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Cancer

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Supplement: Predictive Modeling in Prostate Cancer, Supplement to Cancer

1 July 2009

Volume 115, Issue S13

Pages i–ii, 3035–3162

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  1. Supplement

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    2. Supplement
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      The 2008 European School of Oncology inside Track Conference, “Predictive Modeling in Prostate Cancer” (pages 3035–3038)

      Riccardo Valdagni, Peter T. Scardino and Louis Denis

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24342

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      Introductory remarks on this supplemental issue to Cancer are presented.

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      Development of a prostate cancer metagram : A solution to the dilemma of which prediction tool to use in patient counseling (pages 3039–3045)

      Carvell T. Nguyen and Michael W. Kattan

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24355

      Prostate cancer prediction tools offer patients estimates of treatment-related outcomes. A prostate cancer metagram is needed that incorporates the most accurate tools currently available. The metagram would facilitate patient counseling by clinicians and guide researchers to develop new and more accurate prediction models.

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      Genomic approaches to outcome prediction in prostate cancer (pages 3046–3057)

      Phillip G. Febbo

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24350

      Applications of emerging genomic technologies to prostate cancer in multiple contexts have made significant contributions to current molecular understanding of the development and progression of prostate cancer. In this review, the author highlighted recent work in genomics and its role in evaluating molecular modifiers of prostate cancer risk and behavior and the development of predictive models that anticipate the risk of developing prostate cancer, prostate cancer progression, and the response of prostate cancer to therapy.

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      Biomolecular markers of outcome prediction in prostate cancer (pages 3058–3067)

      Alessia Lopergolo and Nadia Zaffaroni

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24346

      The number of candidate biological markers of prognosis and/or response to specific treatments in prostate cancer continues to grow, mainly because of the advent of high-throughput methods. However, several issues need to be addressed, including: 1) the development of uniform standards for marker measurement to enable comparison across studies; 2) clinical validation of the actual predictive value of biomarkers in prospective clinical trials; and 3) evaluation of the ability of these markers to improve the predictive accuracy of established prediction models (nomograms).

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      Decision support systems for morphology-based diagnosis and prognosis of prostate neoplasms : A methodological approach (pages 3068–3077)

      Rodolfo Montironi, Liang Cheng, Antonio Lopez-Beltran, Roberta Mazzucchelli, Marina Scarpelli and Peter H. Bartels

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24345

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      Recent advances in computer and information technologies have allowed the integration of both numeric and non-numeric data, that is, descriptive, linguistic terms. This has led at 1 end of the spectrum of technology development to machine vision based on image understanding and, at the other, to decision support systems. This has had a significant impact on our capability to derive diagnostic and prognostic information from histopathological material with prostate neoplasms.

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      Systems pathology : A paradigm shift in the practice of diagnostic and predictive pathology (pages 3078–3084)

      Michael J. Donovan, Jose Costa and Carlos Cordon-Cardo

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24353

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      Systems pathology represents a methodological shift in how traditional diagnostic pathology is currently performed using deparaffinized tissue sections. The technical advances outlined in this article exemplify ways in which phenotypic characteristics contained within the diagnostic specimen can be routinely extracted and used, in a clinical setting, for routine patient management. Incorporation of a systems-based approach through functional histology will provide a framework for further advancing personalized medicine and selective therapy design.

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      Techniques and predictive models to improve prostate cancer detection (pages 3085–3099)

      Michael P. Herman, Philip Dorsey, Majnu John, Nishant Patel, Robert Leung and Ashutosh Tewari

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24357

      It has been demonstrated previously that predictive tools are more accurate than clinical judgment for decision making. For this report, the authors reviewed various statistical and computational models in prostate cancer detection, highlighting their accuracy as well as their limitations.

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      Predictive models in diagnosing indolent cancer (pages 3100–3106)

      Chris H. Bangma, Monique J. Roobol and Ewout W. Steyerberg

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24347

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      The ability to predict indolent prostate cancer is needed so that men with indolent disease may be selected for active surveillance. Currently available nomograms are based on clinical patient series or on men from a screening population; thus, the general application of those nomograms is restricted.

