WE-D-BRE-05: Prediction of Late Radiation-Induced Proctitis in Prostate Cancer Patients Using Chromosome Aberration and Cell Proliferation Rate

Authors


Abstract

Purpose:

Chromosome damage and cell proliferation rate have been investigated as potential biomarkers for the early prediction of late radiationinduced toxicity. Incorporating these endpoints, we explored the predictive power for late radiation proctitis using a machine learning method.

Methods:

Recently, Beaton et al. showed that chromosome aberration and cell proliferation rate could be used as biomarkers to predict late radiation proctitis (Beaton et al. (2013) Int J Rad Onc Biol Phys, 85:1346–1352). For the identification of radiosensitive biomarkers, blood samples were collected from 10 patients with grade 3 late proctitis along with 20 control patients with grade 0 proctitis. After irradiation at 6 Gy, statistically significant difference was observed between the two groups, using the number of dicentrics and excess fragments, and the number of cells in metaphase 2 (M2). However, Beaton et al. did not show the usefulness of combining these endpoints. We reanalyzed the dataset to investigate whether incorporating these endpoints can increase the predictive power of radiation proctitis, using a support vector machine (SVM).

Results:

Using the SVM method with the number of fragments and M2 endpoints, perfect classification was achieved. In addition, to avoid biased estimate of the classification method, leave-one-out cross-validation (LOO-CV) was performed. The best performance was achieved when all three endpoints were used with 87% accuracy, 90% sensitivity, 85% specificity, and 0.85 AUC (the area under the receiver operating characteristic (ROC) curve). The most significant endpoint was the number of fragments that obtained 83% accuracy, 70% sensitivity, 90% specificity, and 0.82 AUC.

Conclusion:

We demonstrated that chromosome damage and cell proliferation rate could be significant biomarkers to predict late radiation proctitis. When these endpoints were used together in conjunction with a machine learning method, the better performance was obtained. However, evaluation for other late radiation toxicities is necessary to elucidate these observations.

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