Skeletal Radiology最新文献

Objective: To evaluate the cost-effectiveness of opportunistic CT for sarcopenia screening compared with standard-of-care clinical screening methods, using a decision-analytic model based on quality-adjusted life years (QALYs) and healthcare costs.

Materials and methods: We developed a decision-analytic model simulating a hypothetical cohort of 70-year-old male patients at risk for sarcopenia over a 3-year time horizon from a US healthcare system perspective. The model compared two screening strategies: standard-of-care clinical evaluation per EWGSOP2 guidelines (physical exam + DXA evaluation of lean mass) and opportunistic CT as measures of muscle mass and quality. Model inputs-including screening sensitivities/specificities, costs, utility values, and probabilities of cardiovascular complications-were derived from published literature. Incremental cost-effectiveness ratio (ICER) and net monetary benefit (NMB) were calculated, and sensitivity analyses were performed to assess the robustness of findings across variable inputs.

Results: Opportunistic CT was the favored strategy, with lower costs ($845 vs. $1,295), comparable effectiveness (0.87 QALYs), and higher net monetary benefit ($86,037 vs. $85,588) relative to the standard-of-care strategy. The standard-of-care strategy's ICER was $47.7 million per QALY, exceeding our willingness-to-pay threshold of $100,000. Probabilistic sensitivity analysis across 100,000 simulations demonstrated that opportunistic CT was favored across all tested willingness-to-pay thresholds up to $200,000.

Conclusion: Opportunistic CT is a cost-effective strategy for sarcopenia screening, offering similar effectiveness at a lower cost compared to the standard-of-care approach. By leveraging existing imaging studies, opportunistic CT screening has the potential to enhance early detection and decrease the underdiagnosis of sarcopenia while also reducing the burden of additional DXA scans and clinical visits.

We describe the case of an 11-year-old boy with hemophilia B who presented with swelling and pain in the left index finger. The patient was referred to our hospital after first presenting to another hospital. The patient experienced slight difficulty making a fist because of limited flexion of the proximal interphalangeal joint. Radiography of the left index finger revealed an expansile remodeling osteolytic lesion with a well-defined sclerotic border and cortical thinning. The patient had prolonged activated partial thromboplastin times and decreased factor IX levels. We performed curettage of the tumor in the left proximal phalanx and artificial bone filling. A hemophilic pseudotumor was diagnosed. At 1 year postoperatively, the patient was able to mobilize the index finger without range of motion limitations. Radiography of the left index finger revealed no signs of recurrence. Hemophilic pseudotumor usually occurs in patients with severe hemophilia A; however, this is the first reported case of a hemophilic pseudotumor arising from the finger of a child with mild hemophilia B. When progressive expansile remodeling of bone in the hand of a patient with hemophilia is observed, the possibility of a hemophilic pseudotumor, regardless of the severity or type of hemophilia, should be considered.

Soft tissue tumours (STTs) encompass a wide spectrum of benign, intermediate, and malignant lesions, often posing significant diagnostic and management challenges. Magnetic resonance imaging (MRI) offers excellent soft tissue characterization, but non-specific clinical presentation, overlapping MRI features, and wide histological variability of soft tissue tumours limit the predictive value of MRI. This review article presents a novel 8S framework that integrates MRI features of morphology, function, and biological aggressiveness to enhance diagnostic precision and risk stratification. The approach evaluates Site, Size, Shape/Surface, Signal, Surroundings, Sequence behaviour, Surgical assistance, and Satellites/Skips detailing tumour anatomy and internal structure while focusing on biological aggressiveness, incorporating dynamic contrast enhancement, diffusion characteristics, and patterns of local invasion as well as metastatic potential. The framework represents a clinically actionable MRI strategy. By aligning the 8S framework with the 2020 WHO soft tissue tumour classification, this approach enables differentiation of benign, locally aggressive, and high-grade malignant tumours, guiding biopsy, surgical planning, and prognosis.

Malignant aneurysmal bone cyst is not a well described concept. We present a case of a morphologically malignant bone tumour, interpreted as a malignant aneurysmal bone cyst with areas of characteristic morphology, a myofibroblastic phenotype, and a proven PAFAH1B1:USP6 fusion. The radiologic, morphologic and molecular features of this unusual case are discussed, along with the spectrum of atypical/malignant USP6 translocated tumours.

Knee MRI plays a central role in musculoskeletal diagnostics but has traditionally been associated with relatively long acquisition times. Recent technological advances have fundamentally changed this paradigm. Parallel imaging (PI), simultaneous multi-slice acquisition (SMS), compressed sensing (CS), and combinations thereof have substantially reduced scan times without compromising diagnostic image quality. The introduction of deep learning (DL)-based reconstruction further elevates this transformative breakthrough, as it can reconstruct high-quality diagnostic MR images at higher acceleration factors, where conventional image reconstruction methods have traditionally struggled to succeed. Sixfold PIxSMS-accelerated DL protocols have demonstrated excellent diagnostic performance and image quality, allowing comprehensive knee MRI examinations to be completed in under five minutes. Accelerated three-dimensional (3D) TSE techniques, such as CAIPIRINHA-accelerated SPACE sequences, further expand the potential of knee MRI by enabling high-resolution isotropic 3D imaging at acquisition times that are increasingly practical for routine clinical use. Ongoing improvements in DL-based reconstruction and denoising may soon bridge the remaining gap, promising to enable the acquisition of isotropic 3D datasets with multiple contrasts within minutes. Beyond technical acceleration, the successful implementation of fast MRI requires careful workflow optimization and consideration of architectural and economic factors. This review outlines the technical principles underlying modern acceleration strategies, summarizes evidence from validation studies, discusses practical aspects of clinical implementation and protocol optimization, and highlights future opportunities and challenges.

