Pre‐treatment metabolic tumour volume and total lesion glycolysis are superior to conventional positron‐emission tomography/computed tomography variables for outcome prediction in patients with newly diagnosed multiple myeloma in clinical practice

Terao, Toshiki; Machida, Youichi; Tsushima, Takafumi; Miura, Daisuke; Narita, Kentaro; Kitadate, Akihiro; Takeuchi, Masami; Matsue, Kosei

doi:10.1111/bjh.16633

Cited by 22 publications

(31 citation statements)

References 23 publications

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“…The presented automatic BM segmentation could be of use for a variety of subsequent analyses in the future, as for example automatic extraction of vertebral heights for fracture detection, or to define a search zone for a second algorithm to perform automatic detection or segmentations of all focal lesions within the BM. This could enable automatic, volumetric tumor load assessment, which would be of use for risk stratifications in (smoldering) MM 12–15 . The algorithmic concept can be transferred to other oncologic entities with disseminated skeletal involvement and to other imaging modalities.…”

Section: Discussionmentioning

confidence: 99%

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI

Wennmann¹,

Klein

Bauer

et al. 2022

Invest Radiol

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Objectives: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can transfer only a small fraction of the information of the imaging data set to the report. This limits the influence that imaging can have on clinical decision-making and in research toward precision oncology. The objective of this feasibility study was to implement a concept for automatic, comprehensive characterization of the BM from wb-MRI, by automatic BM segmentation and subsequent radiomics analysis of 30 different BM spaces (BMS). Materials and Methods: This retrospective multicentric pilot study used a total of 106 wb-MRI from 102 patients with (smoldering) MM from 8 centers. Fifty wb-MRI from center 1 were used for training of segmentation algorithms (nnU-Nets) and radiomics algorithms. Fifty-six wb-MRI from 8 centers, acquired with a variety of different MRI scanners and protocols, were used for independent testing. Manual segmentations of 2700 BMS from 90 wb-MRI were performed for training and testing of the segmentation algorithms. For each BMS, 296 radiomics features were calculated individually. Dice score was used to assess similarity between automatic segmentations and manual reference segmentations. Results: The "multilabel nnU-Net" segmentation algorithm, which performs segmentation of 30 BMS and labels them individually, reached mean dice scores of 0.88 ± 0.06/0.87 ± 0.06/0.83 ± 0.11 in independent test sets from center 1/center 2/center 3-8 (interrater variability between radiologists, 0.88 ± 0.01). The subset from the multicenter, multivendor test set (center 3-8) that was of high imaging quality was segmented with high precision (mean dice score, 0.87), comparable to the internal test data from center 1. The radiomic BM phenotype consisting of 8880 descriptive parameters per patient, which result from calculation of 296 radiomics features for each of the 30 BMS, was calculated for all patients.Exemplary cases demonstrated connections between typical BM patterns in MM and radiomic signatures of the respective BMS. In plausibility tests, predicted size and weight based on radiomics models of the radiomic BM phenotype significantly correlated with patients' actual size and weight (P = 0.002 and P = 0.003, respectively). Conclusions: This pilot study demonstrates the feasibility of automatic, objective, comprehensive BM characterization from wb-MRI in multicentric data sets. This concept allows the extraction of high-dimensional phenotypes to capture the complexity of disseminated BM disorders from imaging. Further studies need to assess the clinical potential of this method for automatic staging, therapy response assessment, or prediction of biopsy results.

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Section: Discussionmentioning

confidence: 99%

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI

Wennmann¹,

Klein

Bauer

et al. 2022

Invest Radiol

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“…Wholebody metabolic tumor volume (MTV) and whole-body tumor lesion glycolysis (TLG) (i.e., MTVxSUV mean ) are promising prognostic markers showing superiority in comparison with the baseline number of FLs or baseline SUV max in a cohort of patients treated with chemotherapy comprising thalidomide, dexamethasone, cisplatin, cyclophosphamide, and etoposide, followed by intensification by melphalan, tandem autologous stem cell transplant (ASCT), and maintenance by thalidomide and dexamethasone (total therapy 3 protocol) (54). Similar results were found in a study of 185 NDMM patients treated with a proteasome inhibitor-based regimen (55).…”

Section: Baseline Imaging Techniques As Prognostic Factorsmentioning

confidence: 53%

18F-FDG PET/CT and MRI in the Management of Multiple Myeloma: A Comparative Review

et al. 2022

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During the last two decades, the imaging landscape of multiple myeloma (MM) has evolved with whole-body imaging techniques such as fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) and MRI replacing X-ray skeletal survey. Both imaging modalities have high diagnostic performance at the initial diagnosis of MM and are key players in the identification of patients needing treatment. Diffusion-weighted MRI has a high sensitivity for bone involvement, while 18F-FDG PET/CT baseline parameters carry a strong prognostic value. The advent of more efficient therapeutics, such as immunomodulatory drugs and proteasome inhibitors, has called for the use of sensitive imaging techniques for monitoring response to treatment. Diffusion-weighted MRI could improve the specificity of MRI for tumor response evaluation, but questions remain regarding its role as a prognostic factor. Performed at key time points of treatment in newly diagnosed MM patients, 18F-FDG PET/CT showed a strong association with relapse risk and survival. The deployment of minimal residual disease detection at the cellular or the molecular level may raise questions on the role of these imaging techniques, which will be addressed. This review summarizes and outlines the specificities and respective roles of MRI and 18F-FDG PET/CT in the management of MM.

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“…The Chisquare test verified that the GEP and TLG are independent prognostic factors. Furthermore, they found that TLG > 205 g accurately divided ISS stage II patients into two subgroups with outcomes similar to those of ISS stage I and ISS stage III, respectively [22].…”

Section: Mtv and Tlgmentioning

confidence: 95%

“…Terao et al [22] utilized an absolute fixed threshold of SUV = 2.5 to calculate MTV and TLG from FDG-PET/CT images of NDMM patients. In a multivariate analysis, a high-burden MTV (>56.4 cm 3 ) had prognostic values for PFS and OS even when the model was adjusted for the R-ISS and high-risk FDG-PET findings.…”

Section: Mtv and Tlgmentioning

confidence: 99%

“…Quantitative metrics are less operator-dependent than visual methods and can facilitate inter and multi-center clinical discussions [18]. Attempts have been made to standardize quantitative interpretations of FDG-PET/CT in MM, especially using parameters that refer to active metabolic disease [12,[19][20][21][22]. However, the main limitation of FDG-PET/CT to measure the metabolic tumor volume in MM is to standardize the criteria for delimiting the areas affected by the disease.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic Volume Measurements in Multiple Myeloma

et al. 2021

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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.

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Pre‐treatment metabolic tumour volume and total lesion glycolysis are superior to conventional positron‐emission tomography/computed tomography variables for outcome prediction in patients with newly diagnosed multiple myeloma in clinical practice

Cited by 22 publications

References 23 publications

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI

18F-FDG PET/CT and MRI in the Management of Multiple Myeloma: A Comparative Review

Metabolic Volume Measurements in Multiple Myeloma

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