The Management of Sacral Schwannoma: Report of Four Cases and Review of Literature

Sacral schwannoma is a rare retrorectal tumor in adults. Postoperative sacral neurological deficit is difficult to avoid. Currently, there is no established consensus regarding best treatment options. We present the management and outcomes of sacral schwannoma in 4 patients treated with intralesional curettage and postoperative radiation. There were 3 women and one man (average age: 45.5 years) with long duration of lumbosacral pain with or without radiculopathy. Intralesional curettage was performed by posterior approach and adjuvant radiation therapy with dosage of 5000–6600 cGy was given after surgery. The mean follow-up time was 18 months (range 4–23 months). Symptoms of radiculopathy had decreased in all patients. The recent radiographic findings show evidence of sclerosis at the sacrum one year postoperatively, but the size was unchanged. Intralesional curettage and adjuvant radiation therapy can be used in the treatment of sacral schwannoma to relieve symptoms and preserve neurological function

Effects of Different Durations of 9-Square Dance Exercise Versus Treadmill Exercise on the Physical Fitness and Quality of Life of Healthy Volunteers: A Pilot Randomized Controlled Trial

Objective: To evaluate the impact of 9-square dance exercise (9SDE) on physical fitness and quality of life compared to traditional treadmill exercise (TME). Materials and Methods: In total, 33 healthy volunteers (10 men, 23 women) were recruited and randomly assigned into three groups: 9 square dance exercise for 8 minutes (9SDE-8), 9 square dance exercise for 30 minutes (9SDE-30), or treadmill exercise (TME). Exercises were done three times a week for 12 weeks and physical fitness tests were performed for all the groups at weeks 0, 6, and 12. Participants were assessed using the European Quality of Life Measure 5 Domains and 5 Levels questionnaire (EQ-5D-5L). Results: Significant improvements in cardiorespiratory endurance, leg strength, and flexibility were demonstrated in the 9SDE-30 group (p<0.05). There was no significant difference in physical fitness between the 9SDE-30 and TME groups. The 9SDE-8 group showed a significant improvement in utility in the EQ-5D-5L questionnaire (p<0.05), while the TME group showed a significant improvement in directly evaluated health status (p<0.05). 9SDE-30 and TME showed similar improvements in cardiorespiratory endurance and leg strength. Conclusion: Considering its low-resource requirement and overall utility, coupled with its effectiveness in promoting cardiovascular fitness and leg strength, 9SDE represents a viable exercise alternative for those with limited time and resources

Three-dimensional Kinematic Analysis and Muscle Activation of the Upper Extremity in Ruesi Dutton Exercises

Objective: To investigate 3-D upper extremity joint angles and muscle activities in selected Ruesi-Dutton exercises. Material and Methods: Twenty-six healthy participants (mean age of 25.65, mean height of 165.08 cm, and mean weight of 56.69 Kg) volunteered to take part in this study. 3-D motion analysis consisted of eight cameras synchronized with a wireless electromyography (EMG) system to collect kinematic data and muscle activity. Participants performed five postures, including the Kae Lom Kho Mue posture, Kae Puat Thong Kae Kho Thao posture, Kae Kiat posture, Kae Puat Thong Sabak Chom posture, and Kae Lom Puat Sisa. The upper extremity joint angles and range of motion (ROM) and EMG were analyzed. Results: Most postures were in the normal range of motion. The percentage of MVIC was more than 1% and the Trapezius muscle is the most active in all postures. Conclusion: The data in this research is useful to help select the correct posture and exercise for a specific condition

The prognostic factors of recurrent GCT: A cooperative study by the Eastern Asian Musculoskeletal Oncology Group

