Search

Aims/Background Generally, pelvic osteosarcoma has a worse prognosis compared with limb osteosarcoma. This study aims to create and validate a new nomogram for predicting the prognosis of pelvic osteosarcoma. Methods Clinical data of 62 patients derived from the Surveillance, Epidemiology, and End Results (SEER) database and 31 Chinese patients diagnosed with pelvic osteosarcoma were gathered. Kaplan-Meier survival analysis was utilized to calculate the median survival time for all variables. Univariate and multivariate Cox regression models were employed to identify the prognostic factors of pelvic osteosarcoma. A nomogram was constructed using data gleaned from the SEER cohort and verified using the receiver operating characteristic (ROC) curve and calibration plot in the Chinese cohort. Results Kaplan-Meier analysis revealed that individuals of other races (Asians) (hazard ratio (HR) = 0.24, 95% confidence interval (CI): 0.1–0.57, p = 0.001), aged ≤51 years old (HR = 0.4, 95% CI: 0.22–0.73, p = 0.003), and with tumor size ≤160 mm (HR = 0.37, 95% CI: 0.2–0.71, p = 0.03) had better survival outcomes. Conversely, factors such as no primary surgery (HR = 3.6, 95% CI: 1.81–7.15, p < 0.001), lung metastasis (HR = 1.96, 95% CI: 1.17–3.28, p = 0.010), and radiotherapy (HR = 1.89, 95% CI: 1.10–3.25, p = 0.021) were associated with poorer survival. Multivariate Cox analysis indicated that lung metastasis (HR = 2.57, 95% CI: 1.29–5.13, p = 0.008), other races (Asians) (HR = 0.23, 95% CI: 0.07–0.75, p = 0.015), tumor size (HR = 0.28, 95% CI: 0.13–0.62, p = 0.001) and age (HR = 0.3, 95% CI: 0.16–0.59, p < 0.001) were independent prognostic factors for pelvic osteosarcoma. Univariate and multivariate Cox regression models identified three independent variables in the training cohort: age, lung metastasis, and tumor size. A predictive nomogram was developed based on the data from the SEER cohort and validated in the Chinese cohort. The areas under the curves (AUCs) that are used to predict 1-year, 2-year, and 3-year survival rates were 0.81 (95% CI: 0.68–0.94), 0.75 (95% CI: 0.63–0.86), and 0.80 (95% CI: 0.70–0.89) in the training cohort, and 0.67 (95% CI: 0.30–1.04), 0.66 (95% CI: 0.43–0.90) and 0.71 (95% CI: 0.50–0.93) in the validation cohort. Conclusion The predictive nomogram constructed in this study facilitates accurate and effective prediction of the overall survival of patients with pelvic osteosarcoma and helps enhance the clinical decision-making process. [ABSTRACT FROM AUTHOR]

Surgeries of pelvic bone tumors are very challenging due to the complexity of anatomical structures and the irregular bone shape. CT and MRI are used in clinic for tumor evaluation, each with its own advantages and shortcomings. Combining the data of both CT and MRI images would take advantage of the merits of both images and provide better model for preoperative evaluation. We utilized an artificial intelligence (AI)-assisted CT/MRI image fusion technique and built a personalized 3-D model for preoperative tumor margin assessment. A young female patient with pelvic osteosarcoma was evaluated with our novel image fusion 3-D model in comparison with the 3-D model based solely on CT images. The fusion image model showed more detailed anatomical information and discovered multiple emboli within veins which were previously neglected. The discovery of emboli implied abysmal prognosis and discouraged any attempts for complex reconstruction after tumor resection. Based on the experience with this pelvic osteosarcoma, we believe that our image fusion model can be very informative with bone tumors. Though further validation with a large number of clinical cases is required, we propose that our model has the potential to benefit the clinic in the preoperative evaluation of bone tumors.

