), nor in the subgroup with (p = 0.655) or without pretreatment (p = 0.961). Conclusions Our study investigates the relevance of GATA6 amplification on a large tumor collective, which includes primary resected tumors and the clinically relevant group of neoadjuvant treated EACs. Especially in the pretreated group, we found an accumulation of GATA6-amplified tumors (12.3%) and a frequent co-amplification of PIK3CA. Our data suggest an increased resistance to radio-chemotherapy in GATA6-amplified tumors.

Overall survival with preoperative biliary drainage in patients with resectable pancreatic cancer.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 314-314
Author(s):  
Tobin Joel Crill Strom ◽  
Sarah E. Hoffe ◽  
Shivakumar Vignesh ◽  
Jason Klapman ◽  
Cynthia L. Harris ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Tumor Size ◽  
Biliary Drainage ◽  
Cox Regression ◽  
Neoadjuvant Treatment ◽  
Preoperative Biliary Drainage ◽  
Resectable Pancreatic Cancer ◽  
Pathologic Features

314 Background: Resectable pancreatic cancer patients often present with obstructive jaundice necessitating the placement of biliary stents or percutaneouse drainage catheters. We sought to evaluate whether preoperative biliary drainage affects recurrence and survival. Methods: An IRB-approved study was conducted on our institutional tumor registry to identify pancreatic cancer patients who were treated with upfront surgery between 2000 and 2012. Patients were then stratified by preoperative use of endoscopically placed stents (ERCP), percutaneous catheters (PTC), or no biliary drainage (NBD). The primary endpoint was overall survival (OS). Survival curves were calculated using the Kaplan-Meier method and the log-rank test. Multivariate analysis (MVA) was performed with a Cox regression model. Results: We identified 202 patients for the study (21 PTC; 89 ERCP; 92 NBD). Key differences between the 3 groups were mean pathologic tumor size (p=0.005), pathologic T3/4 (p =0.01), and pathologic N1 (p=0.007) status, with more aggressive pathologic features in PTC patients. PTC patients had a non-significant increase in rate of hepatic recurrences compared with ERCP and NBD patients (47.4% vs. 26.6% vs. 28.7%, respectively; p=0.20). PTC patients also had worse median and 3 year survival (21 months and 16%) compared to ERCP (23.3 months and 39%) and NBD patients (29 months and 45%, p=0.02). MVA revealed that PTC was an independent predictor of worse overall survival (HR 2.3[95% CI 1.3-4.0], p=0.005), along with pathologic tumor size (HR 1.1[1.0-1.3], p=0.008), nodes positive (HR 1.1[1.1-1.2], p=0.001), and post-operative CA19-9 >90 (HR 2.6[1.5-4.4], p=0.001). Conclusions: Patients with resectable pancreatic cancer who require a pre-operative PTC drain had a non-significant increase in hepatic recurrence rate and worse overall survival than patients who either had an ERCP stent placed or no biliary decompression prior to surgery. Given their worse prognosis, patients who require PTC placement might also benefit from neoadjuvant treatment with restaging prior to surgery.

scholarly journals Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuaiqun Wang ◽  
Dalu Yang ◽  
Wei Kong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Survival Rate ◽  
Clinical Data ◽  
Cox Regression ◽  
Cox Model ◽  
Pathway Enrichment Analysis ◽  
Gene Marker ◽  
Overall Survival Rate ◽  
Cox Regression Analysis

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients’ survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients.

scholarly journals A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qinqin Liu ◽  
Jing Li ◽  
Fei Liu ◽  
Weilin Yang ◽  
Jingjing Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cox Model ◽  
Accurate Estimation ◽  
Training Cohort ◽  
Dismal Prognosis ◽  
Texture Parameters ◽  
Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

SOURCE: Prediction models for overall survival in patients with metastatic and potentially curable esophageal and gastric cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 301-301
Author(s):  
Héctor G. van den Boorn ◽  
Ameen Abu-Hanna ◽  
Nadia Haj Mohammad ◽  
Maarten C.C.M. Hulshof ◽  
Suzanne S. Gisbertz ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Decision Making ◽  
Overall Survival ◽  
Shared Decision Making ◽  
Prediction Models ◽  
Cox Regression ◽  
Future Research ◽  
Shared Decision ◽  
Netherlands Cancer Registry ◽  
Treatment Type

301 Background: Prediction models in cancer care can provide personalized prediction outcomes and can aid in shared decision making. Existing prediction models for esophageal and gastric cancer (EGC), however, are mostly aimed at predicting survival after a curative treatment has already been completed. The aim of this study is to develop prediction models, called SOURCE, to predict overall survival at diagnosis in potentially curable and metastatic EGC patients. Methods: The data from 12,756 EGC patients diagnosed between 2014-2017 were retrieved from the prospective Netherlands Cancer Registry. Four Cox regression models were created for potentially curable and metastatic cancers of the esophagus and stomach. Predictors, including treatment type, were selected using the Akaike Information Criterion. The models were validated with temporal cross-validation on their concordance-index (c-index) and calibration. Results: The validated model’s c-index is 0.76 for potentially curable cancer. For the metastatic models, the c-indices are 0.71 and 0.70 for esophageal and gastric cancer, respectively. The calibration intercepts and slopes lie in the 95% confidence interval of 0 and 1, respectively. The included model variables are given in Table. Conclusions: The SOURCE prediction models show fair c-indices and an overall good calibration. The models are the first in EGC to include treatment as a predictor. The models predict survival at diagnosis for a variety of treatments and therefore could have a high clinical applicability. Future research is needed to demonstrate the effect on shared decision making in clinical practice. [Table: see text]

Overall survival of patients with metastatic castrate-resistant prostate cancer (mCRPC) who have PTEN tumor suppressor gene loss of function.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 58-58
Author(s):  
Shilpa Gupta ◽  
Ibrahim M. Abbass ◽  
Christopher Craggs ◽  
Sacha Satram ◽  
Tu My To ◽  
...  
Keyword(s):  
Overall Survival ◽  
Tumor Suppressor ◽  
Regression Model ◽  
Real World ◽  
Cox Regression ◽  
Cox Model ◽  
Suppressor Gene ◽  
Survival Outcomes ◽  
Left Truncation ◽  
Castration Resistance

58 Background: It is estimated that more than 40% of patients with mCRPC have functional loss of phosphatase and tensin homolog (PTEN) tumor suppressor gene, which is associated with unfavorable prognosis and reduced response to androgen receptor-targeting therapy. We describe patient characteristics and survival outcomes by PTEN LOF status among patients with mCRPC in real-world clinical practice. Methods: We conducted a retrospective cohort study using data from the nationwide Flatiron Health-Foundation Medicine mCRPC Clinico-Genomic database (FH-FMI CGDB), a de-identified database linking data derived from electronic health records with genomic data derived from FMI comprehensive genomic profiling (CGP) tests. The study included patients ≥18 years old, with a primary diagnosis of mCRPC between 1/1/2013 and 6/30/2019 who underwent FMI CGP testing and who had a valid PTEN LOF status. Patients were included if their PTEN report date and mCRPC diagnosis date occurred before death or censoring. PTEN LOF status was identified via FMI’s CGP testing. Kaplan-Meier (KM) methods assessed overall survival (OS) by PTEN LOF status from the date of mCRPC diagnosis (later of metastasis and castration resistance) until death or end of study follow-up. A stratified Cox regression model was used to estimate the hazard of death. The Cox model was adjusted for age, race and sequence of metastasis/CRPC diagnoses, and was stratified by the year of mCRPC diagnosis. Adjustments to account for left-truncation and survivorship bias were made in the KM analysis and the Cox regression model. Results: Among the 458 patients who met the eligibility criteria, 174 (38%) had PTEN LOF. The majority of the study sample (76%) was diagnosed with castration-resistance after metastasis. The PTEN LOF group had a higher percentage of white patients (80% vs. 68%; p= 0.01) compared to the PTEN non-LOF group. The mean age of the study sample was 68 years, and there was no difference in mean age at diagnosis by PTEN LOF status ( p= 0.17). Based on the KM estimates adjusted for left-truncation, the median OS was 14.3 months (95% confidence interval [CI]: 11.1-19.7) in the PTEN LOF group compared to 18.3 months (95%CI: 15.5-21.5) in the PTEN non-LOF group (log-rank p= 0.049). In the multivariable Cox model, the PTEN LOF group had numerically 30% higher risk of death compared to the PTEN non-LOF group (hazard ratio = 1.30; 95% CI: 0.99-1.71; p= 0.057). Conclusions: Among real-world patients with mCRPC in the CGDB, PTEN LOF could be associated with poorer survival outcomes, potentially highlighting the unmet need among these patients. Additional studies with larger cohorts are needed to better evaluate the survival outcomes of patients with PTEN LOF. Therapeutic agents acting on the PTEN/PI3K/AKT/mTOR pathway are being tested in clinical trials, and could potentially improve outcomes in this subgroup of patients with mCRPC.

Improved local control of invasive bladder cancer by concurrent cisplatin and preoperative or definitive radiation. The National Cancer Institute of Canada Clinical Trials Group.

1996 ◽  
Vol 14 (11) ◽  
pp. 2901-2907 ◽  
Author(s):  
C M Coppin ◽  
M K Gospodarowicz ◽  
K James ◽  
I F Tannock ◽  
B Zee ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Local Control ◽  
Cox Regression ◽  
Cox Model ◽  
Locally Advanced ◽  
Transitional Cell ◽  
Distant Metastases ◽  
Invasive Bladder Cancer ◽  
To Receive

PURPOSE A prospective randomized trial was conducted to determine whether the addition of concurrent cisplatin to preoperative or definitive radiation therapy in patients with muscle-invasive bladder cancer improved local control or survival. PATIENTS AND METHODS Ninety-nine eligible patients with T2 to T4b transitional cell bladder cancer participated, 64% with cT3b or cT4. Patients and their physicians selected either definitive radiotherapy or precystectomy radiotherapy; patients were then randomly allocated to receive intravenous cisplatin 100 mg/m2 at 2-week intervals for three cycles concurrent with pelvic radiation, or to receive radiation without chemotherapy. Patients were stratified by clinical tumor stage and by radiation plan. The median follow-up duration is 6.5 years. RESULTS The occurrence of distant metastases was the same in both study arms. However, 25 of 48 control patients have had a first recurrence in the pelvis, compared with 15 of 51 cisplatin-treated patients (P = .036). The pelvic relapse rate in the two groups was significantly reduced by concurrent cisplatin (P = .038, log-rank test) and this effect was preserved in a stepwise Cox regression model of prognostic factors (hazards ratio, 0.50; 90% confidence interval [CI], 0.29 to 0.86; P = .036). The hazard reduction was similar for both radiation plans. Pretreatment leukocytosis and high clinical stage were independent adverse factors in a Cox model of overall survival, but the effect of cisplatin was not significant. CONCLUSION Concurrent cisplatin may improve pelvic control of locally advanced bladder cancer with preoperative or definitive radiation, but has not been shown to improve overall survival. The use of concurrent cisplatin had no detectable effect on distant metastases.

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