Osteoarthritis (OA) restricts the daily activities of patients and significantly decreases their quality of life. The development of noninvasive quantitative methods for properly diagnosing and evaluating the process of degeneration of articular cartilage due to OA is essential. Second harmonic generation (SHG) imaging enables the observation of collagen fibrils in live tissues or organs without staining. In the present study, we employed SHG imaging of the articular cartilage in OA model mice ex vivo. Consequently, three-dimensional SHG imaging with successive image processing and statistical analyses allowed us to successfully characterize histopathological changes in the articular cartilage consistently confirmed on histological analyses. The quantitative SHG imaging technique presented in this study constitutes a diagnostic application of this technology in the setting of OA.

Osteoarthritis (OA) is a chronic joint disorder involving degeneration of articular cartilage and subchondral bone in joints. We previously established a second harmonic generation (SHG) imaging technique for evaluating degenerative changes to articular cartilage in an OA mouse model. SHG imaging, an optical label-free technique, enabled observation of collagen fibrils, and characterized critical changes in the collagenous patterns of the joints. However, it still remains to be determined how morphological changes in the organization of tissue collagen fibrils should be quantified. In this study, we addressed this issue by employing an approach based on texture analysis. Image texture analysis using the gray level co-occurrence matrix was explored to extract image features. We investigated an image patch-based strategy, in which texture features were extracted on individual patches derived from original images to capture local structural patterns in them. We verified that this analysis enables discrimination of cartilaginous and osseous tissues in mouse joints. Moreover, we applied this method to OA cartilage pathology assessment, and observed improvements in the performance results compared with those obtained using an existing feature descriptor. The proposed approach can be applied to a wide range of conditions associated with collagen remodeling and diseases of cartilage and bone.

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BackgroundSynovial sarcoma is a relatively rare type of soft tissue sarcoma. The commonly observed symptom is a deep-seated palpable mass accompanied by pain or tenderness. Thus, it is considered a soft tissue sarcoma and rarely occurs primarily in bone. However, only few studies have been reported on intraosseous synovial sarcoma, and reports on cases with cytogenetic or molecular confirmation are even rarer. We report a case of intraosseous synovial sarcoma of the distal ulna that has been confirmed using histopathological examination and molecular analysis.Case presentationA 77-year-old female was referred to our hospital with a 1-month history of right wrist pain after housework. Clinical and imaging findings suggested a benign bone tumor that was enhanced by Gd-DTPA. It was thought that the tumor was possibly an enchondroma. Initially, we planned to evaluate the benignancy of the tumor with intraoperative frozen section, followed by curettage and bone graft at one stage However, when considering carefully, characteristics of the tumor did not perfectly match those of any diagnostic categories including enchondroma. Therefore, an incisional biopsy was performed and revealed that the tumor was synovial sarcoma. Following an elaborate plan, the patient underwent a wide resection of the tumor at the distal part of the right ulna. Reverse transcription-polymerase chain reaction (RT-PCR) from the resected specimen and sequencing of RT-PCR products demonstrated a chimeric SYT-SSX1 transcript, confirming the diagnosis of synovial sarcoma.ConclusionsSynovial sarcoma is seldom considered in differential diagnosis of bone tumors because it is difficult to line up such an unusual diagnosis as a differential diagnosis. When the lesion does not perfectly fit into any diagnostic category, when the initial image diagnosis appears unconvincing, biopsy and pathology are indicated, recalling Jaffe’s triangle. According to these diagnostic processes, the patient successfully completed the treatment for this rare intraosseous synovial sarcoma, following a careful plan based on the preoperative diagnosis.

Significance: Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients' background.Aim: To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.Approach: Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.Results: Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin-O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients. Conclusions:The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual's OA by evaluating the cartilage quality.