• Skip to primary navigation
  • Skip to main content
  • Skip to primary navigation
  • Skip to main content
Choose which site to search.
University of Arkansas for Medical Sciences Logo University of Arkansas for Medical Sciences
College of Medicine: Department of Biomedical Informatics
  • UAMS Health
  • Jobs
  • Giving
  • About Us
    • Employment
    • Access, Opportunity, and Advocacy
      • About DBMI-AOA
      • Current DBMI-AOA Committee Members
      • DBMI-AOA Resources
      • DBMI-AOA Committee Events
    • Links
    • News
    • Department Intranet
  • Faculty & Staff
    • Primary Faculty
    • Secondary Faculty
    • Adjunct Faculty
    • Staff
  • Education
    • Admission Information
    • Clinical Informatics Fellowship
      • Fellowship Overview
      • Training Sites
      • Faculty
      • Current Fellows
      • Welcome to Little Rock!
    • Graduate Programs
    • Current Course Offerings
    • DBMI FAQs
    • Research & Application Seminar
    • Recorded Sessions for CME Credit
    • Student Funding Opportunities
    • Graduate Students
  • Cores and Shared Resources
    • Arkansas Clinical Data Repository (AR-CDR)
    • Bioinformatics Collaborative Resource Center
    • INBRE
      • INBRE Bioinformatics Core Support Request Form
  • Research
    • Databases
    • Research Labs
      • Biomedical Ontologies Arkansas (BOAR)
    • Publications
  • Artificial Intelligence for Health
  1. University of Arkansas for Medical Sciences
  2. College of Medicine
  3. Department of Biomedical Informatics
  4. News
  5. Highly accurate model for prediction of lung nodule malignancy with CT scans

Highly accurate model for prediction of lung nodule malignancy with CT scans

Scientific Reports, Nature. volume 8, Article number: 9286 (2018)

Jason L. Causey, Junyu Zhang, Shiqian Ma, Bo Jiang, Jake A. Qualls, David G. Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang

Abstract

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN)…

Read more: https://www.nature.com/articles/s41598-018-27569-w

Posted by Chris Lesher on June 18, 2018

Filed Under: Publications Tagged With: Bo Jiang, David G. Politte, Fred Prior, Jake A. Qualls, Jason L. Causey, Junyu Zhang, Shiqian Ma, Shuzhong Zhang, Xiuzhen Huang

UAMS College of Medicine LogoUAMS College of MedicineUniversity of Arkansas for Medical Sciences
Mailing Address: 4301 West Markham Street, Little Rock, AR 72205
Phone: (501) 686-7000
  • Facebook
  • X
  • Instagram
  • YouTube
  • LinkedIn
  • Pinterest
  • Disclaimer
  • Terms of Use
  • Privacy Statement

© 2025 University of Arkansas for Medical Sciences