Background Giant-cell tumor (GCT) of bone is a common primary benign tumor with high local recurrence and potential distant metastasis or malignant transformation. We haveinvestigated the clinical behavior of recurrent GCT of bone in the extremities. Methods We retrospectively reviewed 110 patients with recurrent GCTs of bone in the extremities treated by the Eastern Asian Musculoskeletal Oncology Group. The factors that affected the number of recurrences and distant metastasis were analyzed. Results The median interval between initial surgery and the first recurrence of GCTwas 16 months (2-180 months). All patients received additional surgery for first recurrence. Twenty-five patients had a second recurrence and 6 patients had a third recurrence. The mean interval between theinitial surgery and the first recurrence correlated withthe eventual number of recurrences-14.1 months for the repeated recurrence groups (two and three recurrences) and 28.3 months for the single recurrence group (p = 0.016). Campanacci grade did not correlate with repeated recurrence (p = 0. 446). The venue of the initial surgery did not correlate with recurrence but did affect preservation of the adjacent joint (chi-squared test; p =0.046). Campanacci grade II and III also correlated withsacrifice of the adjacent joint (p = 0.020). The incidence of lung metastasis and malignant transformation were 7.5% (8 out of 107 patients) and 2.7% (3 out of 110 patients), respectively. Repeat recurrence was associated with lung metastasis (p = 0.018). © The Japanese Orthopaedic Association 2011

Experience of total scapular excision for musculoskeletal tumor and reconstruction in eastern Asian countries

Total scapulectomy and reconstruction has been performed for scapular tumor, however, most of the reconstruction methods have resulted in poor functional outcomes and there is still room for improvement. Most of the reports of reconstruction after scapulectomy are from a single institution. In the present study, we investigated functional outcomes after total scapulectomy in a multicenter study in The Eastern Asian Musculoskeletal Oncology Group (EAMOG). Thirty-three patients who underwent total scapulectomy were registered at EAMOG affiliated hospitals. The patients were separated into no reconstruction group (n=8), humeral suspension group (n=15) and prosthesis group (n=10). Functional outcome was assessed by the Enneking score. One-way ANOVA was used to compare parameters between the patient groups. Complications included five local recurrences, one superficial infection, one dislocation and one clavicle protrusion. The average follow-up period was 43.5. months. The average active flexion range was 45.8° (0-120°), and 37.1° in abduction (0-120°). The mean total functional score was 22.9 out of 30 (15-29), which is a satisfactory score following resection of the shoulder girdle. There were significant differences in reconstruction methods for active range of motion. Bony reconstruction provided better range of motion in this study. There was a variety of reconstruction methods after scapulectomy in the eastern Asian countries. Although better functional score was obtained using scapular prosthesis or recycled bone and prosthesis composite grafting, postoperative function is still lower than preoperative function. Modified designed prosthesis with or without combination of recycle bone or allograft would restore the lost shoulder function in the future. © 2016 The Authors.Embargo Period 12 month

Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors

This retrospective study aimed to compare the intra- and inter-observer manual-segmentation variability in the feature reproducibility between two-dimensional (2D) and three-dimensional (3D) magnetic-resonance imaging (MRI)-based radiomic features. The study included patients with lipomatous soft-tissue tumors that were diagnosed with histopathology and underwent MRI scans. Tumor segmentation based on the 2D and 3D MRI images was performed by two observers to assess the intra- and inter-observer variability. In both the 2D and the 3D segmentations, the radiomic features were extracted from the normalized images. Regarding the stability of the features, the intraclass correlation coefficient (ICC) was used to evaluate the intra- and inter-observer segmentation variability. Features with ICC > 0.75 were considered reproducible. The degree of feature robustness was classified as low, moderate, or high. Additionally, we compared the efficacy of 2D and 3D contour-focused segmentation in terms of the effects of the stable feature rate, sensitivity, specificity, and diagnostic accuracy of machine learning on the reproducible features. In total, 93 and 107 features were extracted from the 2D and 3D images, respectively. Only 35 features from the 2D images and 63 features from the 3D images were reproducible. The stable feature rate for the 3D segmentation was more significant than for the 2D segmentation (58.9% vs. 37.6%, p = 0.002). The majority of the features for the 3D segmentation had moderate-to-high robustness, while 40.9% of the features for the 2D segmentation had low robustness. The diagnostic accuracy of the machine-learning model for the 2D segmentation was close to that for the 3D segmentation (88% vs. 90%). In both the 2D and the 3D segmentation, the specificity values were equal to 100%. However, the sensitivity for the 2D segmentation was lower than for the 3D segmentation (75% vs. 83%). For the 2D + 3D radiomic features, the model achieved a diagnostic accuracy of 87% (sensitivity, 100%, and specificity, 80%). Both 2D and 3D MRI-based radiomic features of lipomatous soft-tissue tumors are reproducible. With a higher stable feature rate, 3D contour-focused segmentation should be selected for the feature-extraction process

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Abstract Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance