Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy.

Background: Relapsed and refractory Diffuse large B cell lymphoma (DLBCL) can be successfully treated with axicabtagene ciloleucel (axi-cel), a CD19-directed autologous chimeric antigen receptor T cell (CAR-T) therapy. Diagnostic image-based features could help identify the patients who would clinically respond to this advanced immunotherapy.

Purpose: The aim of this study was to establish a radiomic image feature-based signature derived from positron emission tomography/computed tomography (PET/CT), including metabolic tumor burden, which can predict a durable response to CAR-T therapy in refractory/relapsed DLBCL.

Tumor burden quantified by Soluble B-Cell Maturation Antigen and Metabolic Tumor Volume determine myeloma CAR-T outcomes.

Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a breakthrough treatment for relapsed and refractory multiple myeloma (RRMM). However, these products are complex to deliver and alternative options are now available. Identifying biomarkers that can predict therapeutic outcomes is crucial for optimizing patient selection.

5hmC-profiles in Puerto Rican Hispanic/Latino men with aggressive prostate cancer.

Puerto Rican (PR) Hispanic/Latino (H/L) men are an understudied population that has the highest prostate cancer (PCa) specific mortality among other Hispanic populations. Little information is known about the higher mortality in PR H/L men. It is thought that epigenetic changes in key genes may play a critical role in aggressive tumors.

Extracellular microvesicle microRNAs, along with imaging metrics, improve detection of aggressive prostate cancer.

Prostate cancer is the most commonly diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Magnetic resonance imaging (MRI) provides an overall assessment of prostate disease.

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI).