Intensity of bone involvement: a quantitative 18F-FDG PET/CT evaluation for monitoring outcome of multiple myeloma

Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma

Multiple myeloma (MM) is a malignant blood disease, but there have been significant improvements in the prognosis due to advancements in quantitative assessment and targeted therapy in recent years. The quantitative assessment of MM bone marrow infiltration and prognosis prediction is influenced by imaging and artificial intelligence (AI) quantitative parameters. At present, the primary imaging methods include computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These methods are now crucial for diagnosing MM and evaluating myeloma cell infiltration, extramedullary disease, treatment effectiveness, and prognosis. Furthermore, the utilization of AI, specifically incorporating machine learning and radiomics, shows great potential in the field of diagnosing MM and distinguishing between MM and lytic metastases. This review discusses the advancements in imaging methods, including CT, MRI, and PET/CT, as well as AI for quantitatively assessing MM. We have summarized the key concepts, advantages, limitations, and diagnostic performance of each technology. Finally, we discussed the challenges related to clinical implementation and presented our views on advancing this field, with the aim of providing guidance for future research.

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

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.

Clinical Value of FDG-PET/CT in Multiple Myeloma: An Update

FDG-PET/CT is a standardized imaging technique that has reached a great importance in the management of patients affected by Multiple Myeloma. It is proved, in fact, that it allows a deep evaluation of therapy efficacy and provides several prognostic indexes both at staging and after therapy. For this reason, it is now recognised as a gold standard for therapy assessment. Beside this, in reacent years FDG-PET/CT contribution to the understanding of Multiple Myeloma has progressively grown. Papers have been published analyzing the prognostic value of active disease volume measurement and standardization issues, the meaning of FDG positive paramedullary and extrameduallary disease, the prognostic impact of FDG positive minimal residual disease, the relation between focal lesions and clonal eterogenity of this disease and the comparison with whole body DWI-MR in terms of detection and therapy assessment. These newer aspects not of clinical impact yet, of FDG-PET/CT in Multiple Myeloma will be presented and discussed in this review.

Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

BACKGROUND AND OBJECTIVES

The last few years have been crucial in defining the most appropriate way to quantitatively assess [¹⁸F]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 [¹⁸F]FDG PET features.

METHODS

Whole body [¹⁸F]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 [¹⁸F]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 [¹⁸F]FDG PET images was proposed and implemented. The validation showed the possible use of these features for the image-based assessment of MRD.

Metabolic Volume Measurements in Multiple Myeloma

Multiple myeloma (MM) accounts for 10-15% of all hematologic malignancies, as well as 20% of deaths related to hematologic malignant tumors, predominantly affecting bone and bone marrow. Positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (FDG-PET/CT) is an important method to assess the tumor burden of these patients. It is often challenging to classify the extent of disease involvement in the PET scans for many of these patients because both focal and diffuse bone lesions may coexist, with varying degrees of FDG uptake. Different metrics involving volumetric parameters and texture features have been proposed to objectively assess these images. Here, we review some metabolic parameters that can be extracted from FDG-PET/CT images of MM patients, including technical aspects and predicting MM outcome impact. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) are volumetric parameters known to be independent predictors of MM outcome. However, they have not been adopted in clinical practice due to the lack of measuring standards. CT-based segmentation allows automated, and therefore reproducible, calculation of bone metabolic metrics in patients with MM, such as maximum, mean and standard deviation of the standardized uptake values (SUV) for the entire skeleton. Intensity of bone involvement (IBI) is a new parameter that also takes advantage of this approach with promising results. Other indirect parameters obtained from FDG-PET/CT images, such as visceral adipose tissue glucose uptake and subcutaneous adipose tissue radiodensity, may also be useful to evaluate the prognosis of MM patients. Furthermore, the use and quantification of new radiotracers can address different metabolic aspects of MM and may have important prognostic implications.