We retrospectively studied the functional and oncological results of 15 patients after reconstruction of the distal radius with osteoarticular allograft or non-vascularised fibular graft following wide excision of an aggressive benign or malignant tumour. Eight patients underwent osteoarticular allograft and seven patients had a non-vascularised autogenous fibular graft reconstruction. The average time for incorporation of the graft was 6 and 5 months in each reconstruction respectively. There was no tumour recurrence after follow up over 41.5-95.5 (average 60.5) months. All patients had good and excellent functional results. Three patients in the group reconstructed with osteoarticular allograft had plate loosening and graft fractures which were successfully treated subsequently.

One of the major failure modes of cementless acetabular components is the loosening of the acetabular cup, which is mostly attributable to insufficient initial stability. A hemispherical cup with a porous coating which is inserted with press-fit fixation and secured with several screws is one of the most widely used approaches. Many studies have found that bone screws are very helpful aids for cup fixation, but the optimal surgical technique for inserting screws has not been clearly reported. In this study, hemispherical cups were fixed into blocks of foam bone with zero to three screws. The effects of three types of screw eccentricity (a 1-mm offset and angular eccentricities of 15°and 25°) on the initial stability of the acetabular cup were evaluated. The experimental results indicate that increasing the number of screws enhances the cup stability in the case of ideal screwing (i.e., with no eccentricity). An angular eccentricity of 15°did not affect the cup stability for fixation with one or two screws. However, the presence of 25°of angular eccentricity significantly reduced the stability of the cup, while 1 mm of offset eccentricity produced an even greater impact. Résumé Les modes les plus fréquents d'échecs des prothèses sans ciment sont le descellement de la cupule acétabulaire. Ce descellement est souvent en relation avec une stabilité initiale insuffisante. La cupule hémisphérique avec revêtement mis en place en press-fit avec vis additionnelles est un des modes opératoires les plus utilisés. De nombreuses études ont montré que l'utilisation de vis permettait de sécuriser la fixation. Pour cette étude, nous avons fixé des cupules hémisphériques dans un bloc d'os artificiel avec 0 à trois vis. Les effets de trois types de vissage ont permis d'évaluer la stabilité initiale de la cupule. Cette étude expérimentale montre que plus les vis sont nombreuses, meilleure est la stabilité. Une fixation excentrique de la cupule à 15°, stabilisée par une à deux vis n'a pas de conséquences néfastes, cependant une excentricité de 25°diminue de façon significative la stabilité de la cupule, de même, en ce qui concerne l'offset avec excentration de 1 mm.

We examined the mechanical consequences of high partial transverse sacrectomy. Ten human cadaveric pelves were randomly assigned to three groups. In the Control Group, the sacrum was left entirely intact. In Group I, transverse partial sacrectomy was performed just caudal to the S1 neural foramina. In Group II, transverse partial sacrectomy was performed just cephalad to the S1 neural foramina. Each pelvis was mounted on a testing apparatus and loaded vertically at the L4/L5 disk space until failure occurred. The average resection of the sacroiliac joints was 16% in Group I, and 25% in Group II. The average load to failure was 3014 N in the Control Group, 2166 N in Group I, and 1045 N in Group II. The average stiffness was 353 N/mm in the Control Group, 222 N/mm in Group I, and 100 N/mm in Group II. All specimens failed because of fractures through the sacrum (mostly Denis Zone II) in the sagittal plane. Using the literature to predict normal forces at the lumbosacral junction, we suggest Group I pelves could withstand postoperative mobilization without fracture, whereas Group II would probably not. Reconstruction should therefore be considered when performing transverse partial sacrectomy above the S1 nerve root.

This study was carried out to discover the prevalence, characteristics and severity of neuropathic pain after wide resection of chordoma of the sacrum by the use of posterior approach. Patients who had chordoma of their sacrums and underwent wide resection via posterior approach, during 1990-2002, were followed up as a prospective cohort. Pain assessment was carried out in terms of onset, characteristics, intensity (numerical rating scale), response to pain medication and associated symptoms. The correlation between patients' biographic data, preoperative neuropathic pain, type and levels of surgery and pain were analyzed. There were 21 patients; 14 male and 7 female patients. Their ages ranged between 29 and 75 years. Subtotal sacrectomy was carried out in 9 patients and total sacrectomy was carried out in 12 patients. All patients survived the operation. Neuropathic pain was found in 11 patients (52.4%). Male patients and presentation of preoperative neuropathic pain were significantly related to postoperative neuropathic pain. The other factors were not related to the postoperative pain. Recurrent of severe pain with different characteristics after the operation might indicate tumor recurrent. Early detection of the pain and proper treatment could minimize pain intensity and improved pain management satisfaction.

Use of warm Ringer's lactate solution as an adjunctive local treatment during intra-lesional curettage of giant cell tumor with locally soft tissue extension was found to be safe with relatively low recurrence rate. However, additional studies to identify the optimum thermoablation dose at each part of the body should be undertaken before this technique can be used as a standard treatment.

Highlights Customized, single-piece, 3D-printed, titanium phalangeal prosthesis of the 5th toe. Replacement of whole proximal phalanx with a mobile joint distally and proximally. Patient walks with full weight-bearing, no pain, and no recurrence or metastasis. Overriding toe occurred after two years due to scar contracture. Prosthesis design development, including size reduction, may improve outcomes.

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.

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.