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      Nomograms for staging, prognosis, and predicting treatment outcomes (pages 3107–3111)

      Karim Touijer and Peter T. Scardino

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24352

      Prostate cancer is a heterogeneous disease with a wide prognostic spectrum and a variety of treatment options, which has led to uncertainty in risk assessment and prediction of outcome. Nomograms for prostate cancer have been applied to many clinical states and outcomes and have provided the most accurate predictions.

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      Predictive models in external beam radiotherapy for clinically localized prostate cancer (pages 3112–3120)

      Mack Roach III, Fred Waldman and Alan Pollack

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24348

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      Most predictive models for men who receive external beam radiotherapy with curative intent for clinically localized prostate cancer have been based on pretreatment clinical and pathologic-related variables, including Gleason score, tumor classification, and prostate-specific antigen. Some models also have incorporated treatment-related variables, such as radiation dose and androgen-deprivation therapy. More recently, Radiation Therapy Oncology Group trials have investigated the use of several biomarkers, but to the authors' knowledge, these markers are not ready for widespread, routine use.

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      The current state of brachytherapy nomograms for patients with clinically localized prostate cancer (pages 3121–3127)

      Carvell T. Nguyen, Michael J. Zelefsky and Michael W. Kattan

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24344

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      Prostate cancer nomograms currently provide the most accurate estimates of treatment outcomes to aid in patient counseling and decision making. Nomograms assessing the efficacy of brachytherapy are rare, and development of new and highly accurate models will be required to allow patients to make informed treatment decisions.

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      Predictive models in palliative care (pages 3128–3134)

      Carla Ida Ripamonti, Gabriella Farina and Marina Chiara Garassino

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24351

      In the current study, the authors reviewed the literature to identify the major factors that can predict survival in patients with solid tumors. This article reviews the literature and assesses prognostic and predictive models of life expectancy.

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      Predictive models of toxicity in external radiotherapy : Dosimetric issues (pages 3135–3140)

      Claudio Fiorino, Tiziana Rancati and Riccardo Valdagni

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24354

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      Dose-volume modeling of late and acute toxicities caused by radiotherapy for prostate cancer were reviewed. Very reliable dose-volume models for late and acute rectal toxicity were available; whereas models of intestinal toxicity, genitourinary toxicity, and erectile dysfunction still need to be improved.

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      Predictive models of toxicity with external radiotherapy for prostate cancer : Clinical issues (pages 3141–3149)

      Riccardo Valdagni, Tiziana Rancati and Claudio Fiorino

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24356

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      This study presents an analysis of the state of the art and current limitations of available, clinically usable models predicting the risk of genitourinary tract and small bowel complications, erectile dysfunction, and acute and late symptoms of the rectal syndrome caused by external irradiation for prostate cancer.

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      Prediction of sexual function after radical prostatectomy (pages 3150–3159)

      Alberto Briganti, Umberto Capitanio, Felix K.-H. Chun, Pierre I. Karakiewicz, Andrea Salonia, Marco Bianchi, Andrea Cestari, Giorgio Guazzoni, Patrizio Rigatti and Francesco Montorsi

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24349

      In this report, the authors critically analyzed the factors associated with recovery of erectile function after radical prostatectomy. Accurate patient selection, adequate surgical technique, and appropriate postoperative pharmacologic treatment represented the major determinants of postoperative erectile function recovery. Therefore, the authors concluded that an ideal multivariate model predicting the restoration of erectile function after surgery should include patient, surgeon, and postsurgical treatment variables.

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      Conclusions and reflections (pages 3160–3162)

      Louis J. Denis and Mary K. Gospodarowicz

      Version of Record online: 19 JUN 2009 | DOI: 10.1002/cncr.24343

      Predictive modeling has become a standard exercise in clinical decision making. However, optimal treatment is directed toward the individual patient, with prognostic factor evaluation of the tumor, host, and environment.

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