Machine Learning Model Based on Optimized Radiomics Feature from ¹⁸F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study.

ABSTRACT:

Objects

To evaluate the prognostic value of radiomics features extracted from ¹⁸F-FDG-PET/CT images and integrated with clinical characteristics and conventional PET/CT metrics in newly diagnosed multiple myeloma (NDMM) patients.

Methods

We retrospectively reviewed baseline clinical information and ¹⁸F-FDG-PET/CT imaging data of MM patients with ¹⁸F-FDG-PET/CT. Multivariate Cox regression models involving different combinations were constructed, and stepwise regression was performed: (1) radiomics features of PET/CT alone (Rad Model); (2) Using clinical data (including clinical/laboratory parameters and conventional PET/CT metrics) only (Cli Model); (3) Combination radiomics features and clinical data (Cli-Rad Model). Model performance was ev

SUBMITTER: Ni B 

PROVIDER: S-EPMC10059677 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature