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Myeloma-associated bone disease (MBD) develops in about 80–90% of patients and severely affects their quality of life, as it accounts for the majority of mortality and morbidity. Imaging in multiple myeloma (MM) and MBD is of utmost importance in order to detect bone and bone marrow lesions as well as extraosseous soft-tissue masses and complications before the initiation of treatment. It is required for determination of the stage of disease and aids in the assessment of treatment response. Whole-body low-dose computed tomography (WBLDCT) is the key modality to establish the initial diagnosis of MM and is now recommended as reference standard procedure for the detection of lytic destruction in MBD. In contrast, whole-body magnetic resonance imaging (WBMRI) has higher sensitivity for the detection of focal and diffuse plasma cell infiltration patterns of the bone marrow and identifies them prior to osteolytic destruction. It is recommended for the evaluation of spinal and vertebral lesions, while functional, diffusion-weighted MRI (DWI-MRI) is a promising tool for the assessment of treatment response. This review addresses the current improvements and limitations of WBCT and WBMRI for diagnosis and staging in MM, underlining the fact that both modalities offer complementary information. It further summarizes the corresponding radiological findings and novel technological aspects of both modalities.

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 methodsWhole-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.ResultsBM 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.ConclusionsThe 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.

PurposeFluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplant-eligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach.MethodsImaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS).ResultsOne hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUVmaxBone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach.ConclusionWe proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches.Trial registrationIMAJEM: NCT01309334 and EMN02/HO95: NCT01134484

To identify the whole-body MRI (WB-MRI) image type(s) with the highest value for assessment of multiple myeloma, in order to optimise acquisition protocols and read times. Thirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced Dixon images [fat-only (FO) and water-only (WO)], post contrast Dixon [fat-only plus contrast (FOC) and water-only plus contrast (WOC)] and diffusion weighted images (DWI) of the pelvis from all 30 patients were randomised and read by three experienced readers. For each image type, each reader identified and labelled all visible myeloma lesions. Each identified lesion was compared with a composite reference standard achieved by review of a complete imaging dataset by a further experienced consultant radiologist to determine truly positive lesions. Lesion count, true positives, sensitivity, and positive predictive value were determined. Time to read each scan set was recorded. Confidence for a diagnosis of myeloma was scored using a Likert scale. Conspicuity of focal lesions was assessed in terms of percent contrast and contrast to noise ratio (CNR). Lesion count, true positives, sensitivity and confidence scores were significantly higher when compared to other image types for DWI (P<0.0001 to 0.003), followed by WOC (significant for sensitivity (P<0.0001 to 0.004), true positives (P = 0.003 to 0.049) and positive predictive value (P< 0.0001 to 0.006)). There was no statistically significant difference in these metrics between FO and FOC. Percent contrast was highest for WOC (P = 0.001 to 0.005) and contrast to noise ratio (CNR) was highest for DWI (P = 0.03 to 0.05). Reading times were fastest for DWI across all observers (P< 0.0001 to 0.014). Observers detected more myeloma lesions on DWI images and WOC images when compared to other image types. We suggest that these image types should be read preferentially by radiologists to improve diagnostic accuracy and reporting efficiency.

Aim The aim of the study is to assess the feasibility of whole-body low-dose computed tomography (WBLDCT) in the diagnosis and staging of multiple myeloma and compare to skeletal survey (SS), using bone marrow biopsy and whole-body magnetic resonance imaging (WBMRI; where available) as gold standard. Materials and methods Patients referred over an 18-month period for investigation of suspected multiple myeloma or restaging of myeloma were randomized to undergo one of two WBLDCT protocols using high kVp, low mAs technique (140 kVp, 14 mAs; or 140 kVp, 25 mAs). Recent WBMRI scans were reviewed in 23 cases. Each imaging modality was assessed by two radiologists in consensus and scored from 0–3 (0 = normal, 1 = 1–4 lesions, 2 = 5–20 lesions, 3 ≥ 20 lesions/diffuse disease) in ten anatomical areas. Overall stage of disease, image quality score, and the degree of confidence of diagnosis were recorded. Diagnostic accuracy of skeletal survey and WBLDCT were determined using a gold standard of bone marrow biopsy and distribution of disease was compared to WBMRI. Results Thirty-nine patients were evaluated. WBLDCT identified more osteolytic lesions than skeletal survey with a greater degree of diagnostic confidence and led to restaging in 18 instances (16 upstaged, two downstaged). In those with recent WBMRI, distribution of disease on WBLDCT showed superior correlation with WBMRI when compared with SS. Overall reader impression of stage on WBLDCT showed significant correlation with WBMRI ( κ  = 0.454, p  < 0.05). WBLDCT provided complementary information to WBMRI in nine patients with normal marrow signal following treatment response, but which were shown to have diffuse residual cortical abnormalities on CT. Conclusion WBLDCT at effective doses lower than previously reported, is superior to SS at detecting osteolytic lesions and at determining overall stage of multiple myeloma, and provides complementary information to WBMRI.

Recent advances in the treatment of multiple myeloma have increased the need for accurate diagnosis of the disease. The detection of bone and bone marrow lesions is crucial in the investigation of multiple myeloma and often dictates the decision to start treatment. Furthermore, detection of minimal residual disease is important for prognosis determination and treatment planning, and it has underscored an unmet need for sensitive imaging methods that accurately assess patient response to multiple myeloma treatment. Low-dose whole-body CT has increased sensitivity compared with conventional skeletal survey in the detection of bone disease, which can reveal information leading to changes in therapy and disease management that could prevent or delay the onset of clinically significant morbidity and mortality as a result of skeletal-related events. Given the multiple options available for the detection of bone and bone marrow lesions, ranging from conventional skeletal survey to whole-body CT, PET/CT, and MRI, the International Myeloma Working Group decided to establish guidelines on optimal use of imaging methods at different disease stages. These recommendations on imaging within and outside of clinical trials will help standardise imaging for monoclonal plasma cell disorders worldwide to allow the comparison of results and the unification of treatment approaches for multiple myeloma.

