Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

Purpose: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality.

The TRIPLEX study: use of patient-derived tumor organoids as an innovative tool for precision medicine in triple-negative breast cancer.

Background: Triple negative breast cancers (TNBC) account for approximately 15% of all breast cancers and are associated with a shorter median survival mainly due to locally advanced tumor and high risk of metastasis. The current neoadjuvant treatment for TNBC consists of a regimen of immune checkpoint blocker and chemotherapy (chemo-ICB). Despite the frequent use of this combination for TNBC treatment, moderate results are observed and its clinical benefit in TNBC remains difficult to predict.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI.

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied.

Performance on a semantic verbal fluency task across time: dissociation between clustering, switching, and categorical exploitation processes.

The aims of this study were to investigate semantic retrieval processes and errors across time during a semantic fluency task and to specify their components (i.e., executive vs.

Verbal fluency output in children aged 7-16 as a function of the production criterion: qualitative analysis of clustering, switching processes, and semantic network exploitation.

Developmental changes in children's verbal fluency were explored in this study. One hundred and forty children aged from 7 to 16 completed four verbal fluency tasks, each with a different the production criterion (letter, sound, semantic, and free). The age differences were analyzed both in terms of number of words produced, and clustering, switching, and semantic network exploration.