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Predictive Modeling in Prostate Cancer: A Conference Summary
european urology 55 (2009) 300–302
available at www.sciencedirect.com journal homepage: www.europeanurology.com
Editorial
Predictive Modeling in Prostate Cancer: A Conference Summary Riccardo Valdagni a,*, Peter T. Scardino b, Louis Denis c, Michael W. Kattan d a
Prostate Program, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA c Oncology Centre Antwerp, Antwerp, Belgium d Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA b
Prediction has always been considered to be a fascinating topic. History is full of fortune tellers, magicians, prophets, sibyls, oracles, and Cassandras making predictions or anticipating the future, somehow fulfilling the human need to control everyday life. Predictive ‘‘systems’’ have changed over time from magical thinking to today’s complex thinking, and systems of prediction are now a reality in several disciplines, including oncology. On April 17–19, 2008, a great faculty of internationally acknowledged experts in their fields were assembled in Venice, Italy, for the Inside Track Conference ‘‘Predictive Modeling in Prostate Cancer.’’ This first-ever event was organized by the European School of Oncology and the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan, Italy, and was dedicated to the exciting topic of prediction in prostate cancer. A summary of that event is provided in this paper. The anatomic staging classification known as the Union Internationale Contre le Cancer (International Union Against Cancer; UICC)/American Joint Committee on Cancer (AJCC) Tumor-Node-Metastases (TNM) classification has been the standard for a number of decades. Biologic prognostic factors have complemented the staging and stage grouping in most tumors: breast, bladder, melanoma, sar-
coma, renal, lymphoma, and prostate cancer. In the past 15 years, sophisticated mathematical models have been developed to analyze tumor variables and response to treatments. In the case of prostate cancer, the first attempts at models concerned the assessment of the risk of positive nodes before pelvic irradiation [1] (developed at Stanford University in 1987) and the clinical and pathologic tumor and node characteristics before surgery, studied by Partin et al in 1993 [2]. Since then, predictive tools in prostate cancer management have been the subject of growing interest in the uro-oncologic community. Clinicians have been provided with several instruments that aid the decision-making process. Many simple additive models as well as more complex mathematical models use statistical techniques and/or advanced medical informatics to analyze data from past clinical experience and try to make predictions about future outcomes. They have been produced for the clinical practice of urologists and radiation oncologists and, more recently, for medical oncologists. Predictive tools refer to probability formulas, look-up and propensity scoring tables, risk-class stratification models, classification and regressiontree analysis, nomograms, and artificial neural networks. In this scenario, an outstanding position has to be given to nomograms, which proved to be
* Corresponding author. Prostate Program, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, Milan, 20133, Italy. Tel. +39 02 23903033; Fax: +39 02 23903015. E-mail address: [email protected], [email protected] (R. Valdagni). 0302-2838/$ – see back matter # 2008 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi:10.1016/ j.eururo.2008.09.004
european urology 55 (2009) 300–302
useful, reliable, and user-friendly predictive tools and are now available for the prediction of many endpoints [3,4]. Prostate cancer is a very complex disease, and the decision-making process at the basis of the management of radical treatments is challenging because it requires a difficult balance of clinical benefits, life expectancy, previous and concurrent comorbidities, and potential treatment-induced toxicities. For this reason, the prediction of clinical outcomes is essential in the clinician–patient relationship. By understanding the most probable endpoint of a patient’s clinical course, physicians may modify treatment and post-treatment strategies in order to obtain better results and less severe treatmentinduced side effects. Prediction of potential outcomes also allows patients to choose responsibly among the different treatment strategies proposed by the clinicians. The purpose of the innovative meeting in Venice was to describe the state of the art in predictive modeling of prostate cancer and to lay out future research trends in the uro-oncologic community. The conference focused on the different aspects of predictive modeling in prostate cancer: use of genomics and biomolecular sciences, topics related to histopathology and system pathology, prediction of positive biopsies, probability of aggressive disease, response to different treatments (active surveillance, surgery, radiotherapy, brachytherapy, and hormonal therapy), predictive models in medical oncology and palliative care, prediction of longterm survival, and prediction of treatment-related side effects. Presentations were followed by interesting debates and clinical case discussions. The genomic and biomolecular approaches to outcome prediction are in a very early phase. Predictive models are needed that anticipate the development, progression, and aggressiveness of prostate cancer, with the final goal of improving staging and prognostication to facilitate treatment selection. Some progress was made in prostate cancer diagnosis with the development of tools that use a novel biopsy-based variable, cancer density, defined as percent of positive cores divided by prostate volume. This variable helps to evaluate the risk of having a more aggressive cancer. Of course, cancer density is derived from two fundamental variables included in many prognostic models: number of biopsy cores with cancer and the size of the prostate in grams. In the field of prostate cancer treatment, most available tools deal with the prediction of biochemical recurrence, which is used as a surrogate for
301
oncologic control. Clinical recurrence, cause-specific death, long-term survival, and outcomes related to morbidity [5,6] and quality of life are poorly represented and deserve increased attention from researchers. With regard to treatment regimens, open radical prostatectomy [7] and external-beam radiation therapy [8] have the greatest number of prediction models. Brachytherapy nomograms are rare [9], and no published tools can be found for laparoscopic or robotic radical prostatectomy or high-intensity focused ultrasound. Predictive models in medical oncology are now being applied in selected circumstances [10], but further effort is needed to capture clinically relevant and measurable variables for routine use in informing patients, in improving palliation and treatment decisions, and in creating homogeneous prognostic strata for randomized comparative trials of therapeutic agents. Presentations and debates at the conference highlighted prediction models that, although imperfect, are likely to represent the most accurate methods for estimating the probability of having cancer, the outcome of a treatment, or the probability of exhibiting treatment morbidity. Because prostate cancer is so heterogeneous and often occurs in older men with substantial comorbid conditions, treatment should be tailored to each individual patient and to the specific characteristics of one’s cancer if we are to maximize cancer control and minimize morbidity. Within this complex scenario the clinician needs a single, friendly computer interface for all relevant prediction models for his patient. These topics are of growing interest and need further work. Conflicts of interest: The authors have nothing to disclose.
References [1] Woo S, Kaplan I, Roach M, et al. Formula to estimate risk of pelvic lymph node metastasis from the total Gleason score for prostate cancer. J Urol 1988;140:387. [2] Partin AW, Yoo J, Carter HB, et al. The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol 1993;150:110–4. [3] Stephenson AJ, Kattan MW. Nomograms for prostate cancer. BJU Int 2006;98:39–46. [4] Ross RW, Kantoff PW. Predicting outcomes in prostate cancer: how many more nomograms do we need? J Clin Oncol 2007;25:3563–4. [5] Gallina A, Briganti A, Salonia A, et al. Development and internal validation of a nomogram predicting erectile function recovery after bilateral nerve sparing retropubic
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european urology 55 (2009) 300–302
radical prostatectomy for clinically localized prostate cancer [abstract]. Eur Urol Suppl 2008;7:162. [6] Valdagni R, Rancati T, Fiorino C, et al. Development of a set of nomograms to predict acute lower gastrointestinal toxicity for prostate cancer 3D-CRT. Int J Radiat Oncol Biol Phys 2008;71:1065–73. [7] Stephenson AJ, Scardino PT, Eastham JA, et al. Preoperative nomogram predicting the 10-yr probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst 2006;98:715–7. [8] Zelefsky MJ, Kattan MW, Fearn P, et al. Pretreatment nomogram predicting 10-yr biochemical outcome of
three-dimensional conformal radiotherapy and intensity-modulated radiotherapy for prostate cancer. Urology 2007;70:283–7. [9] Kattan MW, Potters L, Blasko JC, et al. Pretreatment nomogram for predicting freedom from recurrence after permanent prostate brachytherapy in prostate cancer. Urology 2001;58:393–9. [10] Armstrong AJ, Garrett-Mayer ES, et al. A contemporary prognostic nomogram for men with hormone-refractory metastatic prostate cancer: a TAX327 study analysis. Clin Cancer Res 2007;13:6396–403.
available at www.sciencedirect.com journal homepage: www.europeanurology.com
Editorial
Predictive Modeling in Prostate Cancer: A Conference Summary Riccardo Valdagni a,*, Peter T. Scardino b, Louis Denis c, Michael W. Kattan d a
Prostate Program, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA c Oncology Centre Antwerp, Antwerp, Belgium d Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA b
Prediction has always been considered to be a fascinating topic. History is full of fortune tellers, magicians, prophets, sibyls, oracles, and Cassandras making predictions or anticipating the future, somehow fulfilling the human need to control everyday life. Predictive ‘‘systems’’ have changed over time from magical thinking to today’s complex thinking, and systems of prediction are now a reality in several disciplines, including oncology. On April 17–19, 2008, a great faculty of internationally acknowledged experts in their fields were assembled in Venice, Italy, for the Inside Track Conference ‘‘Predictive Modeling in Prostate Cancer.’’ This first-ever event was organized by the European School of Oncology and the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan, Italy, and was dedicated to the exciting topic of prediction in prostate cancer. A summary of that event is provided in this paper. The anatomic staging classification known as the Union Internationale Contre le Cancer (International Union Against Cancer; UICC)/American Joint Committee on Cancer (AJCC) Tumor-Node-Metastases (TNM) classification has been the standard for a number of decades. Biologic prognostic factors have complemented the staging and stage grouping in most tumors: breast, bladder, melanoma, sar-
coma, renal, lymphoma, and prostate cancer. In the past 15 years, sophisticated mathematical models have been developed to analyze tumor variables and response to treatments. In the case of prostate cancer, the first attempts at models concerned the assessment of the risk of positive nodes before pelvic irradiation [1] (developed at Stanford University in 1987) and the clinical and pathologic tumor and node characteristics before surgery, studied by Partin et al in 1993 [2]. Since then, predictive tools in prostate cancer management have been the subject of growing interest in the uro-oncologic community. Clinicians have been provided with several instruments that aid the decision-making process. Many simple additive models as well as more complex mathematical models use statistical techniques and/or advanced medical informatics to analyze data from past clinical experience and try to make predictions about future outcomes. They have been produced for the clinical practice of urologists and radiation oncologists and, more recently, for medical oncologists. Predictive tools refer to probability formulas, look-up and propensity scoring tables, risk-class stratification models, classification and regressiontree analysis, nomograms, and artificial neural networks. In this scenario, an outstanding position has to be given to nomograms, which proved to be
* Corresponding author. Prostate Program, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian 1, Milan, 20133, Italy. Tel. +39 02 23903033; Fax: +39 02 23903015. E-mail address: [email protected], [email protected] (R. Valdagni). 0302-2838/$ – see back matter # 2008 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi:10.1016/ j.eururo.2008.09.004
european urology 55 (2009) 300–302
useful, reliable, and user-friendly predictive tools and are now available for the prediction of many endpoints [3,4]. Prostate cancer is a very complex disease, and the decision-making process at the basis of the management of radical treatments is challenging because it requires a difficult balance of clinical benefits, life expectancy, previous and concurrent comorbidities, and potential treatment-induced toxicities. For this reason, the prediction of clinical outcomes is essential in the clinician–patient relationship. By understanding the most probable endpoint of a patient’s clinical course, physicians may modify treatment and post-treatment strategies in order to obtain better results and less severe treatmentinduced side effects. Prediction of potential outcomes also allows patients to choose responsibly among the different treatment strategies proposed by the clinicians. The purpose of the innovative meeting in Venice was to describe the state of the art in predictive modeling of prostate cancer and to lay out future research trends in the uro-oncologic community. The conference focused on the different aspects of predictive modeling in prostate cancer: use of genomics and biomolecular sciences, topics related to histopathology and system pathology, prediction of positive biopsies, probability