Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Jiang, Jianxia; Pan, Hao; Li, Mobai; Qian, Bao; Lin, Xianfeng; