Literature DB >> 26395528

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine.

Philippe Lambin1, Jaap Zindler1, Ben Vanneste1, Lien van de Voorde1, Maria Jacobs1, Daniëlle Eekers1, Jurgen Peerlings1, Bart Reymen1, Ruben T H M Larue1, Timo M Deist1, Evelyn E C de Jong1, Aniek J G Even1, Adriana J Berlanga1, Erik Roelofs1, Qing Cheng1, Sara Carvalho1, Ralph T H Leijenaar1, Catharina M L Zegers1, Evert van Limbergen1, Maaike Berbee1, Wouter van Elmpt1, Cary Oberije1, Ruud Houben1, Andre Dekker1, Liesbeth Boersma1, Frank Verhaegen1, Geert Bosmans1, Frank Hoebers1, Kim Smits1, Sean Walsh1.   

Abstract

BACKGROUND: Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided. AIM: The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm. DISCUSSION: Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

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Year:  2015        PMID: 26395528     DOI: 10.3109/0284186X.2015.1062136

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  21 in total

Review 1.  Radiomics: the bridge between medical imaging and personalized medicine.

Authors:  Philippe Lambin; Ralph T H Leijenaar; Timo M Deist; Jurgen Peerlings; Evelyn E C de Jong; Janita van Timmeren; Sebastian Sanduleanu; Ruben T H M Larue; Aniek J G Even; Arthur Jochems; Yvonka van Wijk; Henry Woodruff; Johan van Soest; Tim Lustberg; Erik Roelofs; Wouter van Elmpt; Andre Dekker; Felix M Mottaghy; Joachim E Wildberger; Sean Walsh
Journal:  Nat Rev Clin Oncol       Date:  2017-10-04       Impact factor: 66.675

2.  Prospects and challenges for clinical decision support in the era of big data.

Authors:  Issam El Naqa; Michael R Kosorok; Judy Jin; Michelle Mierzwa; Randall K Ten Haken
Journal:  JCO Clin Cancer Inform       Date:  2018-11-09

3.  How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?

Authors:  Barry S Rosenstein; Jacek Capala; Jason A Efstathiou; Jeff Hammerbacher; Sarah L Kerns; Feng-Ming Spring Kong; Harry Ostrer; Fred W Prior; Bhadrasain Vikram; John Wong; Ying Xiao
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-11-11       Impact factor: 7.038

Review 4.  Machine Learning and Imaging Informatics in Oncology.

Authors:  Huan-Hsin Tseng; Lise Wei; Sunan Cui; Yi Luo; Randall K Ten Haken; Issam El Naqa
Journal:  Oncology       Date:  2018-11-23       Impact factor: 2.935

Review 5.  Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement.

Authors:  Dirk De Ruysscher; Gilles Defraene; Bram L T Ramaekers; Philippe Lambin; Erik Briers; Hilary Stobart; Tim Ward; Søren M Bentzen; Tjeerd Van Staa; David Azria; Barry Rosenstein; Sarah Kerns; Catharine West
Journal:  Radiother Oncol       Date:  2016-12-12       Impact factor: 6.280

6.  A phase 1 'window-of-opportunity' trial testing evofosfamide (TH-302), a tumour-selective hypoxia-activated cytotoxic prodrug, with preoperative chemoradiotherapy in oesophageal adenocarcinoma patients.

Authors:  Ruben T H M Larue; Lien Van De Voorde; Maaike Berbée; Wouter J C van Elmpt; Ludwig J Dubois; Kranthi M Panth; Sarah G J A Peeters; Ann Claessens; Wendy M J Schreurs; Marius Nap; Fabiënne A R M Warmerdam; Frans L G Erdkamp; Meindert N Sosef; Philippe Lambin
Journal:  BMC Cancer       Date:  2016-08-17       Impact factor: 4.430

Review 7.  Prostate Cancer Radiation Therapy: What Do Clinicians Have to Know?

Authors:  Ben G L Vanneste; Evert J Van Limbergen; Emile N van Lin; Joep G H van Roermund; Philippe Lambin
Journal:  Biomed Res Int       Date:  2016-12-28       Impact factor: 3.411

8.  Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study.

Authors:  Yvonka van Wijk; Bram Ramaekers; Ben G L Vanneste; Iva Halilaj; Cary Oberije; Avishek Chatterjee; Tom Marcelissen; Arthur Jochems; Henry C Woodruff; Philippe Lambin
Journal:  Cancers (Basel)       Date:  2021-05-29       Impact factor: 6.639

Review 9.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24

10.  Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients.

Authors:  Nai-Ming Cheng; Yu-Hua Dean Fang; Din-Li Tsan; Ching-Han Hsu; Tzu-Chen Yen
Journal:  PLoS One       Date:  2016-03-01       Impact factor: 3.240

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