Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers

Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma

Multiple myeloma (MM) is a malignant blood disease, but there have been significant improvements in the prognosis due to advancements in quantitative assessment and targeted therapy in recent years. The quantitative assessment of MM bone marrow infiltration and prognosis prediction is influenced by imaging and artificial intelligence (AI) quantitative parameters. At present, the primary imaging methods include computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These methods are now crucial for diagnosing MM and evaluating myeloma cell infiltration, extramedullary disease, treatment effectiveness, and prognosis. Furthermore, the utilization of AI, specifically incorporating machine learning and radiomics, shows great potential in the field of diagnosing MM and distinguishing between MM and lytic metastases. This review discusses the advancements in imaging methods, including CT, MRI, and PET/CT, as well as AI for quantitatively assessing MM. We have summarized the key concepts, advantages, limitations, and diagnostic performance of each technology. Finally, we discussed the challenges related to clinical implementation and presented our views on advancing this field, with the aim of providing guidance for future research.

The quantitative parameters based on marrow metabolism derived from synthetic MRI: A pilot study of prognostic value in participants with newly diagnosed multiple myeloma

BACKGROUND

The value of SyMRI-derived parameters from lumbar marrow for predicting early treatment response and optimizing the risk stratification of the Revised International Staging System (R-ISS) in participants with multiple myeloma (MM) is unknown.

METHODS

We prospectively enrolled participants with newly diagnosed MM before treatment. The SyMRI of lumbar marrow was used to calculate T1, T2, and PD values and the clinical features were collected. All participants were divided into good response (≥VGPR) and poor response (

RESULTS

Fifty-nine participants (good response, n = 33; poor response, n = 26) were evaluated. The bone marrow plasma cell percentage, β2-microglobulin, T1 and T2 value were difference between two groups (all p < 0.05). The T1 (odds ratio 1.003, p = 0.005) and T2 values (odds ratio 0.910, p = 0.002) were independent predictors and the AUC and cut-off values were 0.787, 967.2 ms and 0.784, 75.9 ms, respectively. There were no significant differences in SyMRI parameters between genders. Participants with both T1 value ≥967.2 ms and T2 value ≤75.9 ms in the R-ISS II stage were potentially to get poor response.

CONCLUSIONS

Synthetic MRI is a promising tool for predicting early treatment response to MM and promoting R-ISS II stage risk stratification.

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Key Words

• R‐ISS stage
• early treatment response
• magnetic resonance imaging
• multiple myeloma
• tumor burden

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MESH Headings

• Humans
• Male
• Female
• Prognosis
• Multiple Myeloma/diagnostic imaging
• Multiple Myeloma/pathology
• Bone Marrow/diagnostic imaging
• Bone Marrow/pathology
• Pilot Projects
• Neoplasm Staging
• Magnetic Resonance Imaging
• Retrospective Studies

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Grants

• 82071898 National Natural Science Foundation of China
• National Natural Science Foundation of China

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Affiliation(s)

Sha Cui Department of Medical ImagingShanxi Medical UniversityTaiyuanChina Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Yinnan Guo Department of PainFifth Hospital of Shanxi Medical UniversityTaiyuanChina
Weiran Niu Department of Medical ImagingShanxi Medical UniversityTaiyuanChina
Jianting Li Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Wenjin Bian Department of Medical ImagingShanxi Medical UniversityTaiyuanChina
Wenqi Wu Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Wenjia Zhang Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Qian Zheng Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Jun Wang Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina
Jinliang Niu Department of RadiologySecond Hospital of Shanxi Medical UniversityTaiyuanChina

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Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors

Magnetic resonance imaging (MRI) is essential in the management of musculoskeletal (MSK) tumors. This review delves into the diverse MRI modalities, focusing on anatomical, functional, and metabolic sequences that provide essential biomarkers for tumor detection, characterization, disease extent determination, and assessment of treatment response. MRI's multimodal capabilities offer a range of biomarkers that enhance MSK tumor evaluation, aiding in better patient management.

