Publications by authors named "N Lassau"
Introduction: FOLFIRINOX, a primary chemotherapy for metastatic pancreatic cancer, often causes severe toxicity, necessitating hospitalization and dose adjustments. This study aims to identify predictors of FOLFIRINOX toxicity, focusing on biological, clinical, and anthropometric factors.
Material & Methods: This retrospective study analyzes pancreatic adenocarcinoma patients on FOLFIRINOX, assessing pre-treatment biological, clinical, and anthropometric traits.
Introduction: The incidence of venous thromboembolism is estimated to be around 3% of cancer patients. However, a majority of incidental pulmonary embolism (iPE) can be overlooked by radiologists in asymptomatic patients, performing CT scans for disease surveillance, which may significantly impact the patient's health and management. Routine imaging in oncology is usually reviewed with delayed hours after the acquisition of images.
View Article and Find Full Text PDFBackground Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023.
View Article and Find Full Text PDFArticle Synopsis
- * This paper aims to create a machine learning algorithm using MRI characteristics to classify parotid gland tumors and compares its effectiveness against diagnoses made by junior and senior radiologists, incorporating data from 134 patients.
- * The study's random forest model achieved notable accuracy (0.720) and improved diagnostic abilities for junior radiologists by 6%, suggesting the algorithm could enhance the identification of tumor types and reduce the need for invasive procedures, though further research is needed for routine implementation.
Article Synopsis
- The 2023 SFR data challenge aimed to encourage researchers to create AI models for detecting pancreatic masses and determining if they are benign or malignant using abdominal CT scans.
- A total of 1,037 CT examinations were gathered from 18 French centers, organized into training and evaluation sets, with teams composed of radiologists, data scientists, and engineers participating in the analysis.
- The challenge involved 10 teams and showed promising results, with AI demonstrating potential in identifying pancreatic lesions from real data, although distinguishing between benign and malignant masses remains challenging.