Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology.
Purpose
Generalizable, updated, and easy‐to‐use prognostic models for patients with metastatic castration‐resistant prostate cancer (mCRPC) are lacking. We developed a nomogram predicting the overall survival (OS) of mCRPC patients receiving standard chemotherapy using data from five randomized clinical trials (RCTs).
Methods
Patients enrolled in the control arm of five RCTs (ASCENT 2, VENICE, CELGENE/MAINSAIL, ENTHUSE 14, and ENTHUSE 33) were randomly split between training (n = 1636, 70%) and validation cohorts (n = 700, 30%). In the training cohort, Cox regression tested the prognostic significance of all available variables as a predictor of OS. Independent predictors of OS on multivariable analysis were used to construct a novel multivariable model (nomogram). The accuracy of this model was tested in the validation cohort using time‐dependent area under the curve (tAUC) and calibration curves.
Results
Most of the patients were aged 65–74 years (44.5%) and the median (interquartile range) follow‐up time was 13.9 (8.9–20.2) months. At multivariable analysis, the following were independent predictors of OS in mCRPC patients: sites of metastasis (visceral vs. bone metastasis, hazard ratio [HR]: 1.24), prostate‐specific antigen (HR: 1.00), aspartate transaminase (HR: 1.01), alkaline phosphatase (HR: 1.00), body mass index (HR: 0.97), and hemoglobin (≥13 g/dl vs. <11 g/dl, HR: 0.41; all p < 0.05). A nomogram based on these variables was developed and showed favorable discrimination (tAUC at 12 and 24 months: 73% and 72%, respectively) and calibration characteristics on external validation.
Conclusion
A new prognostic model to predict OS of patients with mCRPC undergoing first line chemotherapy was developed. This can help urologists/oncologists in counseling patients and might be useful to better stratify patients for future clinical trials.
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