Intérêts de la TEP/TDM au [18F] - fluorodésoxyglucose dans les métastases ganglionnaires cervicales de carcinome de site primitif inconnu

REIMS-BU Santé (514542104) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Rate of Distant Metastases on 18 F-FDG PET/CT at Initial Staging of Breast Cancer: Comparison of Women Younger and Older Than 40 Years

International audienc

Early metabolic response of breast cancer to neoadjuvant endocrine therapy: Comparison to morphological and pathological response

International audienceBackground: Neoadjuvant endocrine therapy (NET) has shown efficacy in terms of clinical response and surgical outcome in postmenopausal patients with estrogen receptor-positive / HER2-negative breast cancer (ER+/HER2- BC) but monitoring of tumor response is challenging. The aim of the present study was to investigate the value of an early metabolic response compared to morphological and pathological responses in this population. Methods: This was an ancillary study of CARMINA 02, a phase II clinical trial evaluating side-by-side the efficacy of 4 to 6 months of anastrozole or fulvestrant. Positron Emission Tomography/Computed Tomography using 2-deoxy-2-[18F]fluoro-D-glucose (FDG-PET/CT) scans were performed at baseline (M0), early after 1 month of treatment (M1) and pre-operatively in 11 patients (74.2 yo ± 3.6). Patients were classified as early "metabolic responders" (mR) when the decrease of SUVmax was higher than 40%, and "metabolic non-responders" (mNR) otherwise. Early metabolic response was compared to morphological response (palpation, US and MRI), variation of Ki-67 index, pathological response according to the Sataloff classification and also to Preoperative Endocrine Prognostic Index (PEPI) score. It was also correlated with overall survival (OS) and recurrence-free survival (RFS). Results: Tumor size measured on US and on MRI was smaller in mR than mNR, with the highest statistically significant difference at M1 (p = 0.01 and 7.1 × 10- 5, respectively). No statistically significant difference in the variation of tumor size between M0 and M1 assessed on US or MRI was observed between mR and mNR. mR had a better clinical response: no progressive disease in mR vs 2 in mNR and 2 partial response in mR vs 1 partial response in mNR. One patient with a pre-operative complete metabolic response had the best pathological response. Pathological response did not show any statistically significant difference between mR and mNR. mR had better OS and RFS (Kaplan-Meier p = 0.08 and 0.06, respectively). All cancer-related events occurred in mNR: 3 patients died, 2 of them from progressive disease. Conclusions: FDG-PET/CT imaging could become a "surrogate marker" to monitor tumor response, especially as NET is a valuable treatment option in postmenopausal women with ER+/HER2- BC

Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour segmentation

Abstract In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or both. However, navigating the multitude of available architectures for automatic segmentation can be overwhelming, making it challenging to determine the appropriate type of architecture and tune the most crucial parameters during dataset optimisation. To address this problem, we examined and refined seven distinct architectures for segmenting the liver, as well as liver tumours, with a restricted training collection of 60 3D contrast-enhanced magnetic resonance images (CE-MRI) from the ATLAS dataset. Included in these architectures are convolutional neural networks (CNNs), transformers, and hybrid CNN/transformer architectures. Bayesian search techniques were used for hyperparameter tuning to hasten convergence to the optimal parameter mixes while also minimising the number of trained models. It was unexpected that hybrid models, which typically exhibit superior performance on larger datasets, would exhibit comparable performance to CNNs. The optimisation of parameters contributed to better segmentations, resulting in an average increase of 1.7% and 5.0% in liver and tumour segmentation Dice coefficients, respectively. In conclusion, the findings of this study indicate that hybrid CNN/transformer architectures may serve as a practical substitute for CNNs even in small datasets. This underscores the significance of hyperparameter optimisation