Musculoskeletal MRI Findings of Haematological Diseases

Background Hematologic diseases are a group of prevalent and clinically diverse diseases that can affect any organ system. Hematologic disorders frequently involve bone and associated tissues causing significant alterations in the bone marrow and may have relevant side effects on the skeleton. In order to evaluate findings in bone marrow on MR imaging, it is essential to understand the normal composition and distribution of bone marrow and the changes in marrow that occur with age, as well as the basis for the MR signals from marrow and the factors that affect those signals. Aim of the Work To describe the musculoskeletal MRI findings in patients with hematological diseases. Patients and Methods cross sectional study was conducted in Ain Shams University hospitals on patients confirmed with hematological disease undergoing musculoskeletal MRI. Conclusion Magnetic resonance imaging is very beneficial noninvasive modality to evaluate bone marrow and detecting marrow lesions due to its ability to provide information at the level of cellular and chemical composition. Knowing normal marrow components and composition and their variation, as well as of factors that affect MR signal intensity, is important for optimal interpretation of MR images. The signal intensity, morphology, and location of marrow findings on MRI can be used to provide accurate diagnoses and to guide treatment of the discussed hematological diseases.

The Use of Gadolinium in Musculoskeletal MRI—Time to Rethink?

Magnetic resonance imaging has continued to evolve over the recent decades, in part, due to the evolution of gadolinium-based contrast agents and their use. These were initially thought to have a relatively low-risk profile. However, there is mounting evidence that trace amounts of gadolinium are retained within the body. To ascertain the current use of gadolinium in medical practice, we performed a survey of musculoskeletal radiologists, within the United Kingdom, Europe and India. The survey demonstrated varied practices amongst all radiologists with relatively indiscriminate use of gadolinium. In this review, we discuss the current evidence for and against the use of gadolinium in musculoskeletal magnetic resonance imaging.

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

Exome Sequencing Reveals Diagnosis of LAMA2-Muscular Dystrophy and Possibility of Coexisting Bethlem Myopathy in a Neonate

We reported a neonate presenting with muscle weakness, hypotonia, and joint contractures since birth. Investigations revealed significantly elevated creatinine-phosphokinase, abnormal electromyography suggestive of muscle disease and normal magnetic resonance imaging (MRI) of the brain. Exome sequencing revealed homozygous pathogenic mutations in LAMA2 (NM_000426.3: c.7881T > G, p.(His2627Gln)) and a heterozygous likely-pathogenic mutation in COL6A2 (NM_001849.3: c.1970–2A > G). Parental segregation by Sanger sequencing confirmed a heterozygous carrier state for the LAMA2 variant in both parents, thus confirming the diagnosis of autosomal recessive LAMA2-muscular dystrophy (LAMA2-MD) in the proband. The COL6A2 variant segregated with the as-yet asymptomatic mother. Musculoskeletal MRI of the proband at 12 months of age revealed peripheral involvement of the vastii, rectus femoris, gastrocnemius and the soleus, with relative central sparing, without areas of fatty infiltration; not serving to distinguish clearly between LAMA-MD and COL6A2- related disease. Reverse phenotyping of a 27-year-old mother revealed a normal musculoskeletal MRI and clinically absent red flags. Potential explanations for the heterozygous likely-pathogenic COL6A2 variant in the proband and the mother include (a) a coexisting diagnosis of autosomal dominant COL6A2-related myopathy, likely Bethlem myopathy, which has a variable clinical phenotype and age of onset; (b) a carrier state for autosomal recessive Ullrich congenital muscular dystrophy; or (c) a heterozygous COL6A2 variant contributing as a synergistic factor along with homozygous LAMA2 mutation. The couple was offered genetic counseling regarding the proband and the future pregnancies.