Predicting radiotherapy-induced cardiac perfusion defects

Shiva K. Das, Alan H. Baydush, Sumin Zhou, Moyed Miften, Xiaoli Yu, Oana Craciunescu, Mark Oldham, Kim Light, Terence Wong, Michael Blazing, Salvador Borges-Neto, Mark W. Dewhirst, Lawrence B. Marks

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

The purpose of this work is to compare the efficacy of mathematical models in predicting the occurrence of radiotherapy-induced left ventricular perfusion defects assessed using single-photon emission computed tomography (SPECT). The basis of this study is data from 73 left-sided breast/ chestwall patients treated with tangential photon fields. The mathematical models compared were three commonly used parametric models [Lyman normal tissue complication probability (LNTCP), relative serialty (RS), generalized equivalent uniform dose (gEUD)] and a nonparametric model (Linear discriminant analysis-LDA). Data used by the models were the left ventricular dose-volume histograms, or SPECT-based dose-function histograms, and the presence/absence of SPECT perfusion defects 6 months postradiation therapy (21 patients developed defects). For the parametric models, maximum likelihood estimation and F-tests were used to fit the model parameters. The nonparametric LDA model step-wise selected features (volumes/function above dose levels) using a method based on receiver operating characteristics (ROC) analysis to best separate the groups with and without defects. Optimistic (upper bound) and pessimistic (lower bound) estimates of each model's predictive capability were generated using ROC curves. A higher area under the ROC curve indicates a more accurate model (a model that is always accurate has area = 1). The areas under these curves for different models were used to statistically test for differences between them. Pessimistic estimates of areas under the ROC curve using dose-volume histogram/ dose-function histogram inputs, in order of increasing prediction accuracy, were LNTCP (0.79/0.75), RS (0.80/0.77), gEUD (0.81/0.78), and LDA (0.84/0.86). Only the LDA model benefited from SPECT-based regional functional information. In general, the LDA model was statistically superior to the parametric models. The LDA model selected as features the left ventricular volumes above approximately 23 Gy (V 23 ), essentially volume in field, and 33 Gy (V 33 ), as best separating the groups with and without defects. In conclusion, the nonparametric LDA model appears to be a more accurate predictor of radiotherapy-induced left ventricular perfusion defects than commonly used parametric models.

Original languageEnglish (US)
Pages (from-to)19-27
Number of pages9
JournalMedical Physics
Volume32
Issue number1
DOIs
StatePublished - Jan 1 2005

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Single-Photon Emission-Computed Tomography
ROC Curve
Radiotherapy
Perfusion
Theoretical Models
Discriminant Analysis
Photons
Area Under Curve
Linear Models
Breast
Therapeutics

Keywords

  • Defect
  • Perfusion
  • Radiation
  • Ventricle

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Das, S. K., Baydush, A. H., Zhou, S., Miften, M., Yu, X., Craciunescu, O., ... Marks, L. B. (2005). Predicting radiotherapy-induced cardiac perfusion defects. Medical Physics, 32(1), 19-27. https://doi.org/10.1118/1.1823571

Predicting radiotherapy-induced cardiac perfusion defects. / Das, Shiva K.; Baydush, Alan H.; Zhou, Sumin; Miften, Moyed; Yu, Xiaoli; Craciunescu, Oana; Oldham, Mark; Light, Kim; Wong, Terence; Blazing, Michael; Borges-Neto, Salvador; Dewhirst, Mark W.; Marks, Lawrence B.

In: Medical Physics, Vol. 32, No. 1, 01.01.2005, p. 19-27.

Research output: Contribution to journalArticle

Das, SK, Baydush, AH, Zhou, S, Miften, M, Yu, X, Craciunescu, O, Oldham, M, Light, K, Wong, T, Blazing, M, Borges-Neto, S, Dewhirst, MW & Marks, LB 2005, 'Predicting radiotherapy-induced cardiac perfusion defects', Medical Physics, vol. 32, no. 1, pp. 19-27. https://doi.org/10.1118/1.1823571
Das SK, Baydush AH, Zhou S, Miften M, Yu X, Craciunescu O et al. Predicting radiotherapy-induced cardiac perfusion defects. Medical Physics. 2005 Jan 1;32(1):19-27. https://doi.org/10.1118/1.1823571
Das, Shiva K. ; Baydush, Alan H. ; Zhou, Sumin ; Miften, Moyed ; Yu, Xiaoli ; Craciunescu, Oana ; Oldham, Mark ; Light, Kim ; Wong, Terence ; Blazing, Michael ; Borges-Neto, Salvador ; Dewhirst, Mark W. ; Marks, Lawrence B. / Predicting radiotherapy-induced cardiac perfusion defects. In: Medical Physics. 2005 ; Vol. 32, No. 1. pp. 19-27.
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