Whole body imaging in musculoskeletal oncology: when, why, and how

Present and future of whole-body MRI in metastatic disease and myeloma: how and why you will do it

Metastatic disease and myeloma present unique diagnostic challenges due to their multifocal nature. Accurate detection and staging are critical for determining appropriate treatment. Bone scintigraphy, skeletal radiographs and CT have long been the mainstay for the assessment of these diseases, but have limitations, including reduced sensitivity and radiation exposure. Whole-body MRI has emerged as a highly sensitive and radiation-free alternative imaging modality. Initially developed for skeletal screening, it has extended tumor screening to all organs, providing morphological and physiological information on tumor tissue. Along with PET/CT, whole-body MRI is now accepted for staging and response assessment in many malignancies. It is the first choice in an ever increasing number of cancers (such as myeloma, lobular breast cancer, advanced prostate cancer, myxoid liposarcoma, bone sarcoma, …). It has also been validated as the method of choice for cancer screening in patients with a predisposition to cancer and for staging cancers observed during pregnancy. The current and future challenges for WB-MRI are its availability facing this number of indications, and its acceptance by patients, radiologists and health authorities. Guidelines have been developed to optimize image acquisition and reading, assessment of lesion response to treatment, and to adapt examination designs to specific cancers. The implementation of 3D acquisition, Dixon method, and deep learning-based image optimization further improve the diagnostic performance of the technique and reduce examination durations. Whole-body MRI screening is feasible in less than 30 min. This article reviews validated indications, recent developments, growing acceptance, and future perspectives of whole-body MRI.

Whole-body MRI in oncology: A comprehensive review

Whole-Body Magnetic Resonance Imaging (WB-MRI) has cemented its position as a pivotal tool in oncological diagnostics. It offers unparalleled soft tissue contrast resolution and the advantage of sidestepping ionizing radiation. This review explores the diverse applications of WB-MRI in oncology. We discuss its transformative role in detecting and diagnosing a spectrum of cancers, emphasizing conditions like multiple myeloma and cancers with a proclivity for bone metastases. WB-MRI's capability to encompass the entire body in a singular scan has ushered in novel paradigms in cancer screening, especially for individuals harboring hereditary cancer syndromes or at heightened risk for metastatic disease. Additionally, its contribution to the clinical landscape, aiding in the holistic management of multifocal and systemic malignancies, is explored. The article accentuates the technical strides achieved in WB-MRI, its myriad clinical utilities, and the challenges in integration into standard oncological care. In essence, this review underscores the transformative potential of WB-MRI, emphasizing its promise as a cornerstone modality in shaping the future trajectory of cancer diagnostics and treatment.

(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.

Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton

OBJECTIVES

To report the diagnostic performance of machine learning-based CT texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton.

METHODS

We retrospectively evaluated 172 patients with multiple myeloma (n = 70) and osteolytic metastatic bone lesions (n = 102) in the peripheral skeleton. Two radiologists individually used two-dimensional manual segmentation to extract texture features from non-contrast CT. In total, 762 radiomic features were extracted. Dimension reduction was performed in three stages: inter-observer agreement analysis, collinearity analysis, and feature selection. Data were randomly divided into training (n = 120) and test (n = 52) groups. Eight machine learning algorithms were used for model development. The primary performance metrics were the area under the receiver operating characteristic curve and accuracy.

RESULTS

In total, 476 of the 762 texture features demonstrated excellent interobserver agreement. The number of features was reduced to 22 after excluding those with strong collinearity. Of these features, six were included in the machine learning algorithms using the wrapper-based classifier-specific technique. When all eight machine learning algorithms were considered for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton, the area under the receiver operating characteristic curve and accuracy were 0.776-0.932 and 78.8-92.3%, respectively. The k-nearest neighbors model performed the best, with the area under the receiver operating characteristic curve and accuracy values of 0.902 and 92.3%, respectively.

CONCLUSION

Machine learning-based CT texture analysis is a promising method for discriminating multiple myeloma from osteolytic metastatic bone lesions.

Functional Imaging in the Evaluation of Treatment Response in Multiple Myeloma: The Role of PET-CT and MRI

Bone disease is among the defining characteristics of symptomatic Multiple Myeloma (MM). Imaging techniques such as fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET/CT) and magnetic resonance imaging (MRI) can identify plasma cell proliferation and quantify disease activity. This function renders these imaging tools as suitable not only for diagnosis, but also for the assessment of bone disease after treatment of MM patients. The aim of this article is to review FDG PET/CT and MRI and their applications, with a focus on their role in treatment response evaluation. MRI emerges as the technique with the highest sensitivity in lesions' detection and PET/CT as the technique with a major impact on prognosis. Their comparison yields different results concerning the best tool to evaluate treatment response. The inhomogeneity of the data suggests the need to address limitations related to these tools with the employment of new techniques and the potential for a complementary use of both PET/CT and MRI to refine the sensitivity and achieve the standards for minimal residual disease (MRD) evaluation.