Skeletal Radiology最新文献

Nodular fasciitis is a benign soft tissue pseudotumour typically occurring in the upper extremities, head and neck, thigh and trunk. It is most commonly seen in subcutaneous locations but also can be present in intramuscular and intermuscular (fascial) locations. Its occurrence in the hand is rare, and while it can occur in close proximity to tendons, its presentation as an intra-tendinous mass has not been previously described. We present a unique and rare case of nodular fasciitis arising within the flexor digitorum profundus (FDP) tendon of the hand in a 16-year-old female. The patient presented with a painful swelling in the volar aspect of the base of her left middle finger, with progressive flexion deformity of the finger. Ultrasound and magnetic resonance imaging revealed a mass within the FDP tendon of the middle finger. An ultrasound-guided biopsy revealed a diagnosis of nodular fasciitis. Given the self-limiting nature of the condition, she was managed conservatively with close clinical and imaging follow-up. This case highlights the importance of considering nodular fasciitis in the differential diagnosis of an intra-tendinous lesion in the hand, even though it is a rare occurrence in this location. The clinical presentation, diagnostic workup, and management of this unique case are discussed, emphasising the potential for its misdiagnosis as a malignancy which can have important implications in management.

Objective: A new axial loading device was used to investigate the effects of axial loading and positions on lumbar structure and lumbar spinal stenosis.

Methods: A total of 40 patients sequentially underwent 4 examinations: (1) the psoas-relaxed position MRI, (2) the extended position MRI, (3) the psoas-relaxed position axial loading MRI, (4) the extended position axial loading MRI. The dural sac cross-sectional area, sagittal vertebral canal diameter, disc height and ligamentum flavum thickness of L3-4, L4-5, L5-S1 and lumbar lordosis angle were measured and compared. A new device with pneumatic shoulder-hip compression mode was used for axial loading.

Results: In the absence of axial loading, there was a significant reduction in dural sac cross-sectional area with extension only seen at the L3-4 (p = 0.033) relative to the dural sac area in the psoas-relaxed position. However, with axial loading, there was a significant reduction in dural sac cross-sectional area at all levels in both psoas-relaxed (L3-4, p = 0.041; L5-S1, p = 0.005; L4-5, p = 0.002) and extension (p < 0.001) positions. The sagittal vertebral canal diameter and disc height were significantly reduced at all lumbar levels with axial loading and extension (p < 0.001); however, in psoas-relaxed position, the sagittal vertebral canal diameter was only reduced with axial loading at L3-4 (p = 0.018) and L4-5 (p = 0.011), and the disc height was reduced with axial-loading at all levels (L3-4, p = 0.027; L5-S1, p = 0.001; L4-5, p < 0.001). The ligamentum flavum thickness and lumbar lordosis in extension position had a statistically significant increase compared to psoas-relaxed position with or without axial loading (p < 0.001).

Conclusion: Both axial loading and extension of lumbar may exacerbate lumbar spinal stenosis. Axial loading in extension position could maximally aggravate lumbar spinal stenosis, but may cause some patients intolerable. For those patients, axial loading MRI in psoas-relaxed position may be a good choice.

Objective: To determine the diagnostic performance of MRI in evaluating meniscal abnormalities in the setting of anterior cruciate ligament (ACL) injury and recognize predictors of false positive (FP) and false negative (FN) MRI diagnosis.

Material and methods: Four hundred twenty patients (mean age, 27.2 years; 326 males, 94 females) who underwent arthroscopy for ACL injury between January 2017 and August 2022, and had preoperative imaging within 4 months, were retrospectively included. Images were independently interpreted by two experienced musculoskeletal radiologists, noting the presence of medial and lateral meniscal tears including tear type and location. Results were correlated with arthroscopic findings. Multivariate logistic regression was implemented to study risk factors (RF) for FP and FN MRI diagnosis.

