Qualitative and Quantitative MRI Techniques for the Evaluation of Musculoskeletal Neoplasms

: This article's primary goal is to provide an image-based review to paediatricians to gain insight into the typical appearance of myelin evolution. We briefly discuss the structure and development of myelination, the role of qualitative and quantitative MRI in myelin imaging, and provide an image-based review as a quick reference for understanding the pattern of myelination on MR imaging.

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Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

BioMed Research International ◽

10.1155/2021/1235314 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Mengqiu Cao ◽

Shiteng Suo ◽

Xiao Zhang ◽

Xiaoqing Wang ◽

Jianrong Xu ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Feature Selection ◽

Idh1 Mutation ◽

Quantitative Mri ◽

Learning Approach ◽

Lower Grade ◽

Gray Level ◽

Qualitative And Quantitative ◽

Machine Learning Approach

Purpose. Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. Materials and Methods. A total of 102 LGG patients were allocated to training ( n = 67 ) and validation ( n = 35 ) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. Results. After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879. Conclusion. The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

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Quantitative 3-T Magnetic Resonance Imaging After Matrix-Associated Autologous Chondrocyte Implantation With Autologous Bone Grafting of the Knee: The Importance of Subchondral Bone Parameters

The American Journal of Sports Medicine ◽

10.1177/0363546520980134 ◽

2021 ◽

Vol 49 (2) ◽

pp. 476-486

Author(s):

Matthias Jung ◽

Dimitrios C. Karampinos ◽

Christian Holwein ◽

Joachim Suchowierski ◽

Thierno D. Diallo ◽

...

Keyword(s):

Magnetic Resonance ◽

Cartilage Repair ◽

Subchondral Bone ◽

Quantitative Mri ◽

Repair Tissue ◽

Water Fraction ◽

Qualitative And Quantitative ◽

Chondrocyte Implantation ◽

Defect Filling ◽

Autologous Chondrocyte

Background: Matrix-associated autologous chondrocyte implantation (MACI) with autologous bone grafting (ABG) is an effective surgical treatment for osteochondral defects. Quantitative magnetic resonance imaging (MRI) techniques are increasingly applied as noninvasive biomarkers to assess the biochemical composition of cartilage repair tissue. Purpose: To evaluate the association of quantitative MRI parameters of cartilage repair tissue and subchondral bone marrow with magnetic resonance morphologic and clinical outcomes after MACI with ABG of the knee. Study Design: Case series; Level of evidence, 4. Methods: Qualitative and quantitative 3 T MRI of the knee was performed in 21 patients (16 male) at 2.5 years after MACI with ABG at the medial (18/21) or lateral (3/21) femoral condyle for the treatment of osteochondral defects. Morphologic MRI sequences were assessed using MOCART (magnetic resonance observation of cartilage repair tissue) 2.0 scores. T2 relaxation time measurements for the assessment of cartilage repair tissue (CRT2) were obtained. Single-voxel magnetic resonance spectroscopy was performed in underlying subchondral bone marrow (BM) and at both central femoral condyles. The presence of pain and Tegner scores were noted. Statistical analyses included Student t tests, correlation analyses, and multivariate regression models. Results: The mean defect size was 4.9 ± 1.9 cm2. At a follow-up of 2.5 ± 0.3 years, 9 of 21 patients were asymptomatic. Perfect defect filling was achieved in 66.7% (14/21) of patients. MOCART 2.0 scores (74.1 ± 18.4) did not indicate pain (68.3 ± 19.0 [pain] vs 81.7 ± 15.4 [no pain]; P = .102). However, knee pain was present in 85.7% (6/7) of patients with deep bony defects (odds ratio, 8.0; P = .078). Relative CRT2 was higher in hypertrophic cartilage repair tissue than in repair tissue with normal filling (1.54 ± 0.42 vs 1.13 ± 0.21, respectively; P = .022). The underlying BM edema–like lesion (BMEL) volume was larger in patients with underfilling compared with patients with perfect defect filling (1.87 ± 1.32 vs 0.31 ± 0.51 cm3, respectively; P = .002). Patients with severe pain showed a higher BMEL volume (1.2 ± 1.3 vs 0.2 ± 0.4 cm3, respectively; P = .046) and had a higher BM water fraction (26.0% ± 12.3% vs 8.6% ± 8.1%, respectively; P = .026) than did patients without pain. Conclusion: Qualitative and quantitative MRI parameters including the presence of subchondral defects, CRT2, BMEL volume, and BM water fraction were correlated with cartilage repair tissue quality and clinical symptoms. Therefore, the integrity of subchondral bone was associated with outcomes after osteochondral transplantation.

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