The use of 90Y-FAPI-46 radioligand therapy resulted in controlled disease progression in nearly half of a 30-person cohort largely comprised of patients with advanced sarcomas, according to new research presented at the 2024 Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting.

In addition to new advances in PET/CT imaging to facilitate precision imaging, the company will also unveil a device at the SNMMI conference that allows in-house production of commercial PET tracers.

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

Facilitating additional consultation on chest and abdominal CT scans, the Second Opinions teleradiology platform now features FDA-cleared AI tools for cardiac, bone and liver assessments.

In a study of asymptomatic women at elevated risk for breast cancer, digital breast tomosynthesis (DBT) demonstrated equivalent sensitivity to full-field digital mammography (FFDM) for three out of four reviewing radiologists and detected no additional cancers beyond those detected with FFDM.

There was over a 31 percent decline in radiology-only practices and over a 25 percent decrease of practices employing 10-24 radiologists, according to new research examining consolidation trends in radiology from 2014 to 2023.

As ongoing advances continue to redefine and elevate the diagnostic capabilities of MRI, ensuring the safety of patients and operators through effective signage, training and regular safety audits is of paranount importance.

Offering comparable sensitivity to radiologists for detecting contralateral breast cancer on mammography images, an emerging adjunctive AI software may also facilitate earlier diagnosis, according to study findings presented at the at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting.

In a retrospective study involving nearly 119,000 women, researchers found that implementation of AI into mammography screening increased the positive predictive value by 11 percent, increased small cancer detection by 8.3 percent and reduced reading workload by approximately 33 percent.

Featuring a combination of automated measurement capabilities and workflow enhancements, the new AI-powered cardiovascular ultrasound platform also provides automated assessment of regional wall motion abnormalities.

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

An AI algorithm that incorporates scoring of coronary inflammation based on coronary CT angiography (CCTA) may enhance long-term cardiovascular risk stratification beyond conventional risk factor and imaging assessments, even in patients without obstructive CAD.

Offering access to over 110 AI applications, the enterprise imaging platform enables radiologists to test, deploy and monitor the use of AI technologies.

Additional carcinoma in the ipsilateral breast was detected on preoperative MRI exams in 24 out of 102 women prior to lumpectomy and mastectomy procedures, according to new study findings presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

The AI-powered Heuron ICH software reportedly has an 86 percent sensitivity rate for diagnosing intracranial hemorrhage on computed tomography (CT) scans.

An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

In comparison to pure solid nodules in patients with non-small cell lung cancer (NSCLC), nodules with a minor ground glass opacity component were associated with over a 38 percent higher rate of recurrence-free survival.

In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.

The second-generation version of the VUZE System reportedly offers expanded functionality and incorporation of varied sources of 3D imaging data, including cone-beam CT scans obtained in the OR.

One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.