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Purpose: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)–based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. Materials and methods: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. Results: Median follow-up [95% CI] of the patient cohort was 110 months [105–123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. Conclusions: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters have shown prognostic value in nasopharyngeal carcinomas (NPC), mostly in monocenter studies. The aim of this study was to assess the prognostic impact of standard and novel PET parameters in a multicenter cohort of patients. Methods: The established PET parameters metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximal standardized uptake value (SUVmax) as well as the novel parameter tumor asphericity (ASP) were evaluated in a retrospective multicenter cohort of 114 NPC patients with FDG-PET staging, treated with (chemo)radiation at 8 international institutions. Uni- and multivariable Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), event-free survival (EFS), distant metastases-free survival (FFDM), and locoregional control (LRC) was performed for clinical and PET parameters. Results: When analyzing metric PET parameters, ASP showed a significant association with EFS (p = 0.035) and a trend for OS (p = 0.058). MTV was significantly associated with EFS (p = 0.026), OS (p = 0.008) and LRC (p = 0.012) and TLG with LRC (p = 0.019). TLG and MTV showed a very high correlation (Spearman's rho = 0.95), therefore TLG was subesequently not further analysed. Optimal cutoff values for defining high and low risk groups were determined by maximization of the p-value in univariate Cox regression considering all possible cutoff values. Generation of stable cutoff values was feasible for MTV (p<0.001), ASP (p = 0.023) and combination of both (MTV+ASP = occurrence of one or both risk factors, p<0.001) for OS and for MTV regarding the endpoints OS (p<0.001) and LRC (p<0.001). In multivariable Cox (age >55 years + one binarized PET parameter), MTV >11.1ml (hazard ratio (HR): 3.57, p<0.001) and ASP > 14.4% (HR: 3.2, p = 0.031) remained prognostic for OS. MTV additionally remained prognostic for LRC (HR: 4.86 p<0.001) and EFS (HR: 2.51 p = 0.004). Bootstrapping analyses showed that a combination of high MTV and ASP improved prognostic value for OS compared to each single variable significantly (p = 0.005 and p = 0.04, respectively). When using the cohort from China (n = 57 patients) for establishment of prognostic parameters and all other patients for validation (n = 57 patients), MTV could be successfully validated as prognostic parameter regarding OS, EFS and LRC (all p-values <0.05 for both cohorts). Conclusions: In this analysis, PET parameters were associated with outcome of NPC patients. MTV showed a robust association with OS, EFS and LRC. Our data suggest that combination of MTV and ASP may potentially further improve the risk stratification of NPC patients. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To establish an advanced automated bone marrow (BM) segmentation model on whole-body (WB-)MRI in monoclonal plasma cell disorders (MPCD), and to demonstrate its robust performance on multicenter datasets with severe myeloma-related pathologies., Materials and Methods: The study cohort comprised multi-vendor, multi-protocol imaging data acquired with varying field strength across 8 different centers. In total, 210 WB-MRIs of 207 MPCD patients were included. An nnU-Net algorithm was established for segmenting the individual bone marrow spaces (BMS) of the spine, pelvis, humeri and femora (advanced segmentation model). For this task, 186 T1-weighted (T1w) WB-MRIs from center 1 were used in the training set. Test sets included 12 T1w WB-MRIs from center 2 (I) and 9 T1w WB-MRIs from centers 3-8 (II). Example cases were included to showcase segmentation performance on T1w WB-MRIs with extensive tumor load. The segmentation accuracy of the advanced segmentation model was compared to a prior established basic segmentation model by calculating Dice scores and using the Wilcoxon signed-rank test., Results: The mean Dice score on the individual BMS was 