B cell maturation antigen (BCMA) is a target for various immunotherapies and a biomarker for tumor load in multiple myeloma (MM). We report a case of irreversible BCMA loss in a patient with MM who was enrolled in the KarMMa trial ( NCT03361748 ) and progressed after anti-BCMA CAR T cell therapy. We identified selection of a clone with homozygous deletion of TNFRSF17 ( BCMA ) as the underlying mechanism of immune escape. Furthermore, we found heterozygous TNFRSF17 loss or monosomy 16 in 37 out of 168 patients with MM, including 28 out of 33 patients with hyperhaploid MM who had not been previously treated with BCMA-targeting therapies, suggesting that heterozygous TNFRSF17 deletion at baseline could theoretically be a risk factor for BCMA loss after immunotherapy. Biallelic loss of BCMA caused a patient with multiple myeloma to relapse after anti-BCMA CAR T cell treatment. Baseline heterozygous BCMA deletions might be a risk factor for this form of resistance.

To compare remission status at completion of chemotherapy for multiple myeloma (MM) with changes in total diffusion volume (tDV) calculated from whole-body diffusion-weighted imaging (WB-DWI) and fat fraction (FF) of lumbar bone marrow (BM) by modified Dixon Quant (mDixon Quant) soon after induction of chemotherapy, and to assess the predictive value of MRI. Fifty patients (mean age, 66.9 ± 10.5 years) with symptomatic myeloma were examined before and after two cycles of chemotherapy. From WB-DWI data, tDV was obtained with the threshold for positive BM involvement. Mean FF was calculated from lumbar BM using the mDixon Quant sequence. At the completion of chemotherapy, patients were categorized into a CR/very good PR (VGPR) group (n = 15; mean age, 67.6 ± 10.3 years) and a PR, SD or PD group (n = 35; mean age, 69.1 ± 8.6 years). ROC curves were plotted to assess performance in predicting achievement of CR/VGPR. At second examination, serum M protein, β2-microglobulin, and tDV were significantly decreased and hemoglobin, mean ADC, and FF were significantly increased in the CR/VGPR group and serum M protein was significantly increased in the PR/SD/PD group. The general linear model demonstrated that percentage changes in FF and M protein contributed significantly to achieving CR/VGPR (P = 0.02, P = 0.04, respectively). AUCs of ROC curves were 0.964 for FF and 0.847 for M protein. Early change in FF of lumbar BM and serum M protein soon after induction of chemotherapy contributed significantly to prediction of CR/VGPR.

Purpose CD38 is a glycoprotein highly specific to multiple myeloma (MM). Therapeutics using antibodies targeting CD38 have shown promising efficacy. However, the efficient stratification of patients who may benefit from daratumumab (Dara) therapy and timely monitoring of therapeutic responses remain significant clinical challenges. To address these issues, we developed a novel nanobody-based PET tracer, [ 68 Ga]Ga-TOHP-CD3813, which exhibits rapid clearance from the blood and rapid accumulation in targeted tumor lesions, facilitating the detection of CD38 expression in murine models of MM and lymphoma. Furthermore, we conducted the world’s first-in-human trials using CD38-targeted nanobodies to validate and assess the clinical imaging effectiveness of [ 68 Ga]Ga-TOHP-CD3813 in guiding cancer immunotherapy. Materials and methods We prepared a new PET imaging probe based on a CD38-targeted nanobody CD3813, [ 68 Ga]Ga-TOHP-CD3813, via the site-specific radiolabeling for noninvasive PET imaging of CD38 expression. [ 68 Ga]Ga-TOHP-CD3813 was assessed for its affinity and specificity to CD38 and its ability to image CD38 expression in MM and lymphoma xenograft models. Biodistribution and the relationship between tumor uptake and CD38 expression were evaluated. Subsequently, we conducted a translational PET imaging of 2 MM patients using [ 68 Ga]Ga-TOHP-CD3813, while compared with [ 18 F]FDG PET/CT head-to-head. Dosimetry was also calculated based on the animal data. Results TOHP-CD3813 retained a high affinity for CD38 with a KD of 0.0826 nmol/L. [ 68 Ga]Ga-TOHP-CD3813 was successfully synthesized at room temperature within 10 min, exhibiting optimal radiochemical properties. Preclinical assessments revealed rapid blood clearance, high CD38 affinity, and significant uptake in CD38-positive xenograft mouse models (6.50 ± 2.69%ID/g). [ 68 Ga]Ga-TOHP-CD3813 showed pronounced accumulation in the kidneys and bladder, with moderate liver uptake, indicating its potential as a viable clinical PET radiotracer for diagnosing MM. Additionally, in first-in-human trials, [ 68 Ga]Ga-TOHP-CD3813 PET/CT provides a substantial improvement over [ 18 F]FDG PET/CT for the visualization of MM. Conclusions [ 68 Ga]Ga-TOHP-CD3813, with its high affinity, specificity, and robust imaging capabilities, rapidly and specifically accumulates in tumors with high CD38 expression, offering a significant advantage over [ 18 F]FDG PET/CT for visualizing MM and enabling same-day PET imaging. Initial human trial results are promising, suggesting its potential as a companion diagnostic tool for optimizing CD38-targeted treatments in tumors. Ongoing larger trials aim to further confirm these findings.

BackgroundWhole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma.MethodsAxial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and “You Look Only Once” (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC).ResultsForty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%.ConclusionWe developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.