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Dr. Majid Chalian

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Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Washington Medical Center, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way Northeast, Box 354755, Seattle, WA 98105, USA
Interests: radiology; imaging; musculoskeletal radiology
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Dear Colleagues,

Multiple myeloma (MM) remains a debilitating disease despite recent advances in diagnosis and treatment with 1:150 lifetime risk, higher incidence in the elderly and black population, and approximately 10-year life expectancy after diagnosis in the United States. Advances in imaging technology with multi-slice CTs, high-resolution 3T MRIs, and state-of-the-art CT, MRI, and PET/CT techniques have revolutionized the diagnosis of musculoskeletal disorders, including MM, and provided us with new opportunities for improved patient care. New imaging techniques have made it possible to predict disease processes. Modern imaging has created an opportunity for the early diagnosis and treatment of patients with MM.

This field has already changed and will continue changing with novel approaches such as radiomics, machine learning, and quantitative analysis using multiparametric imaging. All these new emerging diagnostic approaches are advocating the idea of precision and personalized medicine. This Special Issue aims to provide updates on novel diagnostic approaches for imaging in patients with MM.

Dr. Majid Chalian
Guest Editor

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Keywords

PET/CT
MRI
multiple myeloma
diagnosis
multiparametric imaging

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17 pages, 686 KiB

Open AccessSystematic Review

Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review

by Ehsan Alipour, Atefe Pooyan, Firoozeh Shomal Zadeh, Azad Duke Darbandi, Pietro Andrea Bonaffini and Majid Chalian

Diagnostics 2023, 13(21), 3372; https://doi.org/10.3390/diagnostics13213372 - 02 Nov 2023

Viewed by 1488

Abstract

Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including “artificial intelligence” in “radiologic examinations” of patients with “multiple myeloma”. The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM. Full article

(This article belongs to the Special Issue Advances in Multiple Myeloma Imaging)

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