0.89±0.13 (test set I) and 0.88±0.11 (test set II), significantly higher than the Dice scores of a prior basic model (p<0.05). Dice scores for the BMS of the individual bones ranged from 0.77 to 0.96 (test set I), and 0.81 to 0.95 (test set II). BM altered by myeloma-relevant pathologies, artifacts or low imaging quality was precisely segmented., Conclusion: The advanced model performed reliable, automated segmentations, even on heterogeneously acquired multicenter WB-MRIs with severe pathologies., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marina Hajiyianni reports a relationship with Sanofi that includes: speaking and lecture fees. Sandra Sauer reports a relationship with Celgene, BMS, Janssen, Takeda, and Amgen that includes: speaking and lecture fees and travel reimbursement. Marc-Steffen Raab reports a relationship with BMS, Amgen, GSK, Janssen, Sanofi, Pfizer, AbbVie, Novartis, and Roche that includes: consulting or advisory. Marc-Steffen Raab reports a relationship with Sanofi that includes: funding grants. Marc-Steffen Raab reports a relationship with BMS, AbbVie, Janssen, Sanofi, and GSK that includes: travel reimbursement. Marc-Steffen Raab reports a relationship with BMS, Janssen, GSK, AbbVie, and Sanofi that includes: speaking and lecture fees. Marc-Steffen Raab reports a relationship with Novartis and Sanofi that includes: non-financial support. Elias K. Mai reports a relationship with Amgen, Bristol Myers Squibb Celgene, GlaxoSmithKline, Janssen-Cilag, Pfizer, Sanofi, Stemline, and Takeda that includes: consulting or advisory. Elias K. Mai reports a relationship with Amgen, Bristol Myers Squibb Celgene, GlaxoSmithKline, Janssen-Cilag, Pfizer, Sanofi, Stemline, and Takeda that includes: speaking and lecture fees. Elias K. Mai reports a relationship with Bristol Myers Squibb Celgene, GlaxoSmithKline, Janssen-Cilag, Sanofi, and Takeda that includes: funding grants. Elias K. Mai reports a relationship with Bristol Myers Squibb Celgene, GlaxoSmithKline, Janssen-Cilag, Sanofi, Stemline, and Takeda that includes: travel reimbursement. Hartmut Goldschmidt reports a relationship with Amgen, Array Biopharma Pfizer, BMS, Celgene, Chugai, Dietmar-Hopp-Foundation, Janssen, Johns Hopkins University, Mundipharma GmbH, Sanofi, GlycoMimetics Inc., GSK, Heidelberg Pharma, Hoffmann-La Roche, Karyopharm, Incyte, millennium Pharmaceuticals Inc., Molecular Partners, Merck Sharp and Dohme, MorphoSys AG, Takeda, and Novartis that includes: funding grants and non-financial support. Hartmut Goldschmidt reports a relationship with Adaptive Biotechnology, Amgen, BMS, Janssen, and Sanofi that includes: consulting or advisory. Hartmut Goldschmidt reports a relationship with Amgen, BMS, Chugai, GlaxoSmithKline, Janssen, Novartis, Sanofi, and Pfizer that includes: speaking and lecture fees. Heinz-Peter Schlemmer reports a relationship with Siemens, Curagita, Profound, and Bayer that includes: consulting or advisory, travel reimbursement, and speaking and lecture fees. Heinz-Peter Schlemmer reports a relationship with Curagita that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Chimeric antigen receptor (CAR) T-cell therapy has demonstrated significant benefits in the treatment of relapsed/refractory multiple myeloma (RRMM). However, these outcomes can be compromised by severe complications, including cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome (ICANS) and immune effector cell-associated hematotoxicity (ICAHT), predisposing for life-threatening infections., Methods: This retrospective observational study examined a total of 129 patients with RRMM who had received idecabtagene vicleucel (ide-cel) at two major myeloma centers in Germany and one center in the USA to assess the Endothelial Activation and Stress Index (EASIX) as a risk marker for an unfavorable clinical course and outcome after CAR T-cell therapy. EASIX is calculated by lactate dehydrogenase (U/L) × creatinine (mg/dL) / platelets (10 9 cells/L) and was determined before lymphodepletion (baseline) and at the day of CAR T-cell infusion (day 0). The analysis was extended to EASIX derivatives and the CAR-HEMATOTOX score., Results: An elevated baseline EASIX (>median) was identified as a risk marker for severe late ICAHT, manifesting with an impaired hematopoietic reconstitution and pronounced cytopenias during the late post-CAR-T period. Patients with high EASIX levels (>upper quartile) were particularly at risk, as evidenced by an increased rate of an aplastic phenotype of neutrophil recovery, severe late-onset infections and ICANS. Finally, we found associations between baseline EASIX and an inferior progression-free and overall survival. Moreover, the EASIX at day 0 also demonstrated potential to serve as a risk marker for post-CAR-T complications and adverse outcomes., Conclusions: In conclusion, EASIX aids in risk stratification at clinically relevant time points prior to CAR T-cell therapy with ide-cel. Increased EASIX levels might help clinicians to identify vulnerable patients to adapt peri-CAR-T management at an early stage., Competing Interests: Competing interests: JF has received honoraria from BMS and Stemline and travel and congress participation grants from Janssen-Cilag. XZ declares advisory services for and has received travel support from SkylineDx. JK has received honoraria from AstraZeneca. EKM declares a consulting or advisory role for Amgen, BMS/Celgene, GlaxoSmithKline, Janssen-Cilag, Pfizer, Sanofi, Stemline and Takeda. He has received honoraria from Amgen, BMS/Celgene, GlaxoSmithKline, Janssen-Cilag, Pfizer, Sanofi, Stemline and Takeda, research funding from BMS/Celgene, GlaxoSmithKline, Janssen-Cilag, Sanofi and Takeda and travel support from BMS/Celgene, GlaxoSmithKline, Janssen-Cilag, Sanofi, Stemline and Takeda. MJF declares consulting activity for Pfizer and Kerna Ventures. CSM has received honoraria and travel support from Janssen-Cilag. IB has received honoraria from Oncopeptide and travel support from Janssen. ON reports receiving consulting fees from Janssen, BMS, Takeda, GPCR therapeutics, Sanofi and Pfizer. AS has received travel grants from Hexal and Jazz Pharmaceuticals and research grants from Therakos/Mallinckrodt. She is a consultant for Janssen-Cilag and BMS and co-founder of TolerogenixX Ltd. AS is part-time employee of TolerogenixX Ltd. MS declares an advisory role or expert testimony for MSD, Novartis, BMS and Pierre Fabre. He is co-founder and shareholder of TolerogenixX GmbH, Heidelberg. He has received financial support for research on biosimilars and travel grants from Hexal, financial support of educational activities and conference participation and travels grants from Kite and BMS, collaborative research grants from Novartis and funding for collaborative research from Apogenix. MT has received research funding from Kite, Regeneron and Roche. He is an advisory board member for AstraZeneca, BMS, Incyte, Janssen and Novartis. HE declares a consulting or advisory role for BMS/Celgene, Janssen, Amgen, Takeda, Sanofi, GSK, Novartis and Roche. He has received research funding from BMS/Celgene, Janssen, Amgen, GSK, Sanofi and Novartis, honoraria from BMS/Celgene, Janssen, Amgen, Takeda, Sanofi, GSK, Novartis and travel support from BMS/Celgene, Janssen and Amgen. PD reports consultancy for AbbVie, AstraZeneca, Beigene, BMS, Gilead, Miltenyi, Novartis and Riemser. He is member of the speakers’ bureau for AbbVie, AstraZeneca, BeiGene, BMS, Gilead, Novartis, Riemser and Roche and has received research support from Riemser (all to institution). NCM reports receiving personal fees from BMS, Janssen, Amgen, Takeda, OncoPep, AbbVie, Karyopharm, Novartis, Legend, Raqia, Adaptive Biotechnology, and Pfizer outside the submitted work. He has intellectual property licensed to OncoPep and held stocks in C4 Therapeutics. ASp reports receiving consulting fees from Novartis and Roche. LR consulted for Janssen, Amgen, GSK, Pfizer, BMS, Sanofi, and received honoraria from Janssen, GSK, Pfizer, BMS, Sanofi and received research funding from Skyline Dx and BMS. SS has received travel grants or honoraria for presentations from Celgene, BMS, Janssen, Takeda and Amgen. MSR declares a consulting or advisory role for BMS, Amgen, GSK, Janssen, Sanofi, Pfizer, AbbVie and Takeda. He has received research funding from BMS, Janssen, Sanofi and Heidelberg Pharma, travel support from BMS, Amgen and Janssen and honoraria from BMS, Janssen, AbbVie and Sanofi., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)