Objective: To develop and validate a deep learning model for the detection of aberrant anterior tibial artery (AATA) on axial T2-weighted knee MRI, given the surgical relevance of unrecognized AATA and the lack of automated detection tools.

Materials and methods: This retrospective study included 70,260 MRI images from 2315 examinations (1441 without AATA and 874 with AATA) collected after institutional review board approval. Musculoskeletal radiologists performed image-level annotations. Data were split at the patient level into training, validation, and internal test sets; an independent dataset from another institution served as an external test set. A convolutional neural network was implemented in Python and PyTorch. Model performance was assessed at the patient level.

Results: At the slice level, the model achieved an F1-score of 0.838 on the internal test set. Patient-level classification using the validation-derived threshold (0.17) yielded F1-scores of 0.966 on the validation set, 0.979 on the internal test set, and 0.786 on the external test set. The area under the receiver operating characteristic curve for the external cohort was 0.97, indicating strong generalization despite a decrease in precision due to false positives.

Conclusion: To our knowledge, this is the first study to apply artificial intelligence for automated detection of AATA on knee MRI. The proposed deep learning model performs this task with high sensitivity. Despite reduced precision in the external cohort, it demonstrates strong potential for enhancing preoperative risk assessment and surgical planning. Broader multicenter validation is warranted before clinical deployment.

Objectives: To assess the added value of two additional dedicated MRI sequences in the evaluation of the acromioclavicular ligament complex in patients with traumatic acromioclavicular joint injury.

Materials and methods: In this single-center study, shoulder MRIs of post-traumatic patients with suspected acromioclavicular joint dislocation were retrospectively evaluated twice by four readers of varying levels of expertise: once with the standard protocol and once with two additional dedicated acromioclavicular ligament sequences (a proton density-weighted sequence parallel to the acromioclavicular joint and a coronal oblique PD-weighted sequence, with a slice thickness of 2 mm). The acromioclavicular and the coracoclavicular ligaments, along with their different bundles, were analyzed. Intrareader reliability and interreader agreement were assessed by intraclass correlations. Associated shoulder lesions were reported.

Results: A total of 85 shoulder MRIs from patients with acute trauma were retrospectively analyzed (mean age 38.4 ± 13.6 years, male = 73 [85.9%]). Acromioclavicular dislocations were reported in 75 patients (88.2%). The intrareader reliability was good to excellent, ranging from 0.81 to 0.90. Overall, the interreader agreements were excellent (intraclass correlations = 0.93 and 0.94 at both four and six sequences, respectively). The less experienced reader agreed with the more experienced one with and without dedicated acromioclavicular sequences (significant equivalence: t[12] = 2.946, p = 0.006).

Conclusion: In this retrospective cohort, adding dedicated acromioclavicular joint sequences did not show evidence of improvement in the detection and the characterization of potential acromioclavicular ligament lesions, regardless of the reader's level of experience.

Objectives: The purpose of this study was to evaluate the effectiveness of collagen-sensitive dual-energy computed tomography (DECT) as a quantitative imaging tool for the assessment and monitoring of load-induced tendinopathy in the Achilles and patellar tendons, comparing it to magnetic resonance imaging (MRI).

Methods: In a prospective study, 15 consecutive patients clinically diagnosed with Achilles or patellar tendinopathy underwent bilateral DECT and MRI at baseline and 6 months. Quantitative measurements included collagen density assessed via DECT and signal intensity via MRI. Clinical symptoms were evaluated using numerical pain ratings and VISA-A/P scores. The diagnostic accuracy of both imaging modalities was assessed using ROC analysis, and correlations between DECT and MRI findings were investigated.

Results: DECT revealed significantly lower collagen densities on corresponding maps in affected tendons (n = 18, 23.7 ± 20.2) compared to unaffected tendons (n = 12, 60.2 ± 29.6 HU, p < 0.001), whereas MRI demonstrated increased signal intensities in pathological regions. ROC analysis indicated comparable diagnostic performance for DECT (AUC = 0.84) and MRI (AUC = 0.80). A strong inverse correlation (r = -0.83) was observed between DECT-measured collagen densities and MRI signal intensities. Clinical improvements at follow-up were reflected by normalization trends in both imaging modalities, though not statistically significant.

Conclusions: Collagen-sensitive DECT provides a reliable quantitative approach for detecting and assessing tendon pathologies in load-induced tendinopathy, demonstrating diagnostic capabilities comparable to MRI while offering the possibility for collagen density quantification.