A5094089041- Lung Cancer Diagnosis and Treatment
- Cancer Immunotherapy and Biomarkers
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Lymphoma Diagnosis and Treatment
- Protein Degradation and Inhibitors
- Brain Metastases and Treatment
- Multiple Myeloma Research and Treatments
- Glioma Diagnosis and Treatment
- AI in cancer detection
- CNS Lymphoma Diagnosis and Treatment
- Lung Cancer Treatments and Mutations
- Gastric Cancer Management and Outcomes
Université Paris Sciences et Lettres
2024-2025
Inserm
2024-2025
Institut Curie
2024-2025
Cyceron
2016-2017
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
2016-2017
Université de Caen Normandie
2016-2017
Centre National de la Recherche Scientifique
2017
Centre Hospitalier Universitaire de Caen
2017
Normandie Université
2017
Abstract Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore benefits multimodal approaches predict immunotherapy outcome using multiple machine learning algorithms integration strategies. We analyze baseline data from a cohort 317 NSCLC treated first-line immunotherapy, including positron emission tomography images,...
The normalized distances from the hot spot of radiotracer uptake (SUV
Measuring total metabolic tumor volume (TMTV) on <sup>18</sup>F-FDG PET/CT images in clinical practice requires a fast, reliable, and easy-to-perform multilesional segmentation workflow. We conducted field test to derive volumes using 5 representative baseline scans from patients with diffuse large B-cell lymphoma. The were transferred 10 different sites or readers who used commercially available software platforms TMTV after recently proposed benchmark Observed TMTVs compared reference...
Lymphoma research has advanced thanks to introduction of [(18)F]fludarabine, a positron-emitting tool. This novel radiotracer been shown display great specificity for lymphoid tissues. However, in benign process such as inflammation, the uptake this tracer not questioned. Indeed, inflammatory zones, elevated glucose metabolism rate may result false-positives with [(18)F]FDG-PET Imaging. In present investigation, it argued that cells, involved might be less avid [(18)F]fludarabine. To...
Explaining the decisions made by a radiomic model is of significant interest, as it can provide valuable insights into information learned complex models and foster trust in well-performing ones, thereby facilitating their clinical adoption. Promising approaches that aggregate from multiple regions within an image currently lack suitable explanation tools could identify most significantly influence decisions. Here we present model- modality-agnostic tool (RadShap,...
Purpose Multiple myeloma (MM) is a haematological malignancy that affects plasma cells in the bone marrow. Recently, [18F]fludarabine has been introduced as an innovative PET radiotracer for imaging lymphoma. It demonstrated great potential accurate of lymphoproliferative disorders. With goal to question usefulness [18F]fludarabine-PET other diseases, vivo MM model was investigated. Methods RPMI8226-GFP-Luc expressing green fluorescent protein (GFP) well luciferase reporter (Luc) were...
Abstract The survival of patients with metastatic non-small cell lung cancer (NSCLC) has been increasing immunotherapy, yet efficient biomarkers are still needed to optimize patient care. In this study, we explored the benefits multimodal approaches predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We leveraged a novel cohort 317 NSCLC treated first-line collecting at baseline positron emission tomography images, digitized pathological...