Fiche publication
Date publication
juillet 2025
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Auteurs
Membres identifiés du Cancéropôle Est :
Dr ARNOULD Laurent
,
Pr COCHET Alexandre
Tous les auteurs :
Tareke TW, Payan N, Cochet A, Ali Y, Arnould L, Presles B, Vrigneaud JM, Meriaudeau F, Lalande A
Lien Pubmed
Résumé
This study aims to investigate whether quantitative radiomic features extracted from Positron Emission Tomography/Computed Tomography (PET/CT) could differentiate triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). We propose a pipeline that combines deep learning for cancer lesion segmentation with machine learning techniques to classify TNBC. Our approach leveraged the radiomic features extracted from F-fluorodeoxyglucose PET/CT. This retrospective study included the PET/CT images of 217 patients with breast cancer (57 TNBC and 160 non-TNBC) admitted to Georges-François Leclerc Hospital. The tumor regions of interest were automatically segmented on PET images using a deep learning model and mapped to CT scans. Radiomic features were extracted from 3D tumor volumes and machine learning classifiers were built using stratified 5-fold cross-validation. Recursive feature elimination was employed to rank and select the most relevant radiomic features, thereby enhancing classification performance. The model was evaluated using the F1-score, area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. The proposed method achieved promising performance, with an F1-score of 0.90 ± 0.02, an accuracy of 0.86 ± 0.07, a sensitivity of 0.91 ± 0.06, and an AUC of 0.88 ± 0.04, using the top-ranked features. The metrics were evaluated as the average over a five-fold cross-validation. Radiomic features extracted from PET and CT scans provide valuable prognostic insights for the identification of TNBC. This study demonstrated that machine learning algorithms based on radiomic features and automated PET/CT segmentation can accurately distinguish TNBC from non-TNBC.Clinical relevance- This study demonstrates the potential of image-based radiomic analysis combined with machine learning to differentiate triple-negative breast cancer (TNBC) from non-TNBC. By using deep learning for automatic tumor segmentation and feature extraction, this approach offers a non-invasive, quantitative tool that can improve TNBC diagnosis and the efficiency of treatment strategies. These advancements may help clinicians provide more reliable insights, while reducing the likelihood of misclassification.
Mots clés
Humans, Triple Negative Breast Neoplasms, diagnostic imaging, Female, Positron Emission Tomography Computed Tomography, Middle Aged, Machine Learning, Retrospective Studies, Image Processing, Computer-Assisted, methods, Fluorodeoxyglucose F18, Deep Learning, Adult, Automation, Radiomics
Référence
Annu Int Conf IEEE Eng Med Biol Soc. 2025 07;2025:1-6
