Literature DB >> 12349932

Methodological issues in radiation dose-volume outcome analyses: summary of a joint AAPM/NIH workshop.

Joseph O Deasy1, Andrzej Niemierko, Donald Herbert, Di Yan, Andrew Jackson, Randall K Ten Haken, Mark Langer, Steve Sapareto.   

Abstract

This report represents a summary of presentations at a joint workshop of the National Institutes of Health and the American Association of Physicists in Medicine (AAPM). Current methodological issues in dose-volume modeling are addressed here from several different perspectives. Areas of emphasis include (a) basic modeling issues including the equivalent uniform dose framework and the bootstrap method, (b) issues in the valid use of statistics, including the need for meta-analysis, (c) issues in dealing with organ deformation and its effects on treatment response, (d) evidence for volume effects for rectal complications, (e) the use of volume effect data in liver and lung as a basis for dose escalation studies, and (f) implications of uncertainties in volume effect knowledge on optimized treatment planning. Taken together, these approaches to studying volume effects describe many implications for the development and use of this information in radiation oncology practice. Areas of significant interest for further research include the meta-analysis of clinical data; interinstitutional pooled data analyses of volume effects; analyses of the uncertainties in outcome prediction models, minimal parameter number outcome models for ranking treatment plans (e.g., equivalent uniform dose); incorporation of the effect of motion in the outcome prediction; dose-escalation/isorisk protocols based on outcome models; the use of functional imaging to study radioresponse; and the need for further small animal tumor control probability/normal tissue complication probability studies.

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Year:  2002        PMID: 12349932     DOI: 10.1118/1.1501473

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  13 in total

1.  Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues.

Authors:  Søren M Bentzen; Louis S Constine; Joseph O Deasy; Avi Eisbruch; Andrew Jackson; Lawrence B Marks; Randall K Ten Haken; Ellen D Yorke
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

2.  Use of normal tissue complication probability models in the clinic.

Authors:  Lawrence B Marks; Ellen D Yorke; Andrew Jackson; Randall K Ten Haken; Louis S Constine; Avraham Eisbruch; Søren M Bentzen; Jiho Nam; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

3.  A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data.

Authors:  Jung Hun Oh; Jeffrey M Craft; Reid Townsend; Joseph O Deasy; Jeffrey D Bradley; Issam El Naqa
Journal:  J Proteome Res       Date:  2011-02-16       Impact factor: 4.466

4.  Treatment planning evaluation and optimization should be biologically and not dose/volume based.

Authors:  Joseph O Deasy; Charles S Mayo; Colin G Orton
Journal:  Med Phys       Date:  2015-06       Impact factor: 4.071

5.  Datamining approaches for modeling tumor control probability.

Authors:  Issam El Naqa; Joseph O Deasy; Yi Mu; Ellen Huang; Andrew J Hope; Patricia E Lindsay; Aditya Apte; James Alaly; Jeffrey D Bradley
Journal:  Acta Oncol       Date:  2010-03-02       Impact factor: 4.089

6.  A Bayesian network approach for modeling local failure in lung cancer.

Authors:  Jung Hun Oh; Jeffrey Craft; Rawan Al Lozi; Manushka Vaidya; Yifan Meng; Joseph O Deasy; Jeffrey D Bradley; Issam El Naqa
Journal:  Phys Med Biol       Date:  2011-02-18       Impact factor: 3.609

7.  Evaluation of late rectal toxicity after conformal radiotherapy for prostate cancer: a comparison between dose-volume constraints and NTCP use.

Authors:  Raffaella Cambria; Barbara A Jereczek-Fossa; Federica Cattani; Cristina Garibaldi; Dario Zerini; Cristiana Fodor; Flavia Serafini; Guido Pedroli; Roberto Orecchia
Journal:  Strahlenther Onkol       Date:  2009-06-09       Impact factor: 3.621

8.  Predicting radiotherapy outcomes using statistical learning techniques.

Authors:  Issam El Naqa; Jeffrey D Bradley; Patricia E Lindsay; Andrew J Hope; Joseph O Deasy
Journal:  Phys Med Biol       Date:  2009-08-18       Impact factor: 3.609

9.  Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes.

Authors:  Jack Y Yang; Andrzej Niemierko; Mary Qu Yang; Youping Deng
Journal:  BMC Genomics       Date:  2008-09-16       Impact factor: 3.969

10.  Bioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data.

Authors:  Sarah J Spencer; Damian Almiron Bonnin; Joseph O Deasy; Jeffrey D Bradley; Issam El Naqa
Journal:  J Biomed Biotechnol       Date:  2009-05-28
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