A population-based propensity-matched study of regional dissections in patients with metastatic osteosarcoma

Wang, Wenjuan; Ding, Hongzhi; Sun, Zhenyu; Chen, Jin; Zhu, Yanhui; Wang, Xiang

doi:10.1186/s13018-020-01592-y

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Former studies focused on finding factors that influence the osteosarcoma prognosis to evaluate the overall survival or cancer-specific survival [ 14 , 15 ]. There is still no study which focuses on the metastasis in osteosarcoma, which is the most important factor for cancer prognosis [ 16 ]. However, nomogram has been applied to predict metastasis in other cancer types.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Wang

Liu

et al. 2021

J Orthop Surg Res

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Background Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models. Results One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram. Conclusion We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Wang

Liu

et al. 2021

J Orthop Surg Res

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“…1 Generally speaking, metastasis is one of the most significant factors affecting the prognosis of patients with OS and remains the biggest obstacle to improve the prognosis of OS. 2,3,4 Even after surgery and chemotherapy, there is unfavourable prognosis in patients with metastatic OS. 5,6 The lung is the main site of metastasis in patients with OS.…”

Section: Introductionmentioning

confidence: 99%

Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction

Huang

Yin

et al. 2023

J Orthop Surg (Hong Kong)

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Background Metastasis is one of the most significant prognostic factors in osteosarcoma (OS). The goal of this study was to construct a clinical prediction model for OS patients in a population cohort and to evaluate the factors influencing the occurrence of pulmonary metastasis. Methods We collected data from 612 patients with osteosarcoma (OS), and 103 clinical indicators were collected. After the data were filtered, the patients were randomly divided into training and validation cohorts by using random sampling. The training cohort included 191 patients with pulmonary metastasis in OS and 126 patients with non-pulmonary metastasis, and the validation cohort included 50 patients with pulmonary metastasis in OS and 57 patients with non-pulmonary metastasis. Univariate logistics regression analysis, LASSO regression analysis and multivariate logistic regression analysis were performed to identify potential risk factors for pulmonary metastasis in patients with osteosarcoma. A nomogram was developed that included risk influencing variables selected by multivariable analysis, and used the concordance index (C-index) and calibration curve to validate the model. Receiver operating characteristic curve (ROC), decision analysis curve (DCA) and clinical impact curve (CIC) were employed to assess the model. In addition, we used a predictive model on the validation cohort. Results Logistic regression analysis was used to identify independent predictors [N Stage + Alkaline phosphatase (ALP)+Thyroid stimulating hormone (TSH)+Free triiodothyronine (FT3)]. A nomogram was constructed to predict the risk of pulmonary metastasis in patients with osteosarcoma. The performance was evaluated by the concordance index (C-index) and calibration curve. The ROC curve provides the predictive power of the nomogram (AUC = 0.701 in the training cohort, AUC = 0.786 in the training cohort). Decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the clinical value of the nomogram and higher overall net benefits. Conclusions Our study can help clinicians effectively predict the risk of lung metastases in osteosarcoma with more readily available clinical indicators, provide more personalized diagnosis and treatment guidance, and improve the prognosis of patients. Mini Abstract A new risk model was constructed to predict the pulmonary metastasis in patients with osteosarcoma based on multiple machine learning.

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“…However, for those diagnosed with advanced and metastatic disease, systemic chemotherapy, targeted molecular therapy, and palliative treatment serve as principal strategies (10)(11)(12). Emerging evidence suggests that primary tumor resection (PTR) also offers significant survival benefits for patients with advanced PMBNs (13)(14)(15). This is presumably due to the ability of PTR to counteract the immunosuppressive effects, even in the advanced stage of the disease (16).…”

Section: Introductionmentioning

confidence: 99%

Clinical characteristic–assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score–matched analysis

Tong,

Jiang,

Cui

et al. 2023

Front. Oncol.

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BackgroundPrimary tumor resection (PTR) is the standard treatment for patients with primary malignant bone neoplasms (PMBNs). However, it remains unclear whether patients with advanced PMBNs still benefit from PTR. This study aimed to develop a prediction model to estimate the beneficial probability of PTR for this population.MethodsThis study extracted data from patients diagnosed with advanced PMBNs, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database, with the period from 2004 to 2015. The patient cohort was then bifurcated into two groups: those who underwent surgical procedures and the non-surgery group. Propensity score matching (PSM) was utilized to mitigate any confounding factors in the study. The survival rates of patients from both the surgical and non-surgery groups were evaluated using Kaplan–Meier (K-M) curves analysis. Moreover, the study used this method to assess the capacity of the nomogram to distinguish patients likely to derive benefits from surgical intervention. The study was grounded in the hypothesis that patients who underwent PTR and survived beyond the median overall survival (OS) time would potentially benefit from the surgery. Subsequently, logistic regression analysis was performed to ascertain significant predictors, facilitating the development of a nomogram. This nomogram was subjected to both internal and external validation using receiver operating characteristic curves, area under the curve analysis, calibration plots, and decision curve analysis.ResultsThe SEER database provided a total of 839 eligible patients for the study, among which 536 (63.9%) underwent PTR. Following a 2:1 PSM analysis, patients were classified into two groups: 364 patients in the surgery group and 182 patients in the non-surgery group. Both K-M curves and multivariate Cox regression analysis revealed that patients who received PTR had a longer survival duration, observed both before and after PSM. Crucial factors such as age, M stage, and tumor size were identified to be significantly correlated with surgical benefits in patients with advanced PMBNs. Subsequently, a nomogram was developed that uses these independent predictors. The validation of this predictive model confirmed its high accuracy and excellent discrimination ability of the nomogram to distinguish patients who would most likely benefit from surgical intervention.ConclusionIn this study, we devised a user-friendly nomogram to forecast the likehood of surgical benefits for patients diagnosed with advanced PMBNs. This tool facilitates the identification of the most suitable candidates for PTR, thus promoting more discerning and effective use of surgical intervention in this patient population.

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A population-based propensity-matched study of regional dissections in patients with metastatic osteosarcoma

Cited by 8 publications

References 30 publications

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction

Clinical characteristic–assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score–matched analysis

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