Examining current trends in brain cancer diagnostics, these authors discuss diagnostic imaging advances, pathways with adaptive radiotherapy and the ongoing quest to provide optimal precision with dosimetry.

One of the features on the new Voluson Signature 20 and 18 ultrasound devices reportedly uses automated AI tools to facilitate a 40 percent reduction in the time it takes to perform second trimester exams.

While noting inconsistencies with the diagnostic yield of cardiac MRI in patients who survived sudden cardiac arrest, researchers cited unique advantages in characterizing ischemic cardiomyopathy (ICM) and facilitating alternate diagnoses.

The combination of the optical imaging agent Lumisight and the fluorescence imaging device Lumicell Direct Visualization System, collectively known as LumiSystem, reportedly offers 84 percent accuracy with real-time detection of residual breast cancer after lumpectomy procedures.

A recent meta-analysis found that multiparametric MRI and PET/CT had comparable detection rates for diagnosing prostate cancer recurrence.

The use of continuous ofatumumab in patients within three years of a relapsing multiple sclerosis diagnosis led to substantial reductions in associated lesions on brain MRI scans, according to research recently presented at the American Academy of Neurology (AAN) conference.

A new study shows that GPT-4 may offer comparable error detection rates to those of attendings and radiology residents in reviewing radiology reports, but researchers noted key caveats as well.

A positron emission tomography (PET) agent that targets LAT1 and LAT2 membrane transport proteins, TLX101-CDx or 18F-floretyrosine could be utilized to help characterize progressive or recurrent glioma.

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.