Share Email Print
cover

Proceedings Paper

The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions
Author(s): Faisal M. Khan; Casimir A. Kulikowski
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A major focus area for precision medicine is in managing the treatment of newly diagnosed prostate cancer patients. For patients with a positive biopsy, clinicians aim to develop an individualized treatment plan based on a mechanistic understanding of the disease factors unique to each patient. Recently, there has been a movement towards a multi-modal view of the cancer through the fusion of quantitative information from multiple sources, imaging and otherwise. Simultaneously, there have been significant advances in machine learning methods for medical prognostics which integrate a multitude of predictive factors to develop an individualized risk assessment and prognosis for patients. An emerging area of research is in semi-supervised approaches which transduce the appropriate survival time for censored patients. In this work, we apply a novel semi-supervised approach for support vector regression to predict the prognosis for newly diagnosed prostate cancer patients. We integrate clinical characteristics of a patient’s disease with imaging derived metrics for biomarker expression as well as glandular and nuclear morphology. In particular, our goal was to explore the performance of nuclear and glandular architecture within the transduction algorithm and assess their predictive power when compared with the Gleason score manually assigned by a pathologist. Our analysis in a multi-institutional cohort of 1027 patients indicates that not only do glandular and morphometric characteristics improve the predictive power of the semi-supervised transduction algorithm; they perform better when the pathological Gleason is absent. This work represents one of the first assessments of quantitative prostate biopsy architecture versus the Gleason grade in the context of a data fusion paradigm which leverages a semi-supervised approach for risk prognosis.

Paper Details

Date Published: 23 March 2016
PDF: 8 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979119 (23 March 2016); doi: 10.1117/12.2216435
Show Author Affiliations
Faisal M. Khan, Rutgers, The State Univ. of New Jersey (United States)
Casimir A. Kulikowski, Rutgers, The State Univ. of New Jersey (United States)


Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

© SPIE. Terms of Use
Back to Top