Characteristics and prognosis of telangiectatic osteosarcoma: a population-based study using the Surveillance, Epidemiology and End Results (SEER) database

Xu, Mingfang; Dai, Nan; Yang, Xueqin; Guan, Wei; Pan, Yu; Wang, Dong; Liang, Yin; Nie, Mao

doi:10.21037/atm-20-8001

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…TOS is a high-grade osteosarcoma that accounts for only a small proportion of all osteosarcoma cases; therefore, animal studies and case reports are the primary sources of information regarding this disease ( 11 ). The prognosis of TOS is poor, but it has recently improved with the introduction of adjuvant chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Prognostic analysis of telangiectatic osteosarcoma of the extremities

et al. 2023

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Background and objectivesTelangiectatic osteosarcoma (TOS) is a rare but highly malignant subtype of osteosarcoma. Although surgical treatment is the primary treatment modality for osteosarcoma, evidence on the benefits of different surgical methods in patients with TOS is lacking. This study aimed to compare the effects of different surgical and adjuvant treatments on overall survival of TOS, and the association of patient demographics, oncological characteristics, and socioeconomic status on treatment outcomes.MethodThis retrospective study selected the most common TOS cases of the extremities registered in the Surveillance, Epidemiology, and End Results (SEER) database from 1989 to 2019. Univariate and multivariate Cox regression models were used to analyze all prognostic factors, and Kaplan-Meier analyses were performed for disease-specific treatment factors of survival.ResultA total of 127 patients were included in the analysis. The average age at initial diagnosis was 20.09 years. In univariate analyses, the absence of metastasis at initial diagnosis, limb-salvage surgery, adjuvant chemotherapy, and no regional lymph node dissection were associated with a lower risk of death. Multivariate analysis further showed that the presence or absence of distant metastasis and regional lymph node dissection, implementation of adjuvant chemotherapy, and choice of surgical method were independent predictors of prognosis.ConclusionDistant metastasis and regional lymph node dissection are associated with poorer outcomes in TOS, and amputation has no better prognosis than limb salvage surgery. Compared with conventional chemotherapy, neoadjuvant chemotherapy did not significantly improve the prognosis of TOS.

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Section: Discussionmentioning

confidence: 99%

Prognostic analysis of telangiectatic osteosarcoma of the extremities

et al. 2023

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“…It is an imperative to develop an accurate survival prediction model that influences the decisions of clinicians, patients, and their families [6]. At present, most models for predicting osteosarcoma are Cox proportional hazard regression models combined with nomograms [3,[7][8][9][10][11][12][13]. The Cox models need to meet the proportional hazards assumption, thus the overall quality of these models may have not reached the optimal state.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

et al. 2023

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Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China.

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“…Previous studies have focused on various prognostic factors, such as age (7,8), tumor size and site (9), extent of disease (9,10), tumor grade (10), postchemotherapeutic necrosis rate/Huvos classification (7), pathological fracture (11) and therapeutic methods (3,5); some recently reported techniques like radiomics have also revealed their prognostic significance with satisfactory accuracy in osteosarcoma (12). However, due to the low incidence (4 to 6 cases per million worldwide) of osteosarcoma (1), existing studies have been often limited to small sample sizes, which may lead to inevitable overfitting; and survival prediction models for osteosarcoma are relatively rare (13)(14)(15). Thus, a large cohort of patients and an integration of prognostic factors can be helpful in establishing a statistical prediction model.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an online prognostic nomogram for osteosarcoma after surgery: a retrospective study based on the SEER database and external validation with single-center data

Feng¹,

Chen²,

Ye³

et al. 2022

Transl Cancer Res TCR

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Background: Osteosarcoma is a severe malignancy with relatively low morbidity and significant variation in patient outcomes. Thus the development of predictive models could help clinicians make betterindividualized decisions. The present study established a nomogram to predict postoperative survival of osteosarcoma patients using the large population-based Surveillance, Epidemiology, and End Results (SEER) database and validated it with single-center data from an Asian/Chinese population. Methods: Data from osteosarcoma patients who underwent surgery from 2000 to 2016 in the SEER database were obtained and were randomly divided into a training set (n=1,057) and an internal validation set (n=1,057). Data from osteosarcoma patients who underwent surgery in our hospital from 2013 to 2016 were collected as an external validation set (n=65). Univariate and multivariate Cox proportional hazard models were used in the training set to screen for prognostic factors and a nomogram was established to individually predict 1-, 3-and 5-year cancer-specific survival (CSS) and overall survival (OS). The discrimination and calibration ability of the nomogram were evaluated using the Harrell concordance index (C-index), calibration curves and area under the curve (AUC). The clinical utility was evaluated using decision curve analysis (DCA).Results: Predictive nomograms were generated using characteristics including age, pathological subtype, the American Joint Committee on Cancer (AJCC) group-N, AJCC-M, tumor size, and tumor extension for CSS and OS. The C-indexes for the CSS training set, the internal validation set, and the external validation set were 0.731, 0.713, and 0.721, respectively. The C-indexes of OS C-indices were 0.734, 0.706, and 0.719, respectively. The calibration curve suggested that the nomograms were accurate in their predictions and that DCA showed broad clinical benefits. Moreover, the present nomograms exhibited high accuracy (for

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Characteristics and prognosis of telangiectatic osteosarcoma: a population-based study using the Surveillance, Epidemiology and End Results (SEER) database

Cited by 3 publications

References 27 publications

Prognostic analysis of telangiectatic osteosarcoma of the extremities

Prognostic analysis of telangiectatic osteosarcoma of the extremities

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

Development and validation of an online prognostic nomogram for osteosarcoma after surgery: a retrospective study based on the SEER database and external validation with single-center data

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