We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Artificial Intelligence (AI) Program Accurately Predicts Lung Cancer Risk from CT Images

By MedImaging International staff writers
Posted on 16 May 2021
Print article
Illustration
Illustration
An artificial intelligence (AI) program accurately predicts the risk that lung nodules detected on screening CT will become cancerous, according to a study published in the journal Radiology.

In the new study, researchers at the Radboud University Medical Center (Nijmegen, The Netherlands) developed an algorithm for lung nodule assessment using deep learning, an AI application capable of finding certain patterns in imaging data. The researchers trained the algorithm on CT images of more than 16,000 nodules, including 1,249 malignancies, from the National Lung Screening Trial. They validated the algorithm on three large sets of imaging data of nodules from the Danish Lung Cancer Screening Trial.

The deep learning algorithm delivered excellent results, outperforming the established Pan-Canadian Early Detection of Lung Cancer model for lung nodule malignancy risk estimation. It performed comparably to 11 clinicians, including four thoracic radiologists, five radiology residents and two pulmonologists. The researchers plan to continue improving the algorithm by incorporating clinical parameters like age, sex and smoking history. They are also working on a deep learning algorithm that takes multiple CT examinations as input. The current algorithm is highly suitable for analyzing nodules at the initial, or baseline, screening, but for nodules detected at subsequent screenings, growth and appearance in comparison to the previous CT are important. The researchers have developed other algorithms to reliably extract imaging features from the chest CT related to chronic obstructive pulmonary diseases and cardiovascular diseases. They will be investigating how to effectively integrate these imaging features into the current algorithm.

“The algorithm may aid radiologists in accurately estimating the malignancy risk of pulmonary nodules,” said the study’s first author, Kiran Vaidhya Venkadesh, a Ph.D. candidate with the Diagnostic Image Analysis Group at Radboud University Medical Center in Nijmegen, the Netherlands. “This may help in optimizing follow-up recommendations for lung cancer screening participants.”

“As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation,” said senior author Colin Jacobs, Ph.D., assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen. “This may lead to fewer unnecessary diagnostic interventions, lower radiologists’ workload and reduce costs of lung cancer screening.”

Related Links:
Radboud University Medical Center

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Laptop Ultrasound Scanner
PL-3018
Ultrasound Doppler System
Doppler BT-200
Color Doppler Ultrasound System
DRE Crystal 4PX

Print article
Radcal

Channels

MRI

view channel
Image: The emerging role of MRI alongside PSA testing is redefining prostate cancer diagnostics (Photo courtesy of 123RF)

Combining MRI with PSA Testing Improves Clinical Outcomes for Prostate Cancer Patients

Prostate cancer is a leading health concern globally, consistently being one of the most common types of cancer among men and a major cause of cancer-related deaths. In the United States, it is the most... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.