Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer.

Monocytes in leukapheresis products affect the outcome of CD19-targeted CAR T-cell therapy in patients with lymphoma.

CD19-directed chimeric antigen receptor (CAR) T cells can induce durable remissions in relapsed/refractory large B-cell lymphomas (R/R LBCLs), but 60% of patients do not respond or relapse. Biological mechanisms explaining lack of response are emerging, but they are largely unsuccessful in predicting disease response at the patient level. Additionally, to maximize the cost-effectiveness of CAR T-cell therapy, biomarkers able to predict response and survival before CAR T-cell manufacturing would be desirable.

Prognostic impact of pretreatment cell-free DNA concentration in newly diagnosed peripheral T-cell lymphomas.

Peripheral T-cell lymphomas (PTCLs) have a poor prognosis and, to date, there are no reliable predictive biomarkers of response. In this work we explored the prognostic impact of cell-free DNA (cfDNA) concentration in 75 newly diagnosed patients enrolled in a prospective multicenter study. Pre-treatment cfDNA was strongly associated with clinical risk factors and was identified as a superior predictor for shorter progression-free survival in multivariable analysis, outweighing canonical risk parameters.

Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality.

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels.