Cancer-control outcomes with [Lu]Lu-PSMA Radioligand Therapy in elderly, frail or comorbid mCRPC patients.

[Lu]Lutetium prostate-specific membrane antigen radioligand therapy ([Lu]Lu-PSMA) is EMA-approved for certain indications in metastatic castration resistant prostate cancer (mCRPC). However, cancer-control outcomes in specific and trial-underrepresented subgroups are scant. We relied on the FRAMCAP database to elaborate progression-free (PFS) and overall (OS) survival in elderly (≥75 yrs), frail (ECOG status ≥1) mCRPC patients or those with cardiovascular disease (CVD) treated with [Lu]Lu-PSMA.

Significantly increased bone volume in a critical-sized defect model in the rat animal model by transplantation of a stand-alone vascularized periosteal flap.

Purpose: The repair of bony non-unions remains challenging and often requires graft material due to limited availability of autologous bone. The aim of this study was to investigate the potency of a stand-alone pedicled periosteal flap (PF) versus a ligated periosteal flap (PFx), an empty defect and a crossover group in terms of newly formed bone in a 5 mm critical-sized defect in the rat femur diaphysis.

Methods: The following 4 treatment groups were formed out of a total of 36 male Sprague Dawley rats: Pedicled periosteal flap, ligated periosteal flap, crossover (each n = 10) and empty defect group (n = 6).

The association between the interval from biopsy to radical prostatectomy and biochemical recurrence in patients with intermediate- and high-risk prostate cancer.

Objective: To investigate the association between the interval from biopsy to radical prostatectomy (RP) and biochemical recurrence (BCR) in prostate cancer patients.

Methods: Within a tertiary-care database (01/2014 to 06/2023), D'Amico intermediate- and high-risk prostate cancer patients were stratified according to interval from biopsy to RP (≤3 vs. >3-≤6 months).

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging.

Background: Over the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. However, the development of AI models is often hindered by manual annotation requirements and strict data-sharing regulations between institutions.

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists.