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1.  Predicting an Optimal Outcome after Radical Prostatectomy: The “Trifecta” Nomogram 
The Journal of urology  2008;179(6):2207-2211.
The optimal outcome after radical prostatectomy (RP) for clinically localized prostate cancer is freedom from biochemical recurrence (BCR) along with recovery of continence and erectile function, a so-called trifecta. We evaluated our series of open radical prostatectomy patients to determine the likelihood of this outcome and to develop a nomogram predicting the trifecta.
Material and Methods
We reviewed records of patients undergoing open RP for clinical stage T1c–T3a prostate cancer at our center during 2000–2006. Men were excluded if they received preoperative hormonal therapy, chemotherapy, or radiation therapy; if their pre-treatment PSA was >50 ng/ml; or if they were impotent or incontinent before RP; 1577 men were included in the study. Freedom from BCR was defined as post-RP PSA <0.2 ng/ml. Continence was defined as not having to wear any protective pads. Potency was defined as erections adequate for intercourse on the majority of attempts, with or without a phosphodiesterase-5 inhibitor.
Mean patient age was 58 years and mean pretreatment PSA was 6.4 ng/ml. A trifecta outcome (cancer-free status with recovery of continence and potency) was achieved in 62% of patients. In a nomogram developed to predict the likelihood of the trifecta, baseline PSA was the major predictive factor. The area under the receiver operating characteristic curve for the nomogram was 0.773, and calibration appeared excellent.
A trifecta (optimal) outcome can be achieved in the majority of men undergoing RP. The nomogram will permit patients to estimate preoperatively their likelihood of an optimal outcome after RP.
PMCID: PMC4270351  PMID: 18423693
2.  Development of a Nomogram Model Predicting Current Bone Scan Positivity in Patients Treated with Androgen-Deprivation Therapy for Prostate Cancer 
Frontiers in Oncology  2014;4:296.
Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT) for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations.
Materials and methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy) of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index.
Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA) level was 2.4 ng/ml; median PSA doubling time (PSADT) was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer center who underwent conventional 99mTc bone scans, which themselves have inherent limitations.
Conclusion: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy.
PMCID: PMC4209823  PMID: 25386410
non-steroidal anti-androgens; radionuclide imaging; nomogram; prostatic neoplasms; androgen-deprivation therapy; bone scan positivity
3.  ColoRectal Cancer Predicted Risk Online (CRC-PRO) Calculator Using Data from the Multi-Ethnic Cohort Study 
Better risk predictions for colorectal cancer (CRC) could improve prevention strategies by allowing clinicians to more accurately identify high-risk individuals. The National Cancer Institute's CRC risk calculator was created by Freedman et al using case control data.
An online risk calculator was created using data from the Multi-Ethnic Cohort Study, which followed >180,000 patients for the development of CRC for up to 11.5 years through linkage with cancer registries. Forward stepwise regression tuned to the c statistic was used to select the most important variables for use in separate Cox survival models for men and women. Model accuracy was assessed using 10-fold cross-validation.
Patients in the cohort experienced 2762 incident cases of CRC. The final model for men contained age, ethnicity, pack-years of smoking, alcoholic drinks per day, body mass index, years of education, regular use of aspirin, family history of colon cancer, regular use of multivitamins, ounces of red meat intake per day, history of diabetes, and hours of moderate physical activity per day. The final model for women included age, ethnicity, years of education, use of estrogen, history of diabetes, pack-years of smoking, family history of colon cancer, regular use of multivitamins, body mass index, regular use of nonsteroidal anti-inflammatory drugs, and alcoholic drinks per day. The calculator demonstrated good accuracy with a cross-validated c statistic of 0.681 in men and 0.679 in women, and it seems to be well calibrated graphically. An electronic version of the calculator is available at
This calculator seems to be accurate, is user friendly, and has been internally validated in a diverse population.
PMCID: PMC4219857  PMID: 24390885
Colorectal Cancer; Medical Decision Making; Prevention and Control; Risk
4.  Mortality After Prostate Cancer Treatment with Radical Prostatectomy, External-Beam Radiation Therapy, or Brachytherapy in Men Without Comorbidity 
European urology  2013;64(3):372-378.
Medical comorbidity is a confounding factor in prostate cancer (PCa) treatment selection and mortality. Large-scale comparative evaluation of PCa mortality (PCM) and overall mortality (OM) restricted to men without comorbidity at the time of treatment has not been performed.
