Dosimetric assessment of prostate cancer patients through principal component analysis (PCA)

Aime M Gloi,Robert Buchanan

doi:10.1120/jacmp.v14i1.3882

Abstract

The aims of this study were twofold: first, to determine the impact of variance in dose‐volume histograms (DVH) on patient‐specific toxicity after 2 high‐dose fractions in a sample of 22 men with prostate cancer; and second, to compare the effectiveness of traditional DVH analysis and principal component analysis (PCA) in predicting rectum and urethra toxicity. A series of 22 patients diagnosed with prostate adenocarcinoma was treated with 45 Gy external beam and 20 Gy dose rate brachytherapy. Principal component analysis was applied to model the shapes of the rectum and urethra dose‐volume histograms. We used logistic regression to measure the correlations between the principal components and the incidence of rectal bleeding and urethra stricture. We also calculated the equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) for the urethra and rectum, and tumor control probability (TCP) for the prostate using BioSuite software. We evaluated their correlations with rectal and urethra toxicity. The rectum DVHs are well described by one principal component (PC1), which accounts for 93.5% of the variance in their shapes. The urethra DVHs are described by two principal components, PC1 and PC2, which account for 94.98% and 3.15% of the variance, respectively. Multivariate exact logistic regression suggests that urethra PC2 is a good predictor of stricture, with Nagelkerke's R2 estimated at 0.798 and a Wald criterion of 5.421 (p<0.021). The average NTCPs were 0.06%±0.04% and 1.25%±0.22% for the rectum and urethra, respectively. The average TCP was 85.29%±2.28%. This study suggests that principal component analysis can be used to identify the shape variation in dose‐volume histograms, and that the principal components can be correlated with the toxicity of a treatment plan based on multivariate analysis. The principal components are also correlated with traditional dosimetric parameters.PACS number: 3.6.96.0

Highlights

Adenocarcinoma of the prostate is a slowly growing tumor, with an incidence of 25.3 per 100,000.(1) Its treatment options include laparoscopic or radical prostatectomy, external beam radiation therapy (EBRT), EBRT with high-dose rate (HDR) delivered by remote afterloading brachytherapy, HDR remote afterloading brachytherapy alone, and permanent source interstitial low-dose brachytherapy
equivalent uniform dose (EUD), tumor control probability (TCP), and normal tissue complication probability (NTCP) were estimated for each treatment plan using BioSuite, a radiobiological software tool.[22]. We define EUD, based on the idea originally proposed by Niemierko et al,(10) as the uniformly distributed dose that will lead to the same level of cell killing as a given nonuniform distribution
We have shown that principal component analysis (PCA) can be used to provide information on the most meaningful parameters describing the whole dataset, that it is useful for data reduction, and that it accurately summarizes the statistical correlations among variables related to prostate HDR

Summary

Introduction

Adenocarcinoma of the prostate is a slowly growing tumor, with an incidence of 25.3 per 100,000.(1) Its treatment options include laparoscopic or radical prostatectomy, external beam radiation therapy (EBRT), EBRT with high-dose rate (HDR) delivered by remote afterloading brachytherapy, HDR remote afterloading brachytherapy alone, and permanent source interstitial low-dose brachytherapy. Of the present treatment options, prostate brachytherapy has become widespread because it delivers a lower dose to organs in the vicinity, while giving the maximum dose to the prostate. The modalities with higher dose rates have been associated with better tumor control. The prevalence of three-dimensional (3D) models in treatment planning, namely volumetric datasets and dose-volume histograms (DVHs), has been important in quantifying and predicting treatment outcomes. A DVH allows for the computation of essential biological parameters such as tumor control probability (TCP) and normal tissue complication probability (NTCP). Researchers are using different methods and parameters to construct their DVHs.[2,3,6,7,8,9] For example, one stream of literature debates the merits of correlating a single DVH with the maximum dose or the use-equivalent uniform dose (EUD)(10,11) with toxicity.[5,12,13]

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