Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Abstract

Purpose [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. Materials and methods Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1–6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. Results BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. Conclusions The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [18F]FDG PET/CT in patients with newly diagnosed multiple myeloma

“…2 ). Deep learning-based tools for automated TMTV delineation might be a faster tool to more accurately delineate [ 18 F]FDG-positive tumor burden [ 41 ]. Sachpekidis et al showed that the TMTV obtained using a deep learning–based delineation method in previously untreated MM patients was significantly correlated with BM plasma cell infiltration and was associated with PFS and OS [ 20 ].…”

What is the role of artificial intelligence in general surgery?

“…The DL-based approach has demonstrated significant, moderate, positive correlations between bone marrow plasma cell infiltration and plasma β2-microglobulin levels, as well as with the automated quantitative PET/CT parameters, MTV and TLG [13].…”

Significance of PET/CT Imaging in Myeloma Assessment: Exploring Novel Applications beyond Osteolytic Lesion Detection and Treatment Response

“…The consistency between different observers when interpreting PET/CT scans could be impacted by the diverse patterns of bone marrow infiltration typical of the disease [65]. To tackle this issue, a group of scientists aimed to validate a new three-dimensional deep learning tool [65]. The proposed automated method for evaluating bone marrow…”

“…The consistency between different observers when interpreting PET/CT scans could be impacted by the diverse patterns of bone marrow infiltration typical of the disease [65]. To tackle this issue, a group of scientists aimed to validate a new three-dimensional deep learning tool [65]. The proposed automated method for evaluating bone marrow metabolism begins with the segmentation of the skeleton, which is based on CT data (Figure 15) [65].…”

“…To tackle this issue, a group of scientists aimed to validate a new three-dimensional deep learning tool [65]. The proposed automated method for evaluating bone marrow metabolism begins with the segmentation of the skeleton, which is based on CT data (Figure 15) [65]. This segmentation is then implemented on the SUV PET images, followed by the use of specific SUV thresholds [65].…”