To evaluate PCM and OM in men with no recorded comorbidity treated with radical prostatectomy (RP), external-beam radiation therapy (EBRT), or brachytherapy (BT).
Design, setting, and participants
Data from 10 361 men with localized PCa treated from 1995 to 2007 at two academic centers in the United States were prospectively obtained at diagnosis and retrospectively reviewed. We identified 6692 men with no recorded comorbidity on a validated comorbidity index. Median follow-up after treatment was 7.2 yr.
Treatment with RP in 4459 men, EBRT in 1261 men, or BT in 972 men.
Outcome measurements and statistical analysis
Univariate and multivariate Cox proportional hazards regression analysis, including propensity score adjustment, compared PCM and OM for EBRT and BT relative to RP as reference treatment category. PCM was also evaluated by competing risks analysis.
Results and limitations
Using Cox analysis, EBRT was associated with an increase in PCM compared with RP (hazard ratio [HR]: 1.66; 95% confidence interval [CI], 1.05–2.63), while there was no statistically significant increase with BT (HR: 1.83; 95% CI, 0.88–3.82). Using competing risks analysis, the benefit of RP remained but was no longer statistically significant for EBRT (HR: 1.55; 95% CI, 0.92–2.60) or BT (HR: 1.66; 95% CI, 0.79–3.46). In comparison with RP, both EBRT (HR: 1.71; 95% CI, 1.40–2.08) and BT (HR: 1.78; 95% CI, 1.37–2.31) were associated with increased OM.
In a large multicenter series of men without recorded comorbidity, both forms of radiation therapy were associated with an increase in OM compared with surgery, but there were no differences in PCM when evaluated by competing risks analysis. These findings may result from an imbalance of confounders or differences in mortality related to primary or salvage therapy.
PMCID: PMC3930076  PMID: 23506834
Prostatic neoplasms; Prostatectomy; Radiation therapy; Comorbidity; Comparative effectiveness research
5.  Adjuvant Leuprolide With or Without Docetaxel in Patients With High-Risk Prostate Cancer After Radical Prostatectomy (TAX-3501) 
Cancer  2013;119(20):3610-3618.
The current trial evaluated 2 common therapies for patients with advanced prostate cancer, docetaxel and hormonal therapy (HT), in the surgical adjuvant setting.
TAX-3501 was a randomized, phase 3, adjuvant study post-radical prostatectomy (RP) in high-risk patients with prostate cancer (n = 228) comparing 18 months of HT with (CHT) without docetaxel chemotherapy either immediately (I) or deferred (D). High-risk disease was defined as a 5-year freedom-from-disease-progression rate of ≤60% as predicted by a post-RP nomogram. Progression-free survival (PFS), including prostate-specific antigen disease recurrence, was the primary endpoint. The authors also assessed the accuracy of the nomogram and analyzed testosterone recovery in 108 patients treated with HT who had at least 1 posttreatment testosterone value.
Between December 2005 and September 2007, 228 patients were randomized between the treatment cohorts. TAX-3501 was terminated prematurely because of enrollment challenges, leaving it underpowered to detect differences in PFS. After a median follow-up of 3.4 years (interquartile range, 2.3–3.8 years), 39 of 228 patients (17%) demonstrated PSA disease progression, and metastatic disease progression occurred in 1 patient. The median time to baseline testosterone recovery after the completion of treatment was prolonged at 487 days (95% confidence interval, 457–546 days). The nomogram’s predicted versus observed freedom from disease progression was significantly different for the combination D(HT) and D(CHT) group (P < .00001).
TAX-3501 illustrated several difficulties involved in conducting postoperative adjuvant systemic trials in men with high-risk prostate cancer: the lack of consensus regarding patient selection and treatment, the need for long follow-up time, nonvalidated intermediate endpoints, evolving standard approaches, and the need for long-term research support. Except for selected patients at very high-risk of disease recurrence and death, surgical adjuvant trials in patients with prostate cancer may not be feasible.
PMCID: PMC4124610  PMID: 23943299
prostate cancer; adjuvant therapy; docetaxel; leuprolide; testosterone recovery
6.  Predictive and Prognostic Models in Radical Prostatectomy Candidates: A Critical Analysis of the Literature 
European urology  2010;58(5):687-700.
Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).
To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.
Evidence acquisition
A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.
Evidence synthesis
We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools’ accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.
Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.
PMCID: PMC4119802  PMID: 20727668
Prostate cancer; Radical prostatectomy; Prediction tools; Nomograms
7.  Conditional Probability of Survival Nomogram for 1-, 2-, and 3-Year Survivors After an R0 Resection for Gastric Cancer 
Annals of surgical oncology  2012;20(5):1623-1630.
Survival estimates after curative surgery for gastric cancer are based on AJCC staging, or on more accurate multivariable nomograms. However, the risk of dying of gastric cancer is not constant over time, with most deaths occurring in the first 2 years after resection. Therefore, the prognosis for a patient who survives this critical period improves. This improvement over time is termed conditional probability of survival (CPS). Objectives of this study were to develop a CPS nomogram predicting 5-year disease-specific survival (DSS) from the day of surgery for patients surviving a specified period of time after a curative gastrectomy and to explore whether variables available with follow-up improve the nomogram in the follow-up setting.
A CPS nomogram was developed from a combined US-Dutch dataset, containing 1,642 patients who underwent an R0 resection with or without chemotherapy/ radiotherapy for gastric cancer. Weight loss, performance status, hemoglobin, and albumin 1 year after resection were added to the baseline variables of this nomogram.
The CPS nomogram was highly discriminating (concordance index: 0.772). Surviving 1, 2, or 3 years gives a median improvement of 5-year DSS from surgery of 7.2, 19.1, and 31.6 %, compared with the baseline prediction directly after surgery. Introduction of variables available at 1-year follow-up did not improve the nomogram.
A robust gastric cancer nomogram was developed to predict survival for patients alive at time points after surgery. Introduction of additional variables available after 1 year of follow-up did not further improve this nomogram.
PMCID: PMC4091759  PMID: 23143591
8.  Predictive models for short- and long-term adverse outcomes following discharge in a contemporary population with acute coronary syndromes 
Although numerous risk-prediction models exist in patients presenting with acute coronary syndromes (ACS), they are subject to important short-comings, including lack of contemporary information. Short-term models are frequently biased by in-hospital events. Accordingly, we sought to create contemporary risk-prediction models for clinical outcomes following ACS up to 1 year following discharge. Models were constructed for death at 30 days and 1 year, death/myocardial infarction (MI)/revascularization at 30 days and death/MI at 1 year in consecutive patients presenting with ACS at our institution between 2006 and 2008, and discharged alive. Logistic regression was used to model the 30 day outcomes and Cox proportional hazards were used to model the 1 year outcomes. No linearity assumptions were made for continuous variables. The final model coefficients were used to create a prediction nomogram, which was incorporated into an online risk calculator. A total of 2,681 patients were included, of which about 9.5% presented with ST-elevation MI. All-cause mortality was 2.6% at 30 days and 13% at 1 year. Demographic, past medical history, laboratory, pharmacological and angiographic parameters were identified as being predictive of adverse ischemic outcomes at 30 days and 1 year. The c-indices for these models ranged from 0.73 to 0.82. Our study thus identified risk factors that are predictive of short- and long-term ischemic and revascularization outcomes in contemporary patients with ACS, and incorporated them into an easy-to-use online calculator, with equal or better discriminatory power than currently available models.
PMCID: PMC3584647  PMID: 23467552
Mortality; myocardial infarction; predictors; registry; revascularization
The Journal of urology  2011;185(3):869-875.
Long-term prostate cancer-specific mortality (PCSM) after radical prostatectomy is poorly defined in the era of widespread screening. An understanding of the treated natural history of screen-detected cancers and the pathological risk factors for PCSM are needed for treatment decision-making.
Using Fine and Gray competing risk regression analysis, the clinical and pathological data and follow-up information of 11,521 patients treated by radical prostatectomy at four academic centers from 1987 to 2005 were modeled to predict PCSM. The model was validated on 12,389 patients treated at a separate institution during the same period.
The overall 15-year PCSM was 7%. Primary and secondary pathological Gleason grade 4–5 (P < 0.001 for both), seminal vesicle invasion (P < 0.001), and year of surgery (P = 0.002) were significant predictors of PCSM. A nomogram predicting 15-year PCSM based on standard pathological parameters was accurate and discriminating with an externally-validated concordance index of 0.92. Stratified by patient age, 15-year PCSM for Gleason score ≤ 6, 3+4, 4+3, and 8–10 ranged from 0.2–1.2%, 4.2–6.5%, 6.6–11%, and 26–37%, respectively. The 15-year PCSM risks ranged from 0.8–1.5%, 2.9–10%, 15–27%, and 22–30% for organ-confined cancer, extraprostatic extension, seminal vesicle invasion, and lymph node metastasis, respectively. Only 3 of 9557 patients with organ-confined, Gleason score ≤ 6 cancers have died from prostate cancer.
The presence of poorly differentiated cancer and seminal vesicle invasion are the prime determinants of PCSM after radical prostatectomy. The risk of PCSM can be predicted with unprecedented accuracy once the pathological features of prostate cancer are known.
PMCID: PMC4058776  PMID: 21239008
prostatic neoplasms; prostatectomy; models; statistical; treatment outcome
10.  Predicting Survival After Curative Colectomy for Cancer: Individualizing Colon Cancer Staging 
Journal of Clinical Oncology  2011;29(36):4796-4802.
Cancer staging determines extent of disease, facilitating prognostication and treatment decision making. The American Joint Committee on Cancer (AJCC) TNM classification system is the most commonly used staging algorithm for colon cancer, categorizing patients on the basis of only these three variables (tumor, node, and metastasis). The purpose of this study was to extend the seventh edition of the AJCC staging system for colon cancer to incorporate additional information available from tumor registries, thereby improving prognostic accuracy.
Records from 128,853 patients with primary colon cancer reported to the Surveillance, Epidemiology and End Results Program from 1994 to 2005 were used to construct and validate three survival models for patients with primary curative-intent surgery. Independent training/test data sets were used to develop and test alternative models. The seventh edition TNM staging system was compared with models supplementing TNM staging with additional demographic and tumor variables available from the registry by calculating a concordance index, performing calibration, and identifying the area under receiver operating characteristic (ROC) curves.
Inclusion of additional registry covariates improved prognostic estimates. The concordance index rose from 0.60 (95% CI, 0.59 to 0.61) for the AJCC model, with T- and N-stage variables, to 0.68 (95% CI, 0.67 to 0.68) for the model including tumor grade, number of collected metastatic lymph nodes, age, and sex. ROC curves for the extended model had higher sensitivity, at all values of specificity, than the TNM system; calibration curves indicated no deviation from the reference line.
Prognostic models incorporating readily available data elements outperform the current AJCC system. These models can assist in personalizing treatment and follow-up for patients with colon cancer.
PMCID: PMC3664036  PMID: 22084366
11.  Human kallikrein-2 gene and protein expression predicts prostate cancer at repeat biopsy 
SpringerPlus  2014;3:295.
The human kallikrein-2 (hK2) protein and two single nucleotide polymorphism (SNPs) (rs2664155, rs198977) of the gene are associated with prostate cancer risk. We examined whether hK2 protein and gene SNPs predict prostate cancer at the time of repeat biopsy.
We prospectively offered a repeat biopsy to men with a negative prostate biopsy performed for a PSA >4.0 ng/mL or abnormal Digital Rectal Exam (DRE) between 2001–2005. We genotyped and measured serum hK2 levels in 941 men who underwent a repeat prostate biopsy. Logistic regression analyses were conducted to determine the significance of KLK2 SNPs and hK2 levels for predicting cancer at repeat biopsy.
Of the 941 patients, 180 (19.1%) were found to have cancer. The rs198977 SNP was positively associated with cancer at repeat biopsy (OR variant T allele = 1.8, 95% CI: 1.04-3.13, p = 0.049). When combined, the odds ratio for prostate cancer for patients with high hK2 levels and the variant T-allele of rs198977 was 3.77 (95% CI: 1.94-7.32, p < 0.0001), compared to patients with low hK2 levels and the C-allele. The addition of hK2 levels and KLK2 rs198977 to the baseline predictive model did not significantly increase the area under the curve from a baseline model of 0.67 to 0.69 (p = 0.6).
The KLK2 SNP rs198977 was positively associated with hK2 levels and predicts prostate cancer at the time of repeat prostate biopsy. Further characterization of the KLK2 gene will be needed to determine its clinical utility.
PMCID: PMC4162525  PMID: 25279276
Human Kallikrein-2; Nomogram; Prostate cancer; Single nucleotide polymorphisms
12.  Unmet Needs in the Prediction and Detection of Metastases in Prostate Cancer 
The Oncologist  2013;18(5):549-557.
Despite advances in therapy options, few guidelines or reviews address the optimal timing or methodology for the radiographic detection of metastatic disease in patients with advanced prostate cancer. This review discusses the current status of predicting the presence of metastatic disease, with a particular emphasis on the detection of the M0 to M1 transition, and reviews current data on newer imaging technologies that are changing the way metastases are detected.
The therapeutic landscape for the treatment of advanced prostate cancer is rapidly evolving, especially for those patients with metastatic castration-resistant prostate cancer (CPRC). Despite advances in therapy options, the diagnostic landscape has remained relatively static, with few guidelines or reviews addressing the optimal timing or methodology for the radiographic detection of metastatic disease. Given recent reports indicating a substantial proportion of patients with CRPC thought to be nonmetastatic (M0) are in fact metastatic (M1), there is now a clear opportunity and need for improvement in detection practices. Herein, we discuss the current status of predicting the presence of metastatic disease, with a particular emphasis on the detection of the M0 to M1 transition. In addition, we review current data on newer imaging technologies that are changing the way metastases are detected. Whether earlier detection of metastatic disease will ultimately improve patient outcomes is unknown, but given that the therapeutic options for those with metastatic and nonmetastatic CPRC vary, there are considerable implications of how and when metastases are detected.
PMCID: PMC3662846  PMID: 23650019
Imaging; Lymph nodes; Magnetic resonance imaging; Neoplasm metastasis; Prostatic neoplasms; Radionuclide imaging
13.  Pre-operative nomogram predicting 12-year probability of metastatic renal cancer 
The Journal of urology  2008;179(6):2146-2151.
For patients with renal masses localized to the kidney, there is currently no pre-operative tool to predict the likelihood of metastatic recurrence following surgical intervention. The primary goal of this study was to develop a predictive model that could be used in the pre-operative setting.
We pooled institutional databases from Memorial Sloan-Kettering and Mayo Clinic and identified 2,517 patients with renal masses and no concurrent evidence of metatases, who underwent radical or partial nephrectomy and with complete data. Cox proportional hazard regression analyses were used to model pre-operative clinical and radiographic characteristics as predictors for development of metastases following nephrectomy. Internal validation was performed with a statistical bootstrapping technique.
Metastatic recurrence developed in 340 of the 2517 patients. Median follow-up for patients without metastatic recurrence was 4.7 years. A nomogram was developed using pre-operative characteristics to predict the 12-year likelihood of post-operative metastatic recurrence, with a concordance index (CI) of 0.80. In contrast, the concordance index of pre-operative TNM staging was 0.71. Size of the primary renal mass, evidence of lymphadenopathy or necrosis on pre-operative imaging and the mode of presentation were important predictors for the subsequent development of metastases.
We present a pre-operative nomogram that accurately predicts the development of metastatic recurrence following nephrectomy. This nomogram may be potentially useful to identify high-risk patients for clinical trials in neoadjuvant setting.
PMCID: PMC3985125  PMID: 18423735
nomogram; renal masses; nephrectomy; metastasis
14.  A simulation model of colorectal cancer surveillance and recurrence 
Approximately one-third of those treated curatively for colorectal cancer (CRC) will experience recurrence. No evidence-based consensus exists on how best to follow patients after initial treatment to detect asymptomatic recurrence. Here, a new approach for simulating surveillance and recurrence among CRC survivors is outlined, and development and calibration of a simple model applying this approach is described. The model’s ability to predict outcomes for a group of patients under a specified surveillance strategy is validated.
We developed an individual-based simulation model consisting of two interacting submodels: a continuous-time disease-progression submodel overlain by a discrete-time Markov submodel of surveillance and re-treatment. In the former, some patients develops recurrent disease which probabilistically progresses from detectability to unresectability, and which may produce early symptoms leading to detection independent of surveillance testing. In the latter submodel, patients undergo user-specified surveillance testing regimens. Parameters describing disease progression were preliminarily estimated through calibration to match five-year disease-free survival, overall survival at years 1–5, and proportion of recurring patients undergoing curative salvage surgery from one arm of a published randomized trial. The calibrated model was validated by examining its ability to predict these same outcomes for patients in a different arm of the same trial undergoing less aggressive surveillance.
Calibrated parameter values were consistent with generally observed recurrence patterns. Sensitivity analysis suggested probability of curative salvage surgery was most influenced by sensitivity of carcinoembryonic antigen assay and of clinical interview/examination (i.e. scheduled provider visits). In validation, the model accurately predicted overall survival (59% predicted, 58% observed) and five-year disease-free survival (55% predicted, 53% observed), but was less accurate in predicting curative salvage surgery (10% predicted; 6% observed).
Initial validation suggests the feasibility of this approach to modeling alternative surveillance regimens among CRC survivors. Further calibration to individual-level patient data could yield a model useful for predicting outcomes of specific surveillance strategies for risk-based subgroups or for individuals. This approach could be applied toward developing novel, tailored strategies for further clinical study. It has the potential to produce insights which will promote more effective surveillance—leading to higher cure rates for recurrent CRC.
PMCID: PMC4021538  PMID: 24708517
Colorectal cancer; Recurrence; Surveillance; Follow-up; Model
15.  The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer 
Frontiers in Oncology  2012;2:138.
Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.
PMCID: PMC3468831  PMID: 23087901
prostatic neoplasms; nomogram; chemoprevention; prediction
World journal of urology  2012;32(1):185-191.
To assess the applicability of the Prostate Cancer Prevention Trial High Grade (Gleason grade ≥ 7) Risk Calculator (PCPTHG) in ten international cohorts, representing a range of populations.
25,512 biopsies from 10 cohorts (6 European, 1 UK, and 3 US) were included; 4 implemented 6-core biopsies and the remaining had 10- or higher schemes; 8 were screening cohorts and 2 were clinical. PCPTHG risks were calculated using prostate-specific antigen (PSA), digital rectal examination, age, African origin and history of prior biopsy and evaluated in terms of calibration plots, areas underneath the receiver operating characteristic curve (AUC), and net benefit curves.
The median AUC of the PCPTHG for high grade disease detection in the 10- and higher-core cohorts was 73.5% (range 63.9% to 76.7%) compared to a median of 78.1 (range = 72.0 to 87.6) among the four 6-core cohorts. Only the 10-core Cleveland Clinic cohort showed clear evidence of under-prediction by the PCPTHG, and this was restricted to risk ranges less than 15%. The PCPTHG demonstrated higher clinical net benefit in higher- compared to six-core biopsy cohorts, and among the former, there were no notable differences observed between clinical and screening cohorts, nor between European and US cohorts.
The PCPTHG requires minimal patient information and can be applied across a range of populations. PCPTHG risk thresholds ranging from 5 to 20%, depending on patient risk averseness, are recommended for clinical prostate biopsy decision-making.
PMCID: PMC3702682  PMID: 22527674
Calibration; Discrimination; Net Benefit; High Grade Prostate Cancer; Risk; Prostate Cancer Prevention Trial
17.  Validation of a Postresection Pancreatic Adenocarcinoma Nomogram for Disease-Specific Survival 
Nomograms are statistically based tools that provide the overall probability of a specific outcome. They have shown better individual discrimination than the current TNM staging system in numerous patient tumor models. The pancreatic nomogram combines individual clinicopathologic and operative data to predict disease-specific survival at 1, 2, and 3 years from initial resection. A single US institution database was used to test the validity of the pancreatic adenocarcinoma nomogram established at Memorial Sloan-Kettering Cancer Center.
Patients and Methods
The nomogram was created from a prospective pancreatic adenocarcinoma database that included 555 consecutive patients between October 1983 and April 2000. The nomogram was validated by an external patient cohort from a retrospective pancreatic adenocarcinoma database at Massachusetts General Hospital that included 424 consecutive patients between January 1985 and December 2003.
Of the 424 patients, 375 had all variables documented. At last follow-up, 99 patients were alive, with a median follow-up time of 27 months (range, 2 to 151 months). The 1-, 2-, and 3-year disease-specific survival rates were 68% (95% CI, 63% to 72%), 39% (95% CI, 34% to 44%), and 27% (95% CI, 23% to 32%), respectively. The nomogram concordance index was 0.62 compared with 0.59 with the American Joint Committee on Cancer (AJCC) stage (P = .004). This suggests that the nomogram discriminates disease-specific survival better than the AJCC staging system.
The pancreatic cancer nomogram provides more accurate survival predictions than the AJCC staging system when applied to an external patient cohort. The nomogram may aid in more accurately counseling patients and in better stratifying patients for clinical trials and molecular tumor analysis.
PMCID: PMC3903268  PMID: 16234519
18.  Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials 
PLoS ONE  2014;9(1):e85010.
It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
PMCID: PMC3897405  PMID: 24465467
19.  The Association of Community Health Indicators With Outcomes for Kidney Transplant Recipients in the United States 
To evaluate the association of community health indicators with outcomes for kidney transplant recipients.
