Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.

For the prediction of axillary lymph node metastasis in patients with breast cancer, an MRI-based, 4D convolutional neural network model demonstrated an AUC of 87 percent and sensitivity of 89 percent, according to new research.

While no job is perfect, there is the time-honored wisdom about picking your battles and the realization that some positions are stuck in a status-quo quagmire.

Catch up on the top radiology content of the past week.

Discussing findings from a new study presented at the Society for Breast Imaging (SBI) conference, Shahrzad Tavana, M.D., detailed the significant impact of training sessions for MRI technologists in improving breast positioning, optimal field of view and accuracy of sequence submissions to PACS for breast MRI exams.

The inclusion of simulated adjudication for resolving discordant nodule classifications in a deep learning model for assessing lung adenocarcinoma on chest CT resulted in a 12 percent increase in sensitivity rate.

Leveraging artificial intelligence, the Rho software assessment of X-rays may allow earlier detection of bone loss.

Patients with Medicaid or means-tested insurance were over 27 percent more likely to miss mammography appointments, and only 65 percent of women with three of more adverse social determinants of health had a mammography exam in a two-year period covering 2020 and 2021, according to new research and a report from the CDC.

Emerging research suggests that an artificial intelligence (AI) score of 75 or greater for mammography abnormalities more than doubles the likelihood of invasive upgrade of ductal carcinoma in situ (DCIS) diagnosed with percutaneous biopsy.

Review the case and test your knowledge to make the correct diagnosis.

Can consistent timeliness, visual effort and engaging attitude give a radiologist with so-so skills a discernible advantage?

Strain imaging analysis is one of the automated parameters included in the newly FDA-cleared Us2.v2 software for adjunctive review of echocardiographic images.

In a recent review, researchers examined the pros and cons of specialized scoring systems for prostate MRI, including the PI-QUAL score, PRECISE recommendations, PI-RR scoring and PI-FAB assessments.

Catch up on the top radiology content of the past week.

Utilizing PET imaging to assess pathologic complete response to the combination of trastuzumab and pertuzumab for patients with HER2-positive breast cancer, researchers noted a 94.8 percent rate of invasive disease-free survival at three years.