of aggressive disease, response to different treatments (active surveillance, surgery, radiotherapy, brachytherapy, and hormonal therapy), predictive models in medical oncology and palliative care, prediction of longterm survival, and prediction of treatment-related side effects. Presentations were followed by interesting debates and clinical case discussions. The genomic and biomolecular approaches to outcome prediction are in a very early phase. Predictive models are needed that anticipate the development, progression, and aggressiveness of prostate cancer, with the final goal of improving staging and prognostication to facilitate treatment selection. Some progress was made in prostate cancer diagnosis with the development of tools that use a novel biopsy-based variable, cancer density, defined as percent of positive cores divided by prostate volume. This variable helps to evaluate the risk of having a more aggressive cancer. Of course, cancer density is derived from two fundamental variables included in many prognostic models: number of biopsy cores with cancer and the size of the prostate in grams. In the field of prostate cancer treatment, most available tools deal with the prediction of biochemical recurrence, which is used as a surrogate for
301
oncologic control. Clinical recurrence, cause-specific death, long-term survival, and outcomes related to morbidity [5,6] and quality of life are poorly represented and deserve increased attention from researchers. With regard to treatment regimens, open radical prostatectomy [7] and external-beam radiation therapy [8] have the greatest number of prediction models. Brachytherapy nomograms are rare [9], and no published tools can be found for laparoscopic or robotic radical prostatectomy or high-intensity focused ultrasound. Predictive models in medical oncology are now being applied in selected circumstances [10], but further effort is needed to capture clinically relevant and measurable variables for routine use in informing patients, in improving palliation and treatment decisions, and in creating homogeneous prognostic strata for randomized comparative trials of therapeutic agents. Presentations and debates at the conference highlighted prediction models that, although imperfect, are likely to represent the most accurate methods for estimating the probability of having cancer, the outcome of a treatment, or the probability of exhibiting treatment morbidity. Because prostate cancer is so heterogeneous and often occurs in older men with substantial comorbid conditions, treatment should be tailored to each individual patient and to the specific characteristics of one’s cancer if we are to maximize cancer control and minimize morbidity. Within this complex scenario the clinician needs a single, friendly computer interface for all relevant prediction models for his patient. These topics are of growing interest and need further work. Conflicts of interest: The authors have nothing to disclose.
References [1] Woo S, Kaplan I, Roach M, et al. Formula to estimate risk of pelvic lymph node metastasis from the total Gleason score for prostate cancer. J Urol 1988;140:387. [2] Partin AW, Yoo J, Carter HB, et al. The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol 1993;150:110–4. [3] Stephenson AJ, Kattan MW. Nomograms for prostate cancer. BJU Int 2006;98:39–46. [4] Ross RW, Kantoff PW. Predicting outcomes in prostate cancer: how many more nomograms do we need? J Clin Oncol 2007;25:3563–4. [5] Gallina A, Briganti A, Salonia A, et al. Development and internal validation of a nomogram predicting erectile function recovery after bilateral nerve sparing retropubic
302
european urology 55 (2009) 300–302
radical prostatectomy for clinically localized prostate cancer [abstract]. Eur Urol Suppl 2008;7:162. [6] Valdagni R, Rancati T, Fiorino C, et al. Development of a set of nomograms to predict acute lower gastrointestinal toxicity for prostate cancer 3D-CRT. Int J Radiat Oncol Biol Phys 2008;71:1065–73. [7] Stephenson AJ, Scardino PT, Eastham JA, et al. Preoperative nomogram predicting the 10-yr probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst 2006;98:715–7. [8] Zelefsky MJ, Kattan MW, Fearn P, et al. Pretreatment nomogram predicting 10-yr biochemical outcome of
three-dimensional conformal radiotherapy and intensity-modulated radiotherapy for prostate cancer. Urology 2007;70:283–7. [9] Kattan MW, Potters L, Blasko JC, et al. Pretreatment nomogram for predicting freedom from recurrence after permanent prostate brachytherapy in prostate cancer. Urology 2001;58:393–9. [10] Armstrong AJ, Garrett-Mayer ES, et al. A contemporary prognostic nomogram for men with hormone-refractory metastatic prostate cancer: a TAX327 study analysis. Clin Cancer Res 2007;13:6396–403.