Retrospective observational cohort study using multivariable Cox proportional hazards models.
Transplant recipients in the United States from the Scientific Registry of Transplant Recipients merged with health indicators compiled from several national databases and the Centers for Disease Control and Prevention, including the National Center for Health Statistics, the Behavioral Risk Factor Surveillance System, and the National Center for Chronic Disease Prevention and Health Promotion.
A total of 100 164 living and deceased donor adult (aged ≥18 years) kidney transplant recipients who underwent a transplant between January 1, 2004, and December 31, 2010.
Main Outcome Measures
Risk-adjusted time to post-transplant mortality and graft loss.
Multiple health indicators from recipients’ residence were independently associated with outcomes, in cluding low birth weight, preventable hospitalizations, inactivity rate, and smoking and obesity prevalence. Recipients in the highest-risk counties were more likely to be African American (adjusted odds ratio, 1.59, 95% CI, 1.51-1.68), to be younger (aged 18-39 years; 1.46; 1.32-1.60), to have lower educational attainment (
In a national cohort of patients undergoing complex medical procedures, health indicators from patients’ communities are strong independent predictors of all-cause mortality. Findings highlight the importance of community conditions for risk stratification of patients and development of individualized treatment protocols. Findings also demonstrate that standard risk adjustment does not capture important factors that may affect unbiased performance evaluations of transplant centers.
PMCID: PMC3880685  PMID: 22351876
Treatment decisions on prostate cancer diagnosed by trans-urethral resection (TURP) of the prostate are difficult. The current TNM staging system for pT1 prostate cancer has not been re-evaluated for 25 years. Our objective was to optimise the predictive power of tumor extent measurements in TURP of the prostate specimens. A total of 914 patients diagnosed by TURP of the prostate between 1990 and 1996, managed conservatively were identified. The clinical end point was death from prostate cancer. Diagnostic serum prostate-specific antigen (PSA) and contemporary Gleason grading was available. Cancer extent was measured by the percentage of chips infiltrated by cancer. Death rates were compared by univariate and multivariate proportional hazards models, including baseline PSA and Gleason score. The percentage of positive chips was highly predictive of prostate cancer death when assessed as a continuous variable or as a grouped variable on the basis of and including the quintiles, quartiles, tertiles and median groups. In the univariate model, the most informative variable was a four group-split (≤ 10%, >10–25%, > 25–75% and > 75%); (HR = 2.08, 95% CI = 1.8–2.4, P < 0.0001). The same was true in a multivariate model (ΔX2 (1 d.f.) = 15.0, P = 0.0001). The current cutoff used by TNM (< = 5%) was sub-optimal (ΔX2 (1 d.f.) = 4.8, P = 0.023). The current TNM staging results in substantial loss of information. Staging by a four-group subdivision would substantially improve prognostication in patients with early stage disease and also may help to refine management decisions in patients who would do well with conservative treatments.
PMCID: PMC3853363  PMID: 20834240
conservative treatment; prostate cancer; stage; trans-urethral resection of prostate; watchful waiting
Virchows Archiv : an international journal of pathology  2010;457(5):10.1007/s00428-010-0971-z.
The optimal method for measuring cancer extent in prostate biopsy specimens is unknown. Seven hundred forty-four patients diagnosed between 1990 and 1996 with prostate cancer and managed conservatively were identified. The clinical end point was death from prostate cancer. The extent of cancer was measured in terms of number of cancer cores (NCC), percentage of cores with cancer (PCC), total length of cancer (LCC) and percentage length of cancer in the cores (PLC). These were correlated with prostate cancer mortality, in univariate and multivariate analysis including Gleason score and prostate-specific antigen (PSA). All extent of cancer variables were significant predictors of prostate cancer death on univariate analysis: NCC, hazard ration (HR)=1.15, 95% confidence interval (CI)=1.04–1.28, P=0.011; PPC, HR=1.01, 95% CI=1.01–1.02, P<0.0001; LCC, HR=1.02, 95% CI=1.01–1.03, P=0.002; PLC, HR=1.01, 95% CI=1.01–1.02, P=0.0001. In multivariate analysis including Gleason score and baseline PSA, PCC and PLC were both independently significant P=0.004 and P=0.012, respectively, and added further information to that provided by PSA and Gleason score, whereas NNC and LCC were no longer significant (P=0.5 and P=0.3 respectively). In a final model, including both extent of cancer variables, PCC was the stronger, adding more value than PLC (χ2 (1df)=7.8, P=0.005, χ2 (1df)=0.5, P=0.48 respectively). Measurements of disease burden in needle biopsy specimens are significant predictors of prostate-cancer-related death. The percentage of positive cores appeared the strongest predictor and was stronger than percentage length of cancer in the cores.
