Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma. (October 2022) - Milara, Eva; Gómez-Grande, Adolfo; Tomás-Soler, Sebastián; Seiffert, Alexander P.; Alonso, Rafael; Gómez, Enrique J.; Martínez-López, Joaquín; Sánchez-González, Patricia
Highlights: A segmentation methodology of the bone marrow in [ 18 F]FDG PET/CT images is proposed. Radiomics features of PET images is able to detect measurable residual disease. Machine learning models for the classification obtain high performance metrics. Abstract: Background and objectives: The last few years have been crucial in defining the most appropriate way to quantitatively assess [ 18 F]FDG PET images in Multiple Myeloma (MM) patients to detect persistent tumor burden. The visual evaluation of images complements the assessment of Measurable Residual Disease (MRD) in bone marrow samples by multiparameter flow cytometry (MFC) or next-generation sequencing (NGS). The aim of this study was to quantify MRD by analyzing quantitative and texture [ 18 F]FDG PET features. Methods: Whole body [ 18 F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A segmentation methodology of the skeleton from CT images and an additional manual segmentation tool were proposed, implemented in a software solution including a graphical user interface. Both the compact bone and the spinal canal were removed from the segmentation to obtain only the bone marrow mask. SUV metrics, GLCM, GLRLM, and NGTDM parameters were extracted from the PET images and evaluated by Mann-Whitney U-tests and Spearman ρ rank correlation as valuable features differentiating PET+/PET- and MFC+/MFC- groups. Seven machine learning algorithms were applied for evaluating the classification performance of the extracted features. Results: Quantitative analysis for PET+/PET- differentiating demonstrated to be significant for most of the variables assessed with Mann-Whitney U-test such as Variance, Energy, and Entropy (p-value = 0.001). Moreover, the quantitative analysis with a balanced database evaluated by Mann-Whitney U-test revealed in even better results with 19 features with p-values < 0.001. On the other hand, radiomics analysis for MFC+/MFC- differentiating demonstrated the necessity of combining MFC evaluation with [ 18 F]FDG PET assessment in the MRD diagnosis. Machine learning algorithms using the image features for the PET+/PET- classification demonstrated high performance metrics but decreasing for the MFC+/MFC- classification. Conclusions: A proof-of-concept for the extraction and evaluation of bone marrow radiomics features of [ 18 F]FDG PET images was proposed and implemented. The validation showed the possible use of these features for the image-based assessment of MRD.