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Chandramouli S, Leo P, Lee G, et al. Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers (Basel). 2020;12(9)doi: 10.3390/cancers12092708.
Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J. I., Veltri, R., & Madabhushi, A. (2020). Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers, 12(9), . https://doi.org/10.3390/cancers12092708
Chandramouli, Sacheth, et al. "Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance." Cancers vol. 12,9 (2020). doi: https://doi.org/10.3390/cancers12092708
Chandramouli S, Leo P, Lee G, Elliott R, Davis C, Zhu G, Fu P, Epstein JI, Veltri R, Madabhushi A. Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers (Basel). 2020 Sep 21;12(9). doi: 10.3390/cancers12092708. PMID: 32967377; PMCID: PMC7563653.
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Cancers (Basel). 2020 Sep 21;12(9). doi: 10.3390/cancers12092708.

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.

Cancers

Sacheth Chandramouli, Patrick Leo, George Lee, Robin Elliott, Christine Davis, Guangjing Zhu, Pingfu Fu, Jonathan I Epstein, Robert Veltri, Anant Madabhushi

Affiliations

  1. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
  2. Department of Anatomic Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA.
  3. Department of Surgical Pathology, The Johns Hopkins Hospital, 1800 Orleans St, Baltimore, MD 21287, USA.
  4. Department of Population and Quantitative Health Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA.
  5. Department of Urology and Oncology, The Johns Hopkins University, Baltimore, MD 21287, USA.
  6. Research Health Scientist, Louis Stokes Cleveland Veterans Administration Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA.

PMID: 32967377 PMCID: PMC7563653 DOI: 10.3390/cancers12092708

Abstract

In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk patients while directing high-risk patients to therapy. A total of 191 (125 progressors, 66 non-progressors) AS patients from a single site were identified using The Johns Hopkins University's (JHU) AS-eligibility criteria. Progression was determined by pathologists at JHU. 30 progressors and 30 non-progressors were randomly selected to create the training cohort D

Keywords: active surveillance; machine learning; pathology; prostate cancer

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