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Appl. Sci. 2018, 8(9), 1620; https://doi.org/10.3390/app8091620

The Prostate Clinical Outlook (PCO) Classifier Application for Predicting Biochemical Recurrences in Patients Treated by Stereotactic Body Radiation Therapy (SBRT)

1
Arlington Innovation Center, Virginia Tech, 900 N Glebe Road, Arlington, VA 22203, USA
2
Open Source Electronic Health Record Alliance, 1934 Old Gallows Road Suite 420, Vienna, VA 22182, USA
3
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222, Banpo-dong, Seocho-gu, Seoul 06591, Korea
4
Department of Radiation Medicine, Georgetown University Hospital, 3800 Reservoir Rd. NW, Washington, DC 20007, USA
*
Author to whom correspondence should be addressed.
Received: 18 June 2018 / Revised: 3 August 2018 / Accepted: 7 August 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
Full-Text   |   PDF [1469 KB, uploaded 12 September 2018]   |  

Abstract

(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D’Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (n = 137), 95.4% for intermediate risk (n = 570), and 86.4% for high risk (n = 102). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set); the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D’Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT. View Full-Text
Keywords: biochemical recurrence; CyberKnife; nomogram; prostate cancer; risk classification; stereotactic body radiation therapy; predictive analytics biochemical recurrence; CyberKnife; nomogram; prostate cancer; risk classification; stereotactic body radiation therapy; predictive analytics
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Mun, S.K.; Park, J.; Dritschilo, A.; Collins, S.P.; Suy, S.; Choi, I.Y.; Rho, M.J. The Prostate Clinical Outlook (PCO) Classifier Application for Predicting Biochemical Recurrences in Patients Treated by Stereotactic Body Radiation Therapy (SBRT). Appl. Sci. 2018, 8, 1620.

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