Oncology CT

Photon-counting CT enables enhanced fissure visualization and a lower degree of cardiac motion artifacts for lung perfusion imaging at a significantly reduced scan time in contrast to dual-energy CT, according to new research findings.

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Proprietary technologies with the FCT iStream computed tomography system reportedly facilitate enhanced workflow efficiency and significantly reduced radiation dosing.

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.

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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.

Image quality, sharpness, and contrast with AI-based denoising were significantly enhanced for neck CT in comparison to conventional CT image reconstruction at 100 percent and 50 percent mAs, according to newly published research.

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Reportedly trained on thousands of computed tomography scans, the e-Lung software utilizes machine learning to detect and assess the progression of features associated with interstitial lung diseases.

Combining clinical and CT features, adjunctive use of a classification and regression tree (CART) diagnostic model demonstrated AUCs for detecting clear cell renal cell carcinoma (ccRCC) that were 15 to 22 percent higher than unassisted radiologist assessments.

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A computed tomography (CT)-based radiomics model that includes 28 radiomic features showed significantly higher accuracy, sensitivity, and specificity than conventional CT in differentiating benign and malignant thyroid nodules, according to newly published research.

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Catch up on the top AI-related news and research in radiology over the past month.

In a study of women with oligometastatic breast cancer, the use of 18F-FDG PET/CT detected additional metastases in one-third of cases that were not evident on conventional CT.

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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.

The multimodality system nCommand Lite reportedly facilitates real-time remote imaging guidance on scanning parameters and procedure assessments to licensed technologists for a variety of imaging modalities including CT and MRI.

Catch up on the top AI-related news and research in radiology over the past month.

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

Offering ease of mobility and self-driving capabilities, the Ciartic Move C-arm device reportedly reduces the stress and potential for error associated with manual repositioning during intraoperative imaging with computed tomography and fluoroscopy.

Emerging research suggests that a computed tomography (CT)-based radiomics model can predict FOXM1 expression and is independently prognostic for clear cell renal cell carcinoma.

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In comparison to standard-dose lung CT, the combination of deep learning image reconstruction with ultra-low-dose CT offered similar detection and characterization of pulmonary nodules at a nearly 93 percent reduction of radiation dosing, according to new research.

Catch up on the most-well viewed radiology content in February 2024.