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Prostate cancer: prediction of biochemical failure after external-beam radiation therapy--Kattan nomogram and endorectal MR imaging estimation of tumor volume.

ABSTRACT: To determine whether magnetic resonance (MR) imaging and MR spectroscopic imaging findings can improve predictions made with the Kattan nomogram for radiation therapy.The institutional review board approved this retrospective HIPAA-compliant study. Ninety-nine men who underwent endorectal MR and MR spectroscopy before external-beam radiation therapy for prostate cancer (January 1998 to June 2007) were included. Linear predictors were calculated with input variables from the study sample and the Kattan original coefficients. The linear predictor is a single weighted value that combines information of all predictor variables in a model, where the weight of each value is its association with the outcome. Two radiologists independently reviewed all MR images to determine extent of disease; a third independent reader resolved discrepancies. Biochemical failure was defined as a serum prostate-specific antigen level of 2 ng/mL (2 μg/L) or more above nadir. Cox proportional hazard models were used to determine the probabilities of treatment failure (biochemical failure) in 5 years. One model included only the Kattan nomogram data; the other also incorporated imaging findings. The discrimination performance of all models was determined with receiver operating characteristics (ROC) curve analyses. These analyses were followed by an assessment of net risk reclassification.The areas under the ROC curve for the Kattan nomogram and the model incorporating MR imaging findings were 61.1% (95% confidence interval: 58.1%, 64.0%) and 78.0% (95% confidence interval: 75.7%, 80.4%), respectively. Comparison of performance showed that the model with imaging findings performed significantly better than did the model with clinical variables alone (P < .001). Overall, the addition of imaging findings led to an improvement in risk classification of about 28%, ranging from approximately a minimum of 16% to a maximum of 39%, depending on the risk change considered important.MR imaging data improve the prediction of biochemical failure with the Kattan nomogram after external-beam radiation therapy for prostate cancer. The number needed to image to improve the prediction of biochemical failure in one patient ranged from three to six.

SUBMITTER: Westphalen AC

PROVIDER: S-EPMC3198223 | biostudies-other | 2011 Nov

REPOSITORIES: biostudies-other

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Project description:PurposeBeam angle optimization is a critical issue for modern radiotherapy (RT) and is a challenging task, especially for large body sizes and noncoplanar designs. Noncoplanar RT techniques may have dosimetric advantages but increase the risk of mechanical collision. We propose a software solution to accurately predict colliding/noncolliding configurations for coplanar and noncoplanar beams.Materials and methodsIndividualized software models for two different linear accelerators were built to simulate noncolliding gantry orientations for phantom/patient subjects. The sizes and shapes of the accelerators were delineated based on their manuals and on-site measurements. The external surfaces of the subjects were automatically contoured based on computed tomography (CT) simulations. An Alderson Radiation Therapy phantom was used to predict the accuracy of spatial collision prediction by the software. A gantry collision problem encountered by one patient during initial setup was also used to test the validity of the software. Results: In the comparison between the software estimates and on-site measurements, the noncoplanar collision angles were all predicted within a 5-degree difference in gantry position. The confusion matrix was calculated for each of the two empty accelerator models, and the accuracies were 98.7% and 97.3%. The true positive rates were 97.7% and 96.9%, while the true negative rates were 99.8% and 97.9%, respectively. For the phantom study, the collision angles were predicted within a 5-degree difference. The software successfully predicted the collision problem encountered by the breast cancer patient in the initial setup position and generated shifted coordinates that were validated to correspond to a noncolliding geometry.ConclusionThe developed software effectively and accurately predicted collisions for accelerator-only, phantom, and patient setups. This software may help prevent collisions and expand the range of spatially applicable beam angles.

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Prostate cancer: prediction of biochemical failure after external-beam radiation therapy--Kattan nomogram and endorectal MR imaging estimation of tumor volume.

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