Development of a new risk stratification system for patients with newly diagnosed multiple myeloma using R-ISS and 18F-FDG PET/CT

Evidence-based Korean guidelines for the clinical management of multiple myeloma: addressing 12 key clinical questions

Multiple myeloma (MM), a hematological malignancy, is characterized by malignant plasma cell proliferation in the bone marrow. Recent treatment advances have significantly improved patient outcomes associated with MM. In this study, we aimed to develop comprehensive, evidence-based guidelines for the diagnosis, prognosis, and treatment of MM. We identified 12 key clinical questions essential for MM management, guiding the extensive literature review and meta-analysis of the study. Our guidelines provide evidence-based recommendations by integrating patient preferences with survey data. These recommendations include current and emerging diagnostic tools, therapeutic agents, and treatment strategies. By prioritizing a patient-centered approach and rigorous data analysis, these guidelines were developed to enhance MM management, both in Korea and globally.

Clinical value of 18F-FDG PET/CT in patients with newly diagnosed acute leukemia

PURPOSE

To explore the correlation between semi-quantitative parameters of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scans findings and the clinical features of patients with acute leukemia (AL), as well as to evaluate the clinical utility of 18F-FDG PET/CT in the management of AL.

METHODS

A retrospective study was conducted with 44 patients newly diagnosed with acute leukemia (AL) at Zhongnan Hospital of Wuhan University between January 2019 and August 2024.

RESULTS

Multivariate analysis revealed that age at diagnosis of AL (odds ratio [OR]: 0.888, P < 0.01) and percentage of blasts in the peripheral blood (PB) (OR: 1.061, P < 0.05) were independent predictors of the appearance of active extramedullary disease (EMD). Kaplan-Meier survival analysis for patients with EMD(+) indicated that those with organ infiltration beyond the lymph nodes experienced markedly reduced overall survival (OS) compared to those without such infiltration (157 days and 806 days, respectively). Furthermore, in the AL subgroup with EMD, the ratio of the maximum standardized uptake value (SUVmax) in the bone marrow (BM) to SUVmax of the liver emerged as an independent prognostic factor for OS (Hazard ratio [HR]: 2.372; 95% confidence interval [CI]: 1.079-5.214, P < 0.05).

CONCLUSION

18F-FDG PET/CT offers the benefits of being non-invasive and highly sensitive for the thorough evaluation of disease status in patients newly diagnosed with AL. Furthermore, the SUVmax BM/liver ratio is of significant clinical importance for prognosticating outcomes in patients with AL presenting EMD.

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.

Clinical profiles in multiple myeloma patients with extreme survivals: a study from a National Medical Center in China

OBJECTIVES

The clinical outcomes of multiple myeloma (MM) patients are highly variable in the real-world setting. Some MM patients may have clinical endings that do not abide by the book. We aim to describe features of MM patients with extreme survivals in real-world practice.

METHODS

This retrospective study enrolled 941 patients consecutively visited a national medical center, China, between July 1995 and December 2021. Among patients, we identified two groups of MM patients with extreme survivals, 56 were in the long-term remission (LR) group with progression-free survival (PFS) ≥ 60 months, and 82 were in the rapid progression (RP) group with PFS ≤ 6 months.

RESULTS

CRAB features, of which hypercalcemia, renal insufficiency, and anemia were more common in the RP group, except for bone disease, with a comparable incidence at diagnosis in both groups (88.8 vs 85.7%, P = 0.52). High-risk cytogenetics was detected in 45.7% of patients in the RP group. Of note, 14.3% of MM patients in the LR group harbored del (17p). According to the Revised International Staging System (R-ISS), 9% of patients belonged to stage I in the RP group, and 19% of patients in the LR group were found in stage III. There were 8 (15.7%) patients in the LR group only achieved partial response (PR) as the best response. Median time to best response (TBR) for LR and RP group patients was 4.6 and 1.4 months, respectively.

CONCLUSIONS

The disparities in the survivals of MM patients indicated that some unexpected factors have influenced the outcomes in the real-world setting.

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.

Semi-quantitative FDG parameters predict survival in multiple myeloma patients without autologous stem cell transplantation

BACKGROUND

F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is useful in multiple myeloma (MM) for initial workup and treatment response evaluation. Herein, we evaluated the prognostic value of semi-quantitative FDG parameters for predicting the overall survival (OS) of MM patients with or without autologous stem cell transplantation (ASCT).

METHODS

Study subjects comprised 227 MM patients who underwent baseline FDG PET/CT. Therein, 123 underwent ASCT while 104 did not. Volumes of interest (VOIs) of bones were drawn on CT images using a threshold of 150 Hounsfield units. FDG parameters of maximum standardized uptake value (SUVmax), mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and number of focal lesions (FLs) were measured. Kaplan-Meier survival analysis with log-rank tests and Cox proportional hazards regression analyses were performed for overall survival (OS).

RESULTS

In the ASCT cohort, R-ISS stage, MTV, and TLG were associated with survival. In the non-ASCT cohort, however, R-ISS stage was not associated with patient outcomes. In contrast, high SUVmax, SUVmean, MTV, TLG, and FL could predict worse OS (hazard ratio [HR] = 2.569, 2.649, 2.506, 2.839, and 1.988, respectively). Importantly, combining FDG parameters with R-ISS stage provided a new risk classification system that discriminated worse OS in the non-ASCT cohort significantly better than did R-ISS stage alone.

