Publications by authors named "P H Despres"
Article Synopsis
- Deep learning methods show strong potential for predicting lung cancer risk from CT scans, but there's a need for more comprehensive comparisons and validations of these models in real-world settings.
- The study reviews 21 state-of-the-art deep learning models, analyzing their performance using CT scans from a subset of the National Lung Screening Trial, with a focus on malignant versus benign classification.
- Results reveal that 3D deep learning models generally outperformed 2D models, with the best 3D model achieving an AUROC of 0.86 compared to 0.79 for the best 2D model, emphasizing the need to choose appropriate pretrained datasets and model types for effective lung cancer risk prediction.
Background: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals' personal or protected attributes.
View Article and Find Full Text PDFObjective: Lung cancer remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Hence, there is a need to develop effective predictive models for early detection. This study aims to investigate the impact of imaging parameters and delta radiomic features from temporal scans on lung cancer risk prediction.
View Article and Find Full Text PDFAlmost half of the world's population is exposed to the risk of transmission of the four dengue virus serotypes (DENV 1-4), by mosquitoes of the genus Aedes. A dengue vaccine is effective if it induces prolonged protective immunity against all circulating viral strains, irrespective of the age and infection history of the vaccinated subject. An effective vaccine strategy against dengue is based on the injection of live attenuated viruses in a tetravalent formulation.
View Article and Find Full Text PDFObjectives: Radiomics can predict patient outcomes by automatically extracting a large number of features from medical images. This study is aimed to investigate the sensitivity of radiomics features extracted from 2 different pipelines, namely, Pyradiomics and RaCat, as well as the impact of gray-level discretization on the discovery of immune checkpoint inhibitors (ICIs) biomarkers.
Methods: A retrospective cohort of 164 non-small cell lung cancer patients administered with ICIs was used in this study.