Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Issue 8 (August 2018)
- Record Type:
- Journal Article
- Title:
- Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Issue 8 (August 2018)
- Main Title:
- Advanced Morphologic Analysis for Diagnosing Allograft Rejection
- Authors:
- Peyster, Eliot G.
Madabhushi, Anant
Margulies, Kenneth B. - Abstract:
- Abstract : Abstract: Allograft rejection remains a significant concern after all solid organ transplants. Although qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy to diagnose cardiac allograft rejection illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted endomyocardial biopsy as the diagnostic gold standard. In this context, automated approaches to complex data analysis problems—often referred to as "machine learning"—represent promising strategies to improve overall diagnostic accuracy. By focusing on cardiac allograft rejection, where tissue sampling is relatively frequent, this review highlights the limitations of the current approach to diagnosing allograft rejection, introduces the basic methodology behind machine learning and automated image feature detection, and highlights the initial successes of these approaches within cardiovascular medicine. Abstract : The authors review "machine learning" and "automated image feature detection" to improveAbstract : Abstract: Allograft rejection remains a significant concern after all solid organ transplants. Although qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy to diagnose cardiac allograft rejection illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted endomyocardial biopsy as the diagnostic gold standard. In this context, automated approaches to complex data analysis problems—often referred to as "machine learning"—represent promising strategies to improve overall diagnostic accuracy. By focusing on cardiac allograft rejection, where tissue sampling is relatively frequent, this review highlights the limitations of the current approach to diagnosing allograft rejection, introduces the basic methodology behind machine learning and automated image feature detection, and highlights the initial successes of these approaches within cardiovascular medicine. Abstract : The authors review "machine learning" and "automated image feature detection" to improve the diagnostic accuracy of endomyocardial biopsies to diagnose cardiac allograft rejection. … (more)
- Is Part Of:
- Transplantation. Volume 102:Issue 8(2018)
- Journal:
- Transplantation
- Issue:
- Volume 102:Issue 8(2018)
- Issue Display:
- Volume 102, Issue 8 (2018)
- Year:
- 2018
- Volume:
- 102
- Issue:
- 8
- Issue Sort Value:
- 2018-0102-0008-0000
- Page Start:
- Page End:
- Publication Date:
- 2018-08
- Subjects:
- Transplantation of organs, tissues, etc -- Periodicals
Transplantation immunology -- Periodicals
617.95 - Journal URLs:
- http://journals.lww.com/pages/default.aspx ↗
- DOI:
- 10.1097/TP.0000000000002189 ↗
- Languages:
- English
- ISSNs:
- 0041-1337
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9024.990000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 10619.xml