Analyzing Longitudinal wb-MRI Data and Clinical Course in a Cohort of Former Smoldering Multiple Myeloma Patients: Connections between MRI Findings and Clinical Progression Patterns

Advanced Automated Model for Robust Bone Marrow Segmentation in Whole-body MRI

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.

Illuminating the Shadows: Innovation in Advanced Imaging Techniques for Myeloma Precursor Conditions

Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), the asymptomatic precursors to multiple myeloma, affect up to 5% of the population over the age of 40. Bone involvement, a myeloma-defining event, represents a major source of morbidity for patients. Key goals for the management of myeloma precursor conditions include (1) identifying patients at the highest risk for progression to MM with bone involvement and (2) differentiating precursor states from active myeloma requiring treatment. Computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT with [18F]fluorodeoxyglucose (FDG) have improved sensitivity for the detection of myeloma bone disease compared to traditional skeletal surveys, and such advanced imaging also provides this field with better tools for detecting early signs of progression. Herein, we review the data supporting the use of advanced imaging for both diagnostics and prognostication in myeloma precursor conditions.

Smoldering multiple myeloma: Integrating biology and risk into management

Smoldering multiple myeloma (SMM) was first described over 40 years ago yet much is still unknown including which patients will ultimately progress to symptomatic multiple myeloma (MM). The genetics of the premalignant clone and the immune microenvironment in which it exists is now well understood to both play a role in disease progression. However, the clinical risk models available to help identify patients at most risk of progression still rely primarily on data reflecting volume of disease rather than underlying biology. While it is of upmost importance to accurately diagnose patients with SMM to avoid over or under treatment, efforts are ongoing to tease out if early intervention is indeed warranted for a subgroup of patients with SMM. This article will review the history and biology of SMM, discuss the utility of existing risk models, and examine the efforts to date which have challenged standard management.

Multiple myeloma segmentation net (MMNet): an encoder-decoder-based deep multiscale feature fusion model for multiple myeloma segmentation in magnetic resonance imaging

Background

Patients with multiple myeloma (MM), a malignant disease involving bone marrow plasma cells, shows significant susceptibility to bone degradation, impairing normal hematopoietic function. The accurate and effective segmentation of MM lesion areas is crucial for the early detection and diagnosis of myeloma. However, the presence of complex shape variations, boundary ambiguities, and multiscale lesion areas, ranging from punctate lesions to extensive bone damage, presents a formidable challenge in achieving precise segmentation. This study thus aimed to develop a more accurate and robust segmentation method for MM lesions by extracting rich multiscale features.

Methods

In this paper, we propose a novel, multiscale feature fusion encoding-decoding model architecture specifically designed for MM segmentation. In the encoding stage, our proposed multiscale feature extraction module, dilated dense connected net (DCNet), is employed to systematically extract multiscale features, thereby augmenting the model's sensing field. In the decoding stage, we propose the CBAM-atrous spatial pyramid pooling (CASPP) module to enhance the extraction of multiscale features, enabling the model to dynamically prioritize both channel and spatial information. Subsequently, these features are concatenated with the final output feature map to optimize segmentation outcomes. At the feature fusion bottleneck layer, we incorporate the dynamic feature fusion (DyCat) module into the skip connection to dynamically adjust feature extraction parameters and fusion processes.

Results

We assessed the efficacy of our approach using a proprietary dataset of MM, yielding notable advancements. Our dataset comprised 753 magnetic resonance imaging (MRI) two-dimensional (2D) slice images of the spinal regions from 45 patients with MM, along with their corresponding ground truth labels. These images were primarily obtained from three sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and short tau inversion recovery (STIR). Using image augmentation techniques, we expanded the dataset to 3,000 images, which were employed for both model training and prediction. Among these, 2,400 images were allocated for training purposes, while 600 images were reserved for validation and testing. Our method showed increase in the intersection over union (IoU) and Dice coefficients by 7.9 and 6.7 percentage points, respectively, as compared to the baseline model. Furthermore, we performed comparisons with alternative image segmentation methodologies, which confirmed the sophistication and efficacy of our proposed model.

Conclusions

Our proposed multiple myeloma segmentation net (MMNet), can effectively extract multiscale features from images and enhance the correlation between channel and spatial information. Furthermore, a systematic evaluation of the proposed network architecture was conducted on a self-constructed, limited dataset. This endeavor holds promise for offering valuable insights into the development of algorithms for future clinical applications.

(Smoldering) multiple myeloma: mismatch between tumor load estimated from bone marrow biopsy at iliac crest and tumor load shown by MRI

In multiple myeloma and its precursor stages, precise quantification of tumor load is of high importance for diagnosis, risk assessment, and therapy response evaluation. Both whole-body MRI, which allows to investigate the complete bone marrow of a patient, and bone marrow biopsy, which is commonly used to assess the histologic and genetic status, are relevant methods for tumor load assessment in multiple myeloma. We report on a series of striking mismatches between the plasma cell infiltration estimating the tumor load from unguided biopsies of the bone marrow at the posterior iliac crest and the tumor load assessment from whole-body MRI.

