Diagnostic screening programs help catch cancer, abnormalities or other diseases before they reach an advanced stage, saving lives and healthcare costs. Screening programs include, lung, breast, prostate, and cervical cancer, among many others.
New findings support the routine use of deep learning-based risk assessments, as this method can decrease subjectivity, reduce unnecessary imaging and improve diagnostic accuracy.
The COlorectal Cancer detection with AI, or COCA, model is a cost-effective, scalable solution that turns routine CT scans into opportunistic exams that can be used to proactively identify CRC.
Two respected radiology organizations have issued a stark warning on the new recommendations, stating that they risk confusing patients and “may contribute to thousands of additional breast cancer deaths each year.”
Accurate information relative to personal risk is crucial for improving uptake of low-dose CT (LDCT) lung cancer screening, but new data indicate that many websites' content on the topic is out of date.
How recent developments in hormonal contraceptives affect breast density is an important consideration, as an increase in density category increases cancer risk.
Prior research has shown that not only is contrast-enhanced spectral mammography comparable to CE-MRI in accuracy of loco-regional staging, but some studies have even found it to perform better.
Using risk model-based strategies to determine who should undergo low-dose CT lung cancer screening is more cost effective than current U.S. Preventive Services Task Force guidelines.
The deep learning model was trained to predict risk of lung cancer in the one to six years following completion of an LDCT scan, and it does not require clinical information relative to risk factors to do so.
