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.
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.”
New findings from a large CT lung cancer screening dataset reveal that a substantial number of patients have significant incidental findings visible on their scans.
Decreased screening rates among different subgroups highlight the ongoing need for outreach strategies that target vulnerable populations, experts contend.
Outside of the correlation between measurements obtained using both modalities, the researchers also identified Hounsfield unit thresholds that could reliably rule out osteoporosis.
"This awareness is important to avoid oversight of symptoms like dyspnea and vague chest discomfort, which can easily be interpreted as symptoms caused by the known disease COPD,” experts involved in the study said.
Of the 51 plans, just 31% were consistent with the USPSTF recommendations pertaining to the starting age and frequency of screening women who are at average risk of developing breast cancer.
Although current guidelines recommend radiologists evaluate CAC on all non-gated, non-contrast chest CT scans, the authors of the study note that these guidelines are not consistently followed.
The results of a survey completed by more than 13,000 respondents who were eligible for the cancer screening revealed that less than 2% of eligible participants underwent CTC exams.
Researchers reported that the artificial intelligence system was able to interpret more than 114,000 screening mammograms using a reading protocol with high sensitivity and specificity.