To assess the impact of a single intra-articular (IA) injection of bone marrow–derived mesenchymal stem cells (BM-MSCs) in patients with knee osteoarthritis (OA), a randomized, double-blind, placebo-controlled study was conducted. The study included 24 patients with knee OA who were randomly assigned to receive either a single IA injection of BM-MSCs or normal saline. Changes in the visual analog scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS) after IA injection were assessed at 3, 6, 9, and 12 months. Magnetic resonance imaging (MRI) with T2 mapping sequences was conducted for knee cartilage assessment at baseline and at 3 and 12 months. The MSC group showed between-group improvement in WOMAC (–5.0 ± 3.6 vs. –0.1 ± 5.5, P = 0.02) and KOOS (23.9 ± 18.3 vs. 7.2 ± 15.9, P = 0.028) scores at 9 months compared with the control group. The MSC group exhibited a less sharp increase in the mean T2 value of the medial compartment than the control group at 12 months, with no serious adverse events observed during follow-up. A single IA injection of allogeneic BM-MSCs provided satisfactory pain relief for patients with knee OA compared with the control group at 9 months. Quantitative T2 MRI mapping of the cartilage showed that IA BM-MSCs could have a preventive effect on OA progression for 12 months. Our findings suggest the potential of allogeneic BM-MSCs IA injection as a pain-relieving and disease-modifying treatment for patients with knee OA in the outpatient setting.

Objective: To evaluate the intravoxel incoherent motion (IVIM) diffusion-weighted (DW) MRI for differentiating between benign and malignant musculoskeletal tumours at 3 T. Methods: 65 patients with treatment-naïve musculoskeletal tumours (47 malignant and 23 benign lesions) who underwent 3 T MRI including IVIM DW imaging were included. IVIM-derived parameters included pure diffusion coefficient (D), perfusion related incoherent microcirculation (D*, pseudodiffusion coefficient), and perfusion fraction (f). IVIM parameters and mono-exponential apparent diffusion coefficient (ADC) were retrospectively measured by two independent musculoskeletal radiologists. Results: D and ADC values of malignant tumours (923 ± 360, 965 ± 353 µm2 s–1, respectively) were significantly lower than those of benign tumours (1668 ± 546, 1689 ± 526 µm2 s–1) (p < 0.001). F values of malignant tumours (9.6%) were significantly higher than those of benign tumours (7.2%) (p = 0.021), whereas D* values showed no significant difference ...

We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n = 360) and a test set (n = 107). A sagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrows were segmented. The various kernel functions and SVM input dimensions were experimented to construct the most optimal classifier model. The accuracy and sensitivity increased as the number of training set sizes increased from 180 to 360. The test set was analyzed by SVM and two independent readers, and the accuracy and sensitivity of the SVM classifier, reader 1 and reader 2 were 82.2% and 85.5%, 79.4% and 82.3%, and 82.2% and 83.9%, respectively. The area under receiver operating characteristic curve (AUC) of the SVM classifier, reader 1 and reader 2 were 0.895, 0.879 and 0.880, respectively. The SVM texture classifier produced comparable performance to radiologists in isolating the hematologic diseases, which could support inexperienced physicians with spinal MRI to screen patients with marrow diseases, who need further diagnostic work-ups to make final decisions.

Osteolytic lesions can be seen in both multiple myeloma (MM), and osteolytic bone metastasis on computed tomography (CT) scans. We sought to assess the feasibility of a CT-based radiomics model to distinguish MM from metastasis. This study retrospectively included patients with pre-treatment thoracic or abdominal contrast-enhanced CT from institution 1 (training set: 175 patients with 425 lesions) and institution 2 (external test set: 50 patients with 85 lesions). After segmenting osteolytic lesions on CT images, 1218 radiomics features were extracted. A random forest (RF) classifier was used to build the radiomics model with 10-fold cross-validation. Three radiologists distinguished MM from metastasis using a five-point scale, both with and without the assistance of RF model results. Diagnostic performance was evaluated using the area under the curve (AUC). The AUC of the RF model was 0.807 and 0.762 for the training and test set, respectively. The AUC of the RF model and the radiologists (0.653–0.778) was not significantly different for the test set (p ≥ 0.179). The AUC of all radiologists was significantly increased (0.833–0.900) when they were assisted by RF model results (p < 0.001). In conclusion, the CT-based radiomics model can differentiate MM from osteolytic bone metastasis and improve radiologists’ diagnostic performance.

It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. Our aim is to determine the feasibility of using radiomics analysis to detect MM infiltration of the bone marrow on CT scans of patients with osteopenia. The contrast-enhanced thoracic CT scans of 104 patients with MM and 104 age- and sex-matched controls were retrospectively evaluated. All individuals had decreased bone density on radiography. The study group was divided into development (n = 160) and temporal validation sets (n = 48). The radiomics model was developed using 805 texture features extracted from the bone marrow for a development set, using a Random Forest algorithm. The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. The diagnostic performances were assessed and compared using an area under the receiver operating characteristic curve (AUC) analysis. The AUC of the radiomics model was not significantly different from those of the radiologists (p = 0.056–0.821). The radiomics analysis results showed potential for detecting MM infiltration in the bone marrow on CT scans of patients with osteopenia.