Cancer World Magazine
  • About the Magazine
    • Editorial Team
    • Leadership and Management
    • Events
    • Magazine
    • Archive
    • Contacts
  • Articles
    • Policy
    • Practice Points
    • Delivery of Care
    • Biology basic
    • Medicine
    • Featured
  • Contents
    • News
    • Editorials
    • Interviews to the Expert
    • In the Hot Seat
    • Profiles
    • Obituaries
    • Voices
    • Partnership
    • Supported contents
  • Media Corner
    • Journalist Cancer Guide
    • Cancer Journalism Award
    • Cancer Journalist Grant
NEWSLETTER  |  PRINT VERSION
Facebook
Twitter
LinkedIn
Cancer World Magazine
Cancer World Magazine
  • About the Magazine
    • Editorial Team
    • Leadership and Management
    • Events
    • Magazine
    • Archive
    • Contacts
  • Articles
    • Policy
    • Practice Points
    • Delivery of Care
    • Biology basic
    • Medicine
    • Featured
  • Contents
    • News
    • Editorials
    • Interviews to the Expert
    • In the Hot Seat
    • Profiles
    • Obituaries
    • Voices
    • Partnership
    • Supported contents
  • Media Corner
    • Journalist Cancer Guide
    • Cancer Journalism Award
    • Cancer Journalist Grant
Cancer World Magazine > News > Artificial intelligence might help improve the classification of colorectal polyps
  • News

Artificial intelligence might help improve the classification of colorectal polyps

  • 4 May 2020
  • Alessia De Chiara
Visualization of the classifications of the deep neural network model. The first column shows the original image, and the second column shows pathologist annotations of polyps. The third column depicts the model’s detected heat map, where higher confidence predictions are shown in darker color. In the fourth column, the model’s final output is shown, which highlights precancerous lesions that can potentially be used to aid pathologists in clinical practice. With permission of Saeed Hassanpour, PhD.
Artificial intelligence might help improve the classification of colorectal polyps
Total
0
Shares
0
0
0
0
0
Deep neural networks are as good as practicing pathologists in classifying colorectal polyps, according to an experiment by a computer science and clinical research team led by Saeed Hassanpour, from the Dartmouth Cancer Center in Lebanon (New Hampshire). The team trained one such model on data from a single-center, and then checked its abilities in distinguishing the four most common colorectal polyps in imaging coming from 24 institutions in 13 states in the US, observing that its performance was comparable to that of practicing pathologists.
Visualization of the classifications of the deep neural network model. The first column shows the original image, and the second column shows pathologist annotations of polyps. The third column depicts the model’s detected heat map, where higher confidence predictions are shown in darker color. In the fourth column, the model’s final output is shown, which highlights precancerous lesions that can potentially be used to aid pathologists in clinical practice.
With permission of Saeed Hassanpour, PhD.
«To our knowledge, this study is the first to evaluate a deep neural network for colorectal polyp classification on a large multi-institutional data set with comparison with local diagnoses made at the point of care» Saeed Hassanpour and colleagues write in a paper just published in JAMA Network Open. They suggests that if the performance of the model will be confirmed in clinical trials, it could improve efficiency, reproducibility, and accuracy of the colonoscopy, the most common test used for colorectal cancer screening programs. «Early detection of cancer at an early, curable stage and removal of preinvasive adenomas or serrated lesions during this procedure are associated with a reduced mortality rate» the authors explain. Hassanpour and colleagues used slides from Dartmouth-Hitchcock Medical Center from patients with tubular adenoma, tubulovillous or villous adenoma, hyperplastic polyp, and sessile serrated adenoma to train the deep neural network for their classification. On an internal data set, the mean accuracy of the model observed was 93.5%, compared with that of local pathologists equal to 91.4%. When tested on the external data set from 24 institutions (238 slides for 179 patients), the authors found that «the model performed at a similar level of accuracy, sensitivity, and specificity as local pathologists», (i.e. accuracy was 87.0% and 86.6% respectively). Furthermore, deep neural network and local pathologists had comparable confusion matrixes, which indicate similar misclassifications. For the authors, the model, implemented in laboratory information systems, could guide pathologists, and «although expert practitioner confirmation of diagnoses will still be required, the model could help triage slides indicating diagnoses that are more likely to be preinvasive for subsequent review by pathologists». Authors want to evaluate their model in a clinical trial and to collect more data to improve its performance.    
Total
0
Shares
Share 0
Tweet 0
Share 0
Share 0
Share 0
Related Topics
  • Artificial intelligence
  • colonoscopy
  • colorectal
  • deep neural network
  • polyps
Avatar
Alessia De Chiara

