A Simultaneous Multiparametric ¹⁸F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer.

ABSTRACT:

Purpose

To investigate whether a machine learning (ML)-based radiomics model applied to ¹⁸F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC.

Methods

Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous ¹⁸F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identifica

SUBMITTER: Romeo V 

PROVIDER: S-EPMC9406327 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature