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 evaluated by C-index and Net Reclassification Index (NRI). Results: Ninety-eight patients with NDMM who underwent 18F-FDG-PET/CT between 2014 and 2019 were included in this study. Combining radiomics features from PET/CT with clinical data showed higher prognostic performance than models with radiomics features or clinical data alone (C-index 0.790 vs. 0.675 vs. 0.736 in training cohort; 0.698 vs. 0.651 vs. 0.563 in validation cohort; AUC 0.761, sensitivity 56.7%, specificity 85.7%, p < 0.05 in training cohort and AUC 0.650, sensitivity 80.0%, specificity78.6%, p < 0.05 in validation cohort) When clinical data was combined with radiomics, an increase in the performance of the model was observed (NRI > 0). Conclusions: Radiomics features extracted from the PET and CT components of baseline 18F-FDG-PET/CT images may become an effective complement to provide prognostic information; therefore, radiomics features combined with clinical characteristic may provide clinical value for MM prognosis prediction.

Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art

“…The PRISMA flowchart of the articles included in our review is reported in Figu The literature search identified a total of 31 studies. According to the aforementio eligibility criteria, 10 studies were included [17][18][19][20][21][22][23][24][25][26]. Among these, eight studies w retrospective and two prospective.…”

“…All selected radiomic studies performed a statistical analysis in order to select the optimal/robust features. The total number of features initially extracted from each VOI, both for the CT and PET datasets, is summarized in Table 2 and ranged from 15 [25] to 1702 [19]; the most common features were SUVmax, shape, and first-order statistics, followed by textural features. After analysis, the selected features ranged from 3 [19] to 457 [22].…”

The added diagnostic value of 18F-FDG PET/CT radiomic analysis in multiple myeloma patients with negative visual analysis

“…It is of great significance to choose appropriate imaging methods to perform iteratively for evaluating therapeutic response and guiding clinicians for follow-up treatment [17,25]. One of our recent studies showed that optimized radiomics feature from 18 F-FDG PET/CT images could predict progression of MM [26].…”

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

“…In hematological malignancies including multiple myeloma and acute leukemia, 18 F-FDG PET/CT radiomics-based ML analyses have been applied to identify skeletal metastases, predict diffuse infiltration in the bone marrow, or predict prognosis [ 125 – 128 ] (Table 7 ).…”

“…The diagnostic accuracy of this model was significantly higher than that of visual analysis (0.886 vs. 0.686, p = 0.041). Ni et al [ 128 ] evaluated the ability of 18 F-FDG PET/CT radiomics-based ML analysis for predicting PFS after multiple myeloma treatment. Results showed that the ML model with the LASSO + cox regression algorithm trained using the combined clinical and PET/CT radiomics-based model had a higher predictive performance (C-index: 0.698) than the ML model with clinical data (C-index: 0.563) or PET/CT radiomics-based model (C-index: 0.651) alone.…”