Objective: This study aims to assess the CT and MRI features of head and neck osteosarcoma (HNO). Methods: 37 HNOs were identified, and the following imaging characteristics were reviewed on CT and MRI. Results: A total of 37 patients(age 41.5 ± 15.0 years old; 16 males, 21 females) were included in the study. Tumours occurred in the maxilla (16, 43.2%), mandible (8, 21.6%), skull base (6, 16.2%), calvarium (5, 13.5%), paranasal sinuses (1, 2.7%) and cervical soft tissue (1, 2.7%). 16 patients received radiotherapy for nasopharyngeal carcinoma. Three patients (8.1%) developed osteosarcomas related to a primary bone disease. 16 of the (43.2%) tumours demonstrated lytic density on CT scans, followed by 13 (35.1%) showing mixed density and 7 (18.9%) with sclerotic density. Matrix mineralization was present in 32 (86.5%). 3 out of 24 (12.5%) tumours showed lamellar periosteal reactions, 21 out of 24 (87.5%) showed spiculated periosteal reactions. 12 tumours showed low signal intensities on T₁WI, with 16 having heterogeneous signal intensities. 10 tumours showed high signal intensities on T₂WI, and 18 showed heterogeneous signal intensities. With contrast-enhanced images, 3 tumours showed homogeneous enhancement (2 osteoblastic and 1 giant cell-rich), 18 tumours showed heterogeneous enhancement (13 osteoblastic, 4 fibroblastic and 1 giant cell-rich), and 7 tumours showed peripheral enhancement (6 chondroblastic and 1 osteoblastic). These tumours were characterized by soft tissue masses with a diameter of 5.6 ± 1.8 cm. Conclusions: HNO is a rare condition and is commonly associated with previous radiation exposure. This study provides age, sex distribution, location, CT and MRI features of HNO.

Objective: This study aims to assess the CT and MRI features of calvarium and skull base osteosarcoma (CSBO). Methods: The CT and MRI features and pathological characteristics of 12 cases of pathologically confirmed CSBO were analyzed retrospectively. Results: 12 patients (age range 9–67 years; 3 male, 9 female) were included in the study. Tumours occurred in skull base (7, 58.3%), temporal (4, 33.3%) and frontal (1, 8.3%). Among all, six patients received radiotherapy for nasopharyngeal carcinoma. According to pathology, 11 out of 12 tumours were high-grade (91.7%). On CT, all the tumours had soft tissue mass penetrated into cortical bone with invasion of surrounding soft tissue. Six tumours were shown to have lytic density and six were mixed density. Matrix mineralization was present in 10 cases (83.3%). On MRI, tumours presented as soft-tissue masses measuring 5.9 ± 2.4 (3.9–8.0) cm. Five tumours showed low signal intensities on T 1 weighted imaging with seven having heterogeneous signal intensities. One showed low signal intensity on T 2 weighted imaging, two showed high signal intensities and nine heterogeneous signal intensities. All the tumours showed low signal intensities on diffusion-weighted imaging. On contrast enhanced images, seven cases showed heterogeneous enhancement, three showed peripheral enhancementand and two showed homogeneous enhancement. Dural tail sign were detected in nine cases. Conclusion: CSBO is rare, and is commonly associated with previous radiation exposure. A presumptive diagnosis for osteosarcoma should be considered when calvarium and skull base tumours with osteoid matrix and duraltail sign are found. Advances in knowledge: CT and MR features of CSBO have not been reported. The study helps to identify CSBO and other sarcomas.

Osteosarcoma is a highly malignant bone tumor with a high incidence of lung metastases (LM), significantly impacting the 5-year survival rate of patients. This study aims to predict lung metastasis in osteosarcoma based on computed tomography angiography (CTA) signs. A retrospective study was conducted involving 89 consecutive patients with osteosarcoma. Clinical features and CTA signs, including age, gender, laterality, primary site, type of bone destruction, T stage, periosteal reaction, tumor length, bone marrow involved length, vascular wrapping, and intratumoral vascular network, were evaluated. Univariate and multivariate logistic regression analyses were used to identify risk factors for LM, followed by receiver operating characteristic (ROC) curve analysis. The vascular wrapping and intratumoral vascular network signs were more frequently observed in LM in patients with osteosarcoma (P < 0.05). The intratumoral vascular network remained an independent risk factor in multivariable regression analysis. ROC curve analysis demonstrated that the area under the curve (AUC) of the logistic regression model was 0.804, indicating good predictive accuracy. Preliminary findings suggest that CTA signs, particularly vascular wrapping and the intratumoral vascular network, may have potential utility in predicting lung metastasis (LM) in osteosarcoma patients. The intratumoral vascular network, in particular, was identified as an independent risk factor.

Background: A timely assessment of local recurrence (LoR) risk in extremity high-grade osteosarcoma is crucial for optimizing treatment strategies and improving patient outcomes. Purpose: To explore the potential of machine-learning algorithms in predicting LoR in patients with osteosarcoma. Material and Methods: Data from patients with high-grade osteosarcoma who underwent preoperative radiograph and multiparametric magnetic resonance imaging (MRI) were collected. Machine-learning models were developed and trained on this dataset to predict LoR. The study involved selecting relevant features, training the models, and evaluating their performance using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). DeLong's test was utilized for comparing the AUCs. Results: The performance (AUC, sensitivity, specificity, and accuracy) of four classifiers (random forest [RF], support vector machine, logistic regression, and extreme gradient boosting) using radiograph-MRI as image inputs were stable (all Hosmer-Lemeshow index >0.05) with the fair to good prognosis efficacy. The RF classifier using radiograph-MRI features as training inputs exhibited better performance (AUC = 0.806, 0.868) than that using MRI only (AUC = 0.774, 0.771) and radiograph only (AUC = 0.613 and 0.627) in the training and testing sets ( P <0.05) while the other three classifiers showed no difference between MRI-only and radiograph-MRI models. Conclusion: This study provides valuable insights into the use of machine learning for predicting LoR in osteosarcoma patients. These findings emphasize the potential of integrating radiomics data with algorithms to improve prognostic assessments.