CONCLUSIONS

In the non-ASCT cohort, semi-quantitative FDG parameters were significant predictors of worse OS. Furthermore, combining FDG parameters with R-ISS stage may provide a new risk staging system that can better stratify the survival of MM patients without ASCT.

The role of staging in multiple myeloma

INTRODUCTION

The importance of cancer staging is determined by how accurately it can predict prognosis, and how useful it is for treatment decisions. Compared to other malignancies, multiple myeloma (MM) staging proved more challenging because of unreliable prognostic factors and wide-ranging life expectancy. As traditional MM staging continues to evolve, it requires reassessment of its prognostic and predictive value.

AREAS COVERED

The studies that included prognostic and predictive value of MM stages from 1975 through 2023 were selected for this review using PubMed, MEDLINE platforms. The history and evolution of MM staging are revisited, including its role in predicting survival, treatment planning and potential practical implications for the future. The role of MM staging for oncological practice and patient counseling is discussed.

EXPERT OPINION

The utility of the traditional MM staging remains unsatisfactory because it lacks a strong connection with the disease biology, prognosis or treatment planning. Additionally, it demonstrates a modest value for patient counseling because individual prognosis is subject to under- or overestimation, and the median survival or survival rates are difficult concepts to grasp. Although the role of MM stages may change in the future, the current research upholds the notion that MM staging benefits more medical research and clinical trials than oncological practice.

Clinical Value of FDG-PET/CT in Multiple Myeloma: An Update

FDG-PET/CT is a standardized imaging technique that has reached a great importance in the management of patients affected by Multiple Myeloma. It is proved, in fact, that it allows a deep evaluation of therapy efficacy and provides several prognostic indexes both at staging and after therapy. For this reason, it is now recognised as a gold standard for therapy assessment. Beside this, in reacent years FDG-PET/CT contribution to the understanding of Multiple Myeloma has progressively grown. Papers have been published analyzing the prognostic value of active disease volume measurement and standardization issues, the meaning of FDG positive paramedullary and extrameduallary disease, the prognostic impact of FDG positive minimal residual disease, the relation between focal lesions and clonal eterogenity of this disease and the comparison with whole body DWI-MR in terms of detection and therapy assessment. These newer aspects not of clinical impact yet, of FDG-PET/CT in Multiple Myeloma will be presented and discussed in this review.

A Machine Learning Model to Predict Survival and Therapeutic Responses in Multiple Myeloma

Multiple myeloma (MM) is a highly heterogeneous hematologic tumor. Ubiquitin proteasome pathways (UPP) play a vital role in its initiation and development. We used cox regression analysis and least absolute shrinkage and selector operation (LASSO) to select ubiquitin proteasome pathway associated genes (UPPGs) correlated with the overall survival (OS) of MM patients in a Gene Expression Omnibus (GEO) dataset, and we formed this into ubiquitin proteasome pathway risk score (UPPRS). The association between clinical outcomes and responses triggered by proteasome inhibitors (PIs) and UPPRS were evaluated. MMRF CoMMpass was used for validation. We applied machine learning algorithms to MM clinical and UPPRS in the whole cohort to make a prognostic nomogram. Single-cell data and vitro experiments were performed to unravel the mechanism and functions of UPPRS. UPPRS consisting of 9 genes showed a strong ability to predict OS in MM patients. Additionally, UPPRS can be used to sort out the patients who would gain more benefits from PIs. A machine learning model incorporating UPPRS and International Staging System (ISS) improved survival prediction in both datasets compared to the revisions of ISS. At the single-cell level, high-risk UPPRS myeloma cells exhibited increased cell adhesion. Targeted UPPGs effectively inhibited myeloma cells in vitro. The UPP genes risk score is a helpful tool for risk stratification in MM patients, particularly those treated with PIs.

Newly Diagnosed Multiple Myeloma Patients with Skeletal-Related Events and Abnormal MRI Pattern Have Poor Survival Outcomes: A Prospective Study on 370 Patients

Contemporary information is sparse on the frequency of skeletal-related events (SREs) in multiple myeloma (MM) patients at a population-based level in the era of novel agents. In this context, we conducted this single-center, prospective, observational study to determine the incidence of SREs among newly diagnosed MMs (NDMM) and to explore the possible correlations with disease characteristics, imaging finding, and patient prognosis. A total of 370 patients with available baseline MRIs were included. Among them, 208 (56%) presented with at least one SRE at diagnosis. Fractures were the most common reported SREs (48%). The incidence of SREs at diagnosis was higher in patients with osteolytic lesions, abnormal MRI pattern, hypercalcemia, and at least 60% bone marrow infiltration by plasma cells. Importantly, the patients with normal MRI pattern, who did not present with SREs at diagnosis, had statistically significant improved median OS in comparison with the patients who had abnormal MRI patterns and/or the presence of SREs at diagnosis (9.3 vs. 6.6 years, p = 0.048). Our data, which represent one of a few systematic reports on the incidence and characteristics of SREs in the era of novel agents, was indicative of a high incidence of SREs at the time of MM diagnosis. Early detection of myeloma bone disease and tailored patient management are essential to optimize patient outcomes.