Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Role of Imaging in Multiple Myeloma: A Potential Opportunity for Quantitative Imaging and Radiomics?

Multiple myeloma (MM) is the second most prevalent hematologic malignancy, particularly affecting the elderly. The disease often begins with a premalignant phase known as monoclonal gammopathy of undetermined significance (MGUS), solitary plasmacytoma (SP) and smoldering multiple myeloma (SMM). Multiple imaging modalities are employed throughout the disease continuum to assess bone lesions, prevent complications, detect intra- and extramedullary disease, and evaluate the risk of neurological complications. The implementation of advanced imaging analysis techniques, including artificial intelligence (AI) and radiomics, holds great promise for enhancing our understanding of MM. The integration of advanced image analysis techniques which extract features from magnetic resonance imaging (MRI), computed tomography (CT), or positron emission tomography (PET) images has the potential to enhance the diagnostic accuracy for MM. This innovative approach may lead to the identification of imaging biomarkers that can predict disease prognosis and treatment outcomes. Further research and standardized evaluations are needed to define the role of radiomics in everyday clinical practice for patients with MM.

Advances in estimating plasma cells in bone marrow: A comprehensive method review

The quantitation of plasma cells in bone marrow (BM) is crucial for diagnosing and classifying plasma cell neoplasms. Various methods, including Romanowsky-stained BM aspirates (BMA), immunohistochemistry, flow cytometry, and radiological imaging, have been explored. However, challenges such as patchy infiltration and sample haemodilution can impact the reliability of BM plasma cell percentage estimates. Bone marrow plasma cell percentage varies across methods, with immunohistochemically stained biopsies consistently yielding higher values than Romanowsky-stained BMA or flow cytometry alone. CD138 or MUM1 immunohistochemistry and artificial intelligence image analysis on whole-slide images are emerging as promising tools for accurate plasma cell identification and quantification. Radiological imaging, particularly with advanced technologies like dual-energy computed tomography and radiomics, shows potential for multiple myeloma diagnosis, although standardisation remains a challenge. Molecular techniques, such as allele-specific oligonucleotide quantitative polymerase chain reaction and next-generation sequencing, offer insights into clonality and measurable residual disease. While no consensus exists on a gold standard method for BM plasma cell quantitation, CD138-stained biopsies are favoured for accurate estimation and play a pivotal role in diagnosing and assessing multiple myeloma treatment responses. Combining multiple methods, such as BMA, BM biopsy, and flow cytometry, enhances accuracy of diagnosis and classification of plasma cell neoplasms. The quest for a gold standard requires ongoing research and collaboration to refine existing methods. Furthermore, the rise of digital pathology is anticipated to reshape laboratory medicine and the role of pathologists in the digital era.

What this study adds

This article adds a comprehensive review and comparison of different methods for plasma cell estimation in the bone marrow, highlighting their strengths and limitations. The goal is to contribute valuable insights that can guide the selection of optimal techniques for accurate plasma cell estimation.

The significance of autologous hematopoietic stem cell transplantation on immunoglobulin reconstitution and prognosis in elderly patients with multiple myeloma

OBJECTIVES

Autologous hematopoietic stem cell transplantation (ASCT) is a crucial method used in patients with multiple myeloma (MM). This study aims to evaluate the role of ASCT in immunoglobulin (Ig) reconstitution and long-term outcomes in patients aged ≥ 60 years.

METHODS

From March 2008 to May 2019, 93 patients aged ≥ 60 years who were diagnosed with MM and underwent ASCT were retrospectively analyzed. All patients underwent follow-ups and the deadline for follow-up was October 31, 2022. The Ig levels were measured using the immune turbidimetry method at 3, 6, and 12 months after transplantation. Patients who died or experienced relapse were excluded from the analysis. The prognostic value of Ig levels was estimated using the Kaplan-Meier survival curve and Cox regression method.

RESULTS

No patients died, and all patients with complications showed improvements after treatment. Patients in the Ig reconstitution group had a lower international staging system (ISS) stage, whereas those in the immunoparesis group had a higher ISS stage. The median duration of follow-up was 36 (range, 13-120) months. The Ig reconstitution within 12 months indicated a longer overall survival and progression-free survival outcomes. The detection of Ig levels was an independent indicator for the prognosis of MM.

DISCUSSION AND CONCLUSION

The Ig reconstitution within 12 months of ASCT could predict the overall outcomes of patients with MM aged ≥ 60 years.

Myeloma Spine and Bone Damage Score (MSBDS) on Whole-Body Computed Tomography (WBCT): Multiple Reader Agreement in a Multicenter Reliability Study

Objective: To assess the reliability of the myeloma spine and bone damage score (MSBDS) across multiple readers with different levels of expertise and from different institutions. Methods: A reliability exercise, including 104 data sets of static images and complete CT examinations of patients affected by multiple myeloma (MM), was performed. A complementary imaging atlas provided detailed examples of the MSBDS scores, including low-risk and high-risk lesions. A total of 15 readers testing the MSBDS were evaluated. ICC estimates and their 95% confidence intervals were calculated based on mean rating (k = 15), absolute agreement, a two-way random-effects model and Cronbach's alpha. Results: Overall, the ICC correlation coefficient was 0.87 (95% confidence interval: 0.79-0.92), and the Cronbach's alpha was 0.93 (95% confidence interval: 0.94-0.97). Global inter- and intra-observer agreement among the 15 readers with scores below or equal to 6 points and scores above 6 points were 0.81 (95% C.I.: 0.72-0.86) and 0.94 (95% C.I.:0.91-0.98), respectively. Conclusion: We present a consensus-based semiquantitative scoring systems for CT in MM with a complementary CT imaging atlas including detailed examples of relevant scoring techniques. We found substantial agreement among readers with different levels of experience, thereby supporting the role of the MSBDS for possible large-scale applications. Significance and Innovations • Based on previous work and definitions of the MSBDS, we present real-life reliability data for quantitative bone damage assessment in multiple myeloma (MM) patients on CT. • In this study, reliability for the MSBDS, which was tested on 15 readers with different levels of expertise and from different institutions, was shown to be moderate to excellent. • The complementary CT imaging atlas is expected to enhance unified interpretations of the MSBDS between different professionals dealing with MM patients in their routine clinical practice.

Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases

Context

Multiple myeloma and extensive lytic skeletal metastases may appear similar on positron-emission tomography and computed tomography (PET-CT) in the absence of an obvious primary site or occult malignancy. Radiomic analysis extracts a large number of quantitative features from medical images with the potential to uncover disease characteristics below the human visual threshold.

Aim

This study aimed to evaluate the diagnostic capability of PET and CT radiomic features to differentiate skeletal metastases from multiple myeloma.

Settings and Design

Forty patients (20 histopathologically proven cases of multiple myeloma and 20 cases of a variety of bone metastases) underwent staging 18F-fluorodeoxyglucose PET-CT at our institute.

Methodology

A total of 138 PET and 138 CT radiomic features were extracted by manual semi-automatic segmentation and standardized. The original dataset was subject separately to receiver operating curve analysis and correlation matrix filtering. The former showed 16 CT and 19 PET parameters to be significantly related to the outcome at 5%, whereas the latter resulted in 16 CT and 14 PET features. Feature selection was done with 7 evaluators with stratified 10-fold cross-validation. The selected features of each evaluator were subject to 14 machine-learning algorithms. In view of small sample size, two approaches for model performance were adopted: The first using 10-fold stratified cross-validation and the second using independent random training and test samples (26:14). In both approaches, the highest area under the curve (AUC) values were selected for 5 CT and 5 PET features. These 10 features were combined and the same process was repeated.

Statistical Analysis Used

The quality of the performance of the models was assessed by MSE, RMSE, kappa statistic, AUC, area under the precision-recall curve, F-measure, and Matthews correlation coefficient.

Results

In the first approach, the highest AUC = 0.945 was seen with 5 CT parameters. In the second approach, the highest AUC = 0.9538 was seen with 4 CT and one PET parameter. CT neighborhood gray-level different matrix coarseness and CT gray-level run-length matrix LGRE were common parameters in both approaches. Comparison of AUC of the above models showed no significant difference (P = 0.9845). Feature selection by principal components analysis and feature classification by the multilayer perceptron machine-learning model using independent training and test samples yielded the overall highest AUC.

Conclusions

Machine-learning models using CT parameters were found to differentiate bone metastases from multiple myeloma better than models using PET parameters. Combined models using PET and CECT data showed better overall performance than models using only either PET or CECT data. Machine-learning models using independent training and test sets were performed on par with those using 10-fold stratified cross-validation with the former incorporating slightly more PET features. Certain first- and second-order CT and PET texture features contributed in differentiating these two conditions. Our findings suggested that, in general, metastases were finer in CT and PET texture and myelomas were more compact.

CT-derived relationship between low relative muscle mass and bone damage in patients with multiple myeloma undergoing stem cells transplantation

OBJECTIVE

Sarcopenia or low muscle mass is related to worse prognosis in cancer patients. We investigated whether muscle mass is related to bone damage on CT in patients with multiple myeloma (MM).

METHODS

Approval from the institutional review board was obtained. N = 74 consecutive patients (mean age, 60.8 years ± 9.24 [standard deviation]; range, 36-89 years) for MM who underwent transplant were included. Sarcopenia cut-off points defined as skeletal muscle index (SMI) used were<41 cm2/m2. To assess bone damage in MM the MSBDS (myeloma spine and bone damage score) was used. One-way analysis of variance and the X2 test were used. Kaplan-Meier analysis was performed to generate progression and survival curves according to SMI and MSBDS. The testing level was set at 0.05.

RESULTS

The median SMI was 47.1 ± 14.2 and according to SMI 18/74 (24%) had sarcopenia which was more prevalent in females (p.001). A strong and significant association between patients with low muscle mass and elevated bone damage (24/74, 32.4%) and patients with normal/non-low muscle mass low bone damage (30/74, 40.5%) was present. Multiple Logistic regression did not show any significant relationship or confounding influence among SMI and MSBDS regarding sex (p.127), cytogenetic status (p.457), staging (p.756) and relapse (.126). Neither SMI nor MSBDS resulted significantly related to overall survival as shown in Kaplan-Meier analysis.

CONCLUSION

Sarcopenia and bone damage affected MM patients undergoing stem cell transplantation and are significantly associated.

ADVANCES IN KNOWLEDGE

Quantitative measurement of sarcopenia and bone damage on CT resulted present in MM patients undergoing stem cell transplantation and are significantly associated.

Imaging in multiple myeloma: Computed tomography or magnetic resonance imaging?

Multiple myeloma (MM) is the second most common type of hematological disease with its incidence rising in the elderly. In MM, the extent of the bone disease increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and magnetic resonance imaging (MRI) is crucial to separate asymptomatic from symptomatic MM patients even when no clinical symptoms are present. Although radiology is essential in the staging and management of patients with MM there is still high variability in the choice between MRI and CT. In addition, there is still suboptimal agreement among readers. The potential of medical imaging in MM is largely under-evaluated: artificial intelligence, radiomics and new quantitative methods to report CT and MRI will improve imaging usage.