Previous Article
  • Articles
  • Featured

Cancer and COVID-19 took the stage at AACR Annual Meeting

  • 30 April 2020
  • Cristina Ferrario
View Post
Next Article
  • Articles
  • Delivery of Care

MDT meetings: why patient care suffers if I’m not there

  • 10 May 2020
  • Janet Fricker
View Post
You May Also Like
View Post
  • News

Study brings mass biparametric MRI screening for prostate cancer a step closer

  • Janet Fricker
  • 23 February 2021
View Post
  • News

Oncology providers urged to offer healthy life style advice to breast cancer survivors

  • Janet Fricker
  • 19 February 2021
View Post
  • News

Studies provide clarity on breast cancer genes for genetic panels

  • Janet Fricker
  • 9 February 2021
View Post
  • News

EC publishes route map for tackling cancer

  • Janet Fricker
  • 5 February 2021
View Post
  • News

Therapy targeting androgen receptors opens new chapter for hormone driven breast cancers

  • Janet Fricker
  • 27 January 2021
View Post
  • News

Coffee may protect against prostate cancer

  • Janet Fricker
  • 20 January 2021
View Post
  • News

Financial burden on older adults with advanced cancer worsens quality of life

  • Janet Fricker
  • 15 January 2021
View Post
  • News

Integrated imaging opens the way for virtual biopsies

  • Janet Fricker
  • 15 January 2021

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

search
or search in Cancer World archive
Latest news
  • Study brings mass biparametric MRI screening for prostate cancer a step closer
    • 23 February 2021
  • Oncology providers urged to offer healthy life style advice to breast cancer survivors
    • 19 February 2021
  • Studies provide clarity on breast cancer genes for genetic panels
    • 9 February 2021
  • EC publishes route map for tackling cancer
    • 5 February 2021
  • Therapy targeting androgen receptors opens new chapter for hormone driven breast cancers
    • 27 January 2021
Latest printed issue
Article
  • Natural killers: a new tactical unit joins the cancer immunotherapy brigade
    • 25 February 2021
  • Immunotherapy toxicities demand a joined up approach from oncologists and organ specialists
    • 24 February 2021
  • Beating cancer is complex – our messaging must be clear
    • 11 February 2021
Newsletter

Don't miss our newsletter

Would you like to receive our bimonthly e-newsletter with the latest news from Cancer World magazine?

Subscribe now
Social

Would you follow us ?

Contents
  • Moving towards efficiency in cancer care: Examples
    • 8 February 2021
  • World Cancer Day: we commemorate Professor Agim Sallaku
    • 3 February 2021
  • Gordon McVie 1945–2021: a lifetime dedicated to defeating cancer
    • 22 January 2021
MENU
  • About the Magazine
    • Editorial Team
    • Leadership and Management
    • Events
    • Magazine
    • Archive
    • Contacts
  • Articles
    • Policy
    • Practice Points
    • Delivery of Care
    • Biology basic
    • Medicine
    • Featured
  • Contents
    • News
    • Editorials
    • Interviews to the Expert
    • In the Hot Seat
    • Profiles
    • Obituaries
    • Voices
    • Partnership
    • Supported contents
  • Media Corner
    • Journalist Cancer Guide
    • Cancer Journalism Award
    • Cancer Journalist Grant
Cancer World Magazine
  • About the Magazine
  • Articles
  • Media Corner
  • Privacy Policy
  • Cookie Policy

Cancer World is managed by SPCC Sharing Progress in Cancer Care | Piazza Indipendenza 2, 6500 Bellinzona - Switzerland | info@spcc.net

Archivio Cancerworld

Input your search keywords and press Enter.