Derivation and representation of dose-volume response from large clinical trial data sets: an example from the RADAR prostate radiotherapy trial

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Ebert, M.A.^1,2 ; Foo, K.^3,4 ; Haworth, A.^5,6 ; Gulliford, S.L.^7 ; Kearvall, R.^1 ; Kennedy, A.^1 ; Richardson, S.^1 ; Krawiec, M.^1 ; Stewart, N.^1 ; Joseph, D.J.^1,8 ; Denham, J.W.^9

Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia^1

School of Physics, University of Western Australia, Perth, Australia^2

Radiation Oncology, Sydney Cancer Centre, Sydney, Australia^3

University of Sydney, Sydney, Australia^4

Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia^5

University of Melbourne, Melbourne, Australia^6

Joint Department of Physics, Institute of Cancer Research, Royal Marsden National Health Service Foundation Trust, Sutton, United Kingdom^7

School of Surgery, University of Western Australia, Perth, Australia^8

University of Newcastle, Newcastle, Australia^9

关键词: Clinical trial;    Graphical representations;    Graphical technique;    Logistic regressions;    Prostate radiotherapy;    Relational Database;    ROC analysis;    Toxicity data;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012090/pdf
DOI  :  10.1088/1742-6596/489/1/012090

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Large multicentre radiotherapy trials incorporating assessment of multiple outcomes at multiple timepoints can generate extensive datasets. We have investigated graphical techniques for presentation of this data and the associated underlying dose-volume response information, necessary for guiding statistical analyses and translating outcomes to future patient treatments. A relational database was used to archive reviewed plan data for patients accrued to the TROG 03.04 RADAR trial. Viewing software was used to clean and enhance the data. Scripts were developed to export arbitrary dose-histogram data which was combined with clinical toxicity data with a median follow-up of 72 months. Graphical representations of dose-volume response developed include prevalence atlasing, univariate logistic regression and dose-volume-point odds ratios, and continuous cut-point derivation via ROC analysis. These representations indicate variable association of toxicities across structures and time-points.