PMCID: PMC3853376  PMID: 20827488
Prostate biopsy; Prostate cancer; Biopsy; prognostic factors; Tumour extent
Diabetes Care  2008;31(12):2301-2306.
OBJECTIVE—The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS—This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model.
RESULTS—Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752.
CONCLUSIONS—We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions.
PMCID: PMC2584185  PMID: 18809629
PeerJ  2013;1:e123.
Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.
Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression) were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1) concordance indices; (2) Brier scores; and (3) calibration curves.
Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.
Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
PMCID: PMC3740143  PMID: 23940836
Prediction; Propensity score; Calibration curve; Concordance index; Multivariable regression
Neuro-Oncology  2012;14(7):910-918.
Purpose: An estimated 24%–45% of patients with cancer develop brain metastases. Individualized estimation of survival for patients with brain metastasis could be useful for counseling patients on clinical outcomes and prognosis. Methods: De-identified data for 2367 patients with brain metastasis from 7 Radiation Therapy Oncology Group randomized trials were used to develop and internally validate a prognostic nomogram for estimation of survival among patients with brain metastasis. The prognostic accuracy for survival from 3 statistical approaches (Cox proportional hazards regression, recursive partitioning analysis [RPA], and random survival forests) was calculated using the concordance index. A nomogram for 12-month, 6-month, and median survival was generated using the most parsimonious model. Results: The majority of patients had lung cancer, controlled primary disease, no surgery, Karnofsky performance score (KPS) ≥ 70, and multiple brain metastases and were in RPA class II or had a Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) score of 1.25–2.5. The overall median survival was 136 days (95% confidence interval, 126–144 days). We built the nomogram using the model that included primary site and histology, status of primary disease, metastatic spread, age, KPS, and number of brain lesions. The potential use of individualized survival estimation is demonstrated by showing the heterogeneous distribution of the individual 12-month survival in each RPA class or DS-GPA score group. Conclusion: Our nomogram provides individualized estimates of survival, compared with current RPA and DS-GPA group estimates. This tool could be useful for counseling patients with respect to clinical outcomes and prognosis.
PMCID: PMC3379797  PMID: 22544733
brain metastases; nomogram; prediction; prognosis; survival
PeerJ  2013;1:e87.
Introduction. The objective of this study was to create a tool that accurately predicts the risk of morbidity and mortality in patients with type 2 diabetes according to an oral hypoglycemic agent.
Materials and Methods. The model was based on a cohort of 33,067 patients with type 2 diabetes who were prescribed a single oral hypoglycemic agent at the Cleveland Clinic between 1998 and 2006. Competing risk regression models were created for coronary heart disease (CHD), heart failure, and stroke, while a Cox regression model was created for mortality. Propensity scores were used to account for possible treatment bias. A prediction tool was created and internally validated using tenfold cross-validation. The results were compared to a Framingham model and a model based on the United Kingdom Prospective Diabetes Study (UKPDS) for CHD and stroke, respectively.
Results and Discussion. Median follow-up for the mortality outcome was 769 days. The numbers of patients experiencing events were as follows: CHD (3062), heart failure (1408), stroke (1451), and mortality (3661). The prediction tools demonstrated the following concordance indices (c-statistics) for the specific outcomes: CHD (0.730), heart failure (0.753), stroke (0.688), and mortality (0.719). The prediction tool was superior to the Framingham model at predicting CHD and was at least as accurate as the UKPDS model at predicting stroke.
Conclusions. We created an accurate tool for predicting the risk of stroke, coronary heart disease, heart failure, and death in patients with type 2 diabetes. The calculator is available online at under the heading “Type 2 Diabetes” and entitled, “Predicting 5-Year Morbidity and Mortality.” This may be a valuable tool to aid the clinician’s choice of an oral hypoglycemic, to better inform patients, and to motivate dialogue between physician and patient.
PMCID: PMC3685323  PMID: 23781409
Type 2 diabetes mellitus; Prediction; Propensity; Coronary heart disease; Heart failure; Stroke; Mortality; Electronic health record; Hypoglycemic agents

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