Results: The sensitivity/specificity/positive predictive value/negative predictive value/accuracy of MRI for medial meniscus tear was 97.5%/74.46%/65.63%/98.35%/82.15%; for lateral meniscus tear, it was 83.5%/93.70%/70.8%/94.55% /87.86%, with substantial interreader agreement. Female gender (odds ratio (OR), 0.434), posterior horn and posterior root tears (OR, 3.268/22.588), horizontal tear (OR, 3.134), and ramp lesion (OR, 4.964) were found RF for FP medial meniscus, and meniscal body tears (OR, 308.011) were found RF for FP lateral meniscus. RF for FN medial meniscus were meniscal tear at the posterior horn, body, and posterior root (OR, 12.371/123.000/13.045).

Conclusion: MRI is an effective screening tool for meniscal tears, but less accurate in detecting all medial meniscus injuries. Gender, meniscal tear location, and type increased the risk of FP medial meniscal tear on MRI, while meniscal tear location increased the risk of FP lateral meniscus and FN medial meniscus tears.

Purpose: (I) Characterize the demographics and clinical features of patients with meniscal root tears (MRT); (II) analyze the morphology, extent, and grade of MRT on MRI; (III) evaluate associated abnormalities on imaging; and (IV) evaluate the associations between imaging findings, demographics, clinical features, and joint structural abnormalities.

Material and methods: A search was performed to identify meniscal root tears. Age, sex, BMI, and pain were recorded. Knee radiographs and MRI were reviewed. Presence, grade and morphology of MRT, meniscal extrusion, insufficiency fractures, as well as joint structural abnormalities were scored. For goals (I), (II), and (III), tabulations for categorical variables and mean for continuous variables were computed. MRT findings variables were described using percentages. For goal (IV), adjusted linear and logistic regression were employed.

Results: Ninety-six patients with a mean age of 56.6 years (69 females) and mean BMI of 28.9 kg/m² were included; 88 of the MRT were located at the posterior horn of the medial meniscus (PHMM), and 82% were radial tear. The mean tear diameter was 3.8 mm, and 78/96 tears presented with meniscal extrusion. Nineteen patients presented with subchondral insufficiency fracture (SIF), which was significantly associated with the gap of the tear (p = 0.001) and grade of the meniscal root lesion (p = 0.005).

Conclusion: MRT typically found in middle-aged to older overweight and obese women. Lesions were mostly radial tears and located at PHMM and were frequently associated with meniscal extrusion and SIF. Moreover, the presence of SIF was significantly associated with the gap width and grade of root tear.

Erdheim-Chester disease (ECD) is a rare clonal myeloid neoplasm typically affecting adults over 50 years old, with bone lesions in almost all patients. The prognosis is poor in most cases if left untreated. Clinical manifestations are not specific, which hinders early diagnosis. The disease has distinct radiological features. However, three-phase bone scintigraphy exhibits the most typical pattern of all imaging modalities, which is the prominent strikingly symmetrical radiotracer uptake in the distal ends of the femurs and proximal and distal ends of the tibiae, sparing the epiphyses. We report a case of a 54-year-old female patient, presenting with atypical persistent knee joint pain. After an MRI scan, she underwent a three-phase bone scan, revealing the characteristic pattern, thus indicating a possible ECD diagnosis, which was eventually confirmed in biopsy material. Novel aspects of the pathophysiology and treatment of the disease, as well as a differential diagnosis from the perspective of an MSK radiologist and nuclear medicine physician, are also discussed.

Objective: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).

Materials and methods: WBLDCTs of MM patients performed within a year of diagnosis were included. Cytogenetic assessments of clonal plasma cells via fluorescent in situ hybridization (FISH) were used to risk-stratify patients as high-risk (HR) or standard-risk (SR). Presence of any of del(17p), t(14;16), t(4;14), and t(14;20) on FISH was defined as HR. The dataset was evenly divided into five groups (folds) at the individual patient level for model training. Mean and standard deviation (SD) of the area under the receiver operating curve (AUROC) across the folds were recorded.

Results: One hundred fifty-one patients with MM were included in the study. The model performed best for t(4;14), mean (SD) AUROC of 0.874 (0.073). The lowest AUROC was observed for trisomies: AUROC of 0.717 (0.058). Two- and 5-year survival rates for HR cytogenetics were 87% and 71%, respectively, compared to 91% and 79% for SR cytogenetics. Survival predictions by the WBLDCT deep learning model revealed 2- and 5-year survival rates for patients with HR cytogenetics as 87% and 71%, respectively, compared to 92% and 81% for SR cytogenetics.

Conclusion: A deep learning model trained on WBLDCT scans predicted the presence of cytogenetic abnormalities used for risk stratification in MM. Assessment of the model's performance revealed good to excellent classification of the various cytogenetic abnormalities.

Objective: To compare radiographic measurements of lateral tibial slope (LTS), medial TS (MTS), and coronal TS (CTS) in MRI-defined intact, injured, and mucoid-degenerated native ACL knees and determine inter-reader reliability.

Materials and methods: Patient records from 2 years at tertiary care hospitals were reviewed for individuals aged 18-100 undergoing 3-Tesla knee MRI and radiographs. Two randomly selected cohorts, control, and pathologic ACLs on MRI with 86 patients each, were age, gender, and BMI-matched. A fellowship-trained musculoskeletal radiologist reevaluated curated images, characterizing ACL status. Two trained medical students independently collected clinical data and measured slopes on blinded radiographs. ICC, Cohen's kappa, and case-control matching were performed using SPSS statistical package, with ICC and ANOVA used for comparisons.

Results: Among 172 patients with 172 MRIs and radiographs, there were 86 controls and 86 ACL lesions. There were 108/172 (62.79%) males and 64/172 (37.21%) females. ICCs were 0.966 for MTS, 0.975 for LTS, and 0.978 for CTS. Mucoid degeneration patients had a higher BMI and were older than control (p < .05) or completely torn (p < .001) ACL patients. There was no difference in TS between normal and pathologic ACLs; however, LTS-MTS differences were larger with partial tears (2.5 ± 4.9) than normal ACLs by 4.5° (± 1.2, p < .001), complete tears by 4.5° (± 1.3, p < .001), and mucoid degeneration by 4.9° (± 1.5, p = .001).

Conclusion: Various TS measurements are reliable. LTS-MTS differences are associated with different ACL lesions compared to normal ACLs.

Purpose: Morphological magnetic resonance (MR) and computed tomography (CT) features are used in combination with histology for diagnosis and treatment selection of primary bone neoplasms. Isolated functional MRI parameters have shown potential in diagnosis. Our goal is to facilitate diagnosis of primary bone neoplasms of the skull base, mobile spine and sacrum, by a comprehensive approach, combining morphological and functional imaging parameters.

Materials and methods: Pre-treatment MR of 80 patients with histologically proven diagnosis of a primary bone neoplasm of the skull base, mobile spine and sacrum were retrospectively analyzed for morphological and functional MRI parameters. Functional parameters were measured in 4 circular regions of interest per tumor placed on non-adjacent scan slices. Differences in values of functional parameters between different histologies were analyzed with Dunn's test.

Results: Chordomas were the predominant histology (60.0%). Most neoplasms (80.0%) originated in the midline and had geographical (78.2%) bone destruction. Amorphous-type calcification (pre-existing bone) was seen only in chordomas. Homogeneous contrast enhancement pattern was seen only in chondrosarcoma and plasmacytoma. Ktrans and Kep were significantly lower in both chordoma, and chondrosarcoma compared to giant cell tumor of the bone (p = 0.006 - 0.011), and plasmacytoma (p = 0.004 - 0.014). Highest diffusion-weighted MRI apparent diffusion coefficient (ADC) values corresponded to chondrosarcoma and were significantly higher to those of chordoma (p = 0.008).

Conclusion: We identified the most discriminating morphological parameters and added functional MR parameters based on histopathological features that are useful in making a confident diagnosis of primary bone neoplasms in the skull base, mobile spine and sacrum.

Objectives: This study utilizes [^99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach.

Materials and methods: We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC).

Results: Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set.

Conclusions: Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.