Neuroradiology of acute pathologies in adults with hematologic malignancies: a pictorial review

Hematopoietic and lymphoid tumors are a heterogeneous group of diseases including lymphomas, multiple myeloma (MM), and leukemias. These diseases are associated with systemic involvement and various clinical presentations including acute neurological deficits. Adult patients with hematologic malignancies (HM) are at risk for developing a wide array of acute conditions involving the nervous system. HM in adults may present as tumoral masses responsible for mass effect, possibly resulting in acute neurological signs and symptoms caused by tumor growth with compression of central nervous system (CNS) structures. Moreover, as result of the hematologic disease itself or due to systemic treatments, hematologic patients are at risk for vascular pathologies, such as ischemic, thrombotic, and hemorrhagic disorders due to the abnormal coagulation status. The onset of these disorders is often with acute neurologic signs or symptoms. Lastly, it is well known that patients with HM can have impaired function of the immune system. Thus, CNS involvement due to immune-related diseases such as mycotic, parasitic, bacterial, and viral infections linked to immunodeficiency, together with immune reconstitution inflammatory syndrome, are frequently seen in hematologic patients. Knowledge of the etiology and expected CNS imaging findings in patients with HM is of great importance to reach a fast and correct diagnosis and guide treatment choices. In this manuscript, we review the computed tomography (CT) and magnetic resonance findings of these conditions which can be related to the disease itself and/or to their treatments.

Fat quantification: Imaging methods and clinical applications in cancer

Recently, the study of the relationship between lipid metabolism and cancer has evolved. The characteristics of intratumoral and peritumoral fat are distinct and changeable during cancer development. Subcutaneous and visceral adipose tissue are also associated with cancer prognosis. In non-invasive imaging, fat quantification parameters such as controlled attenuation parameter, fat volume fraction, and proton density fat fraction from different imaging methods complement conventional images by providing concrete fat information. Therefore, measuring the changes of fat content for further understanding of cancer characteristics has been applied in both research and clinical settings. In this review, the authors summarize imaging advances in fat quantification and highlight their clinical applications in cancer precaution, auxiliary diagnosis and classification, therapy response monitoring, and prognosis.

The Apparent Diffusion Coefficient of Diffusion-Weighted Whole-Body Magnetic Resonance Imaging Affects the Survival of Multiple Myeloma Independently

Background

Diffusion-weighted whole-body MRI (DW-MRI) is increasingly used to evaluate bone diseases of multiple myeloma (MM), but there is lack of quantitative indicator for DW-MRI to reflect the prognosis of MM. Apparent diffusion coefficient (ADC) values in DW-MRI has potential correlations between some indexes of MM, but the influence of ADC on MM survival needs to be further verified.

Methods

A total of 381 newly diagnosed MM patients were enrolled in the study to analyze the effect of ADC values in DW-MRI on progression-free survival (PFS) and overall survival (OS). The Kaplan–Meier method was used to perform univariate survival analysis, and the Cox proportional hazards model was used for multivariate analysis. In addition to the ADC value, genetic and serological indexes were also included.

Results

The survivals were observed in univariate ADC stratification with median PFS of 52.0, 45.0, 34.0, and 26.0 months (the unit of ADC value was 10⁻³ mm²/s; the ADC ranges were ADC < 0.4886, 0.4886 ≤ ADC < 0.6545, 0.6545 ≤ ADC < 0.7750, and ADC ≥ 0.7750; 95% CI, 43.759–62.241, 46.336–53.664, 39.753–46.247, and 27.812–32.188). The OS were 81.0, 61.0, 47.0, and 36.0 months (p < 0.001; 95% CI, 71.356–82.644, 67.630–70.370, 57.031–60.969, and 36.107–43.893). In Cox proportional hazards model, the ADC value was considered to be an independent risk factor affecting PFS and OS of MM (both p < 0.001).

Conclusions

This study supports that ADC in DW-MRI may independently stratify MM patients and better predict their prognosis. The combined use of DW-MRI and other parameters allows more accurate evaluation of MM survival.

Trial Registration

http://www.chictr.org.cn/showproj.aspx?proj=49012, ChiCTR2000029587.