title: Predicting radiotherapy-induced cardiac perfusion defects.
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 (V23), essentially volume in field, and 33 Gy (V33), 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.
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA. shiva@radonc.duke.edu
authors.forename: Shiva K
authors.initials: SK
authors.lastname: Das
authors.forename: Alan H
authors.initials: AH
authors.lastname: Baydush
authors.forename: Sumin
authors.initials: S
authors.lastname: Zhou
authors.forename: Moyed
authors.initials: M
authors.lastname: Miften
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Yu
authors.forename: Oana
authors.initials: O
authors.lastname: Craciunescu
authors.forename: Mark
authors.initials: M
authors.lastname: Oldham
authors.forename: Kim
authors.initials: K
authors.lastname: Light
authors.forename: Terence
authors.initials: T
authors.lastname: Wong
authors.forename: Michael
authors.initials: M
authors.lastname: Blazing
authors.forename: Salvador
authors.initials: S
authors.lastname: Borges-Neto
authors.forename: Mark W
authors.initials: MW
authors.lastname: Dewhirst
authors.forename: Lawrence B
authors.initials: LB
authors.lastname: Marks
mesh_terms: D004307:Dose-Response Relationship, Radiation; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D010477:Perfusion; D017785:Photons; D012372:ROC Curve; D011878:Radiotherapy / Q000009:adverse effects*; D011879:Radiotherapy Dosage; D012680:Sensitivity and Specificity; D013997:Time Factors; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis* / Q000209:etiology; D016277:Ventricular Function, Left / Q000528:radiation effects*