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      Is Open Access

      Decision Support Systems in Prostate Cancer Treatment: An Overview

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          Abstract

          Background

          A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking.

          Methods

          We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals.

          Results

          Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS.

          Conclusions

          Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.

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          Most cited references56

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          Rapid-learning system for cancer care.

          J Etheredge, Patrick B. Murphy, B Bach … (2010)
          Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is "rapid-learning health care." In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled "A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care" in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action.
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            A new risk classification system for therapeutic decision making with intermediate-risk prostate cancer patients undergoing dose-escalated external-beam radiation therapy.

            Zachary Zumsteg, Isaac Pei, Marisa Kollmeier … (2013)
            The management of intermediate-risk prostate cancer (PCa) is controversial, in part due to the heterogeneous nature of patients falling within this classification. We propose a new risk stratification system for intermediate-risk PCa to aid in prognosis and therapeutic decision making. Between 1992 and 2007, 1024 patients with National Comprehensive Cancer Network intermediate-risk PCa and complete biopsy information were treated with definitive external-beam radiation therapy (EBRT) utilizing doses ≥ 81 Gy. Unfavorable intermediate-risk (UIR) PCa was defined as any intermediate-risk patient with a primary Gleason pattern of 4, percentage of positive biopsy cores (PPBC) ≥ 50%, or multiple intermediate-risk factors (IRFs; cT2b-c, prostate-specific antigen [PSA] 10-20, or Gleason score 7). All patients received EBRT with ≥ 81 Gy with or without neoadjuvant and concurrent androgen-deprivation therapy (ADT). Univariate and multivariate analyses were performed using a Cox proportional hazards model for PSA recurrence-free survival (PSA-RFS) and distant metastasis (DM). PCa-specific mortality (PCSM) was analyzed using a competing-risk method. Median follow-up was 71 mo. Primary Gleason pattern 4 (hazard ratio [HR]: 3.26; p<0.0001), PPBC ≥ 50% (HR: 2.72; p=0.0007), and multiple IRFs (HR: 2.20; p=0.008) all were significant predictors of increased DM in multivariate analyses. Primary Gleason pattern 4 (HR: 5.23; p<0.0001) and PPBC ≥ 50% (HR: 4.08; p=0.002) but not multiple IRFs (HR: 1.74; p=0.21) independently predicted for increased PCSM. Patients with UIR disease had inferior PSA-RFS (HR: 2.37; p<0.0001), DM (HR: 4.34; p=0.0003), and PCSM (HR: 7.39; p=0.007) compared with those with favorable intermediate-risk disease, despite being more likely to receive neoadjuvant ADT. Short follow-up and retrospective study design are the primary limitations. Intermediate-risk PCa is a heterogeneous collection of diseases that can be separated into favorable and unfavorable subsets. These groups likely will benefit from divergent therapeutic paradigms. Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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              New Guideline for the Reporting of Studies Developing, Validating, or Updating a Multivariable Clinical Prediction Model: The TRIPOD Statement.

              Karel Moons, Douglas G. Altman, Johannes Reitsma … (2015)
              Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include models to predict treatment outcomes or responses; in the cancer literature often referred to as predictive models. Clinical prediction models have become abundant. Pathology measurement or results are frequently included as predictors in such prediction models, certainly in the cancer domain. Only when full information on all aspects of a prediction modeling study are clearly reported, risk of bias and potential usefulness of the prediction model can be adequately assessed. Many reviews have illustrated that the quality of reports on the development, validation, and/or adjusting (updating) of prediction models, is very poor. Hence, the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative has developed a comprehensive and user-friendly checklist for the reporting of studies on, both diagnostic and prognostic, prediction models. The TRIPOD Statement intends to improve the transparency and completeness of reporting of studies that report solely on development, both development and validation, and solely on the validation (with or without updating) of diagnostic or prognostic, including predictive, models.
              Article 0 comments Cited 64 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Y. van Wijk:
                ORCID: http://orcid.org/0000-0001-9389-8005
                Journal
                Journal ID (nlm-ta): Biomed Res Int
                Journal ID (iso-abbrev): Biomed Res Int
                Journal ID (publisher-id): BMRI
                Title: BioMed Research International
                Publisher: Hindawi
                ISSN (Print): 2314-6133
                ISSN (Electronic): 2314-6141
                Publication date Collection: 2019
                Publication date (Electronic): 6 June 2019
                Volume: 2019
                Electronic Location Identifier: 4961768
                Affiliations
                1The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC, Maastricht University Medical Center, Maastricht, Netherlands
                2Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
                Author notes

                Guest Editor: Shadnaz Asgari

                Author information
                http://orcid.org/0000-0001-9389-8005
                http://orcid.org/0000-0003-2334-5207
                Article
                DOI: 10.1155/2019/4961768
                PMC ID: 6590598
                PubMed ID: 31281840
                SO-VID: 4d5da1ad-5d7c-4b46-83c9-6d2f90571733
                Copyright © Copyright © 2019 Y. van Wijk et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 26 October 2018
                Date revision received : 2 April 2019
                Date accepted : 6 May 2019
                Categories
                Subject: Review Article

                Data availability:

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