The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions

Faisal M. Khan, Casimir A. Kulikowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationDigital Pathology
EditorsAnant Madabhushi, Metin N. Gurcan
ISBN (Electronic)9781510600263
StatePublished - 2016
Event4th Medical Imaging 2016: Digital Pathology - San Diego, United States
Duration: Mar 2 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


Other4th Medical Imaging 2016: Digital Pathology
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


  • biopsy
  • data fusion
  • prostate cancer
  • semi-supervised


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