Surgeries of pelvic bone tumors are very challenging due to the complexity of anatomical structures and the irregular bone shape. CT and MRI are used in clinic for tumor evaluation, each with its own advantages and shortcomings. Combining the data of both CT and MRI images would take advantage of the merits of both images and provide better model for preoperative evaluation. We utilized an artificial intelligence (AI)-assisted CT/MRI image fusion technique and built a personalized 3-D model for preoperative tumor margin assessment. A young female patient with pelvic osteosarcoma was evaluated with our novel image fusion 3-D model in comparison with the 3-D model based solely on CT images. The fusion image model showed more detailed anatomical information and discovered multiple emboli within veins which were previously neglected. The discovery of emboli implied abysmal prognosis and discouraged any attempts for complex reconstruction after tumor resection. Based on the experience with this pelvic osteosarcoma, we believe that our image fusion model can be very informative with bone tumors. Though further validation with a large number of clinical cases is required, we propose that our model has the potential to benefit the clinic in the preoperative evaluation of bone tumors. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the frequency of tumor thrombus in the large veins draining primary pelvic osteosarcoma on early cross-sectional imaging studies and its effect on patient survival.Materials and Methods: Our retrospective study included all patients with primary pelvic osteosarcoma treated at our facility between January 2000 and May 2014, who were ≤ 45 years of age, and had adequate imaging studies and clinical follow up. Four radiologists evaluated for tumor in the large draining veins on initial CT, MRI and PET/CTs. A consensus evaluation by the four radiologists together with findings on operative reports, pathology reports or follow-up imaging was used as the reference standard.Results: Thirty-nine patients with primary pelvic osteosarcoma met final inclusion criteria. Tumor thrombus was identified in the large draining veins in 10 of the 22 (45%) patients who underwent tumor resection and 10 of the 17 (59%) who did not. In the 22 patients who underwent tumor resection, tumor thrombus was significantly associated with worse overall survival (p = 0.03).Conclusions: Tumor thrombus in the large draining veins is identified in a significant proportion of initial imaging studies in patients with pelvic osteosarcoma, and is associated with worse overall survival in patients who undergo tumor resection. [ABSTRACT FROM AUTHOR]

Simple Summary: Pelvic osteosarcoma has a poor prognosis compared to osteosarcomas in other locations, and the reasons for this remains unknown. Surgical resection of pelvic osteosarcoma is technically demanding and often results in dysfunction and complications. In this study, we investigated the reasons underlying the poor prognosis of pelvic osteosarcoma by comparing it to femoral osteosarcoma using data from the Bone Tumor Registry in Japan. We used propensity score analysis to determine whether surgical resection of pelvic osteosarcoma improved its prognosis. We demonstrated that pelvic osteosarcoma had a poor prognosis because it occurred more often in the elderly, often had a larger tumor size, and had metastasis at presentation more often in comparison to femoral osteosarcoma. These three factors were also associated with the non-surgical treatment of pelvic osteosarcoma, which also led to a poor outcome. The overall survival rate was only comparable in pelvic osteosarcoma and femoral osteosarcoma in cases treated with surgical resection. Propensity score analysis revealed that surgical treatment improved the prognosis of pelvic osteosarcoma. As such, we propose that surgical resection should be considered based on tumor stage and patient age in order to improve the prognosis of pelvic osteosarcoma. Pelvic osteosarcoma has a poor prognosis compared to osteosarcomas in other locations, and the reasons for this remain unknown. Surgical resection of pelvic osteosarcoma is technically demanding and often results in dysfunction and complications. In this study, we investigated the reasons underlying the poor prognosis of pelvic osteosarcoma by comparing it to femoral osteosarcoma using data from the Bone Tumor Registry in Japan. We used propensity score analysis to determine whether surgical resection of pelvic osteosarcoma improved its prognosis. We demonstrated that pelvic osteosarcoma had a poor prognosis because it occurred more often in the elderly, often had larger tumor size, and had metastasis at presentation more often in comparison to femoral osteosarcoma. These three factors were also associated with the non-surgical treatment of pelvic osteosarcoma, which also led to a poor outcome. The overall survival rate was only comparable in pelvic osteosarcoma and femoral osteosarcoma in cases treated with surgical resection. Propensity score analysis revealed that surgical treatment improved the prognosis of pelvic osteosarcoma. As such, we propose that surgical resection should be considered based on tumor stage and patient age in order to improve the prognosis of pelvic osteosarcoma. [ABSTRACT FROM AUTHOR]