Identification of focal lesion characteristics in MRI which indicate presence of corresponding osteolytic lesion in CT in patients with multiple myeloma

PURPOSE

The presence of bone marrow focal lesions and osteolytic lesions in patients with multiple myeloma (MM) is of high prognostic significance for their individual outcome. It is not known yet why some focal lesions seen in MRI, reflecting localized bone marrow infiltration of myeloma cells, remain non-lytic, whereas others are associated with destruction of mineralized bone. In this study, we analyzed MRI characteristics of manually segmented focal lesions in MM patients to identify possible features that might discriminate lytic and non-lytic lesions.

METHOD

The initial cohort included a total of 140 patients with different stages of MM who had undergone both whole-body MRI and whole-body low-dose CT within 30 days, and of which 29 satisfied the inclusion criteria for this study. Focal lesions in MRI and corresponding osteolytic areas in CT were segmented manually. Analysis of the lesions included volume, location and first order texture features analysis.

RESULTS

There were significantly more lytic lesions in the axial skeleton than in the appendicular skeleton (p = 0.037). Out of 926 focal lesions in the axial skeleton seen on MRI, 544 (59.3 %) were osteolytic. Analysis of volume and first order texture features showed differences in texture and volume between focal lesions in MRI with and without local bone destruction in CT, but these findings were not statistically significant.

CONCLUSIONS

Neither morphological imaging characteristics like size and location nor first order texture features could predict whether focal lesions seen in MRI would exhibit corresponding bone destruction in CT. Studies performing biopsies of such lesions are ongoing.

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study

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.

Smoldering multiple myeloma: Reviewing the rationale for intervention

Smoldering multiple myeloma has been recognized for over 40 years and represents a pre-symptomatic phase of the 2nd most common hematologic malignancy. 1/3 of patients will remain asymptomatic at 10 years. There is an identifiable subset of patients that will develop CRAB within 2 years of recognition and these patients are considered for therapeutic intervention before the development of potentially irreversible complications. Obstacles to widespread implementation of therapeutic guidelines are limited by the variable definitions associated with this high-risk group as well as the poor concordance between classification schemes. Analysis of clinical trial outcomes as well as uniform eligibility helps determine whether a given patient should be considered for therapeutic intervention outside of a clinical trial.

Tumors of the Spine: When Can Biopsy Be Avoided?

Regarding osseous tumors of the spine, characteristic morphology is encountered in hemangioma of the vertebral body, osteoid osteoma (OO), osteochondroma, Paget's disease, and bone islands. In these cases, radiologic imaging can make a specific diagnosis and thereby avoid biopsy, especially when the radiologist has chosen the correct imaging modality to establish the diagnosis, such as thin-slice computed tomography in suspected OO. A benign lesion is suggested by a high amount of fat within the lesion, the lack of uptake of the contrast agent, and a homogeneous aspect without solid parts in a cystic tumor. Suspicion of malignancy should be raised in spinal lesions with a heterogeneous disordered matrix, distinct signal decrease in T1-weighted magnetic resonance imaging, blurred border, perilesional edema, cortex erosion, and a large soft tissue component. Biopsy is mandatory in presumed malignancy, such as any Lodwick grade II or III osteolytic lesion in the vertebral column. The radiologist plays a crucial role in determining the clinical pathway by choosing the imaging approach wisely, by narrowing the differential diagnosis list, and, when characteristic morphology is encountered, by avoiding unnecessary biopsies.

Whole-body magnetic resonance imaging plus serological follow-up for early identification of progression in smouldering myeloma patients to prevent development of end-organ damage

The definition of multiple myeloma (MM) was updated in 2014, with the intent to enable earlier treatment and thereby avoid appearance of end-organ damage at progression from smouldering multiple myeloma (SMM) to MM. The purpose of this study was to investigate to which extent the development of end-organ damage at progression to MM was reduced under the updated guidelines. In this prospective observational cohort study (ClinicalTrials.gov Identifier: NCT01374412), between 2014 and 2020, 96 SMM patients prospectively underwent whole-body magnetic resonance imaging (wb-MRI) and serological follow-up at baseline and every 6 months thereafter. A total of 22 patients progressed into MM during follow-up, of which seven (32%) showed SLiM-criteria only but no end-organ damage. Four (57%) of the seven patients who progressed by SLiM-criteria only progressed with >1 focal lesion (FL) or a growing FL, and three (43%) due to serum free light-chain-ratio ≥100. Fifteen (68%) out of 22 patients who progressed still suffered from end-organ damage at progression. The updated disease definition reduced the proportion of SMM patients suffering from end-organ damage at progression to MM by one third. wb-MRI is an important tool for detection of SMM patients who progress to MM without end-organ damage.

[Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data]

CLINICAL/METHODICAL ISSUE

Multiple myeloma can affect the complete skeleton, which makes whole-body imaging necessary. With the current assessment of these complex datasets by radiologists, only a small part of the accessible information is assessed and reported.

STANDARD RADIOLOGICAL METHODS

Depending on the question and availability, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) is performed and the results are then visually examined by radiologists.

METHODOLOGICAL INNOVATIONS

A combination of automatic skeletal segmentation using artificial intelligence and subsequent radiomics analysis of each individual bone have the potential to provide automatic, comprehensive, and objective skeletal analyses.

PERFORMANCE

A few automatic skeletal segmentation algorithms for CT already show promising results. In addition, first studies indicate correlations between radiomics features of bone and bone marrow with established disease markers and therapy response.

ACHIEVEMENTS

Artificial intelligence (AI) and radiomics algorithms for automatic skeletal analysis from whole-body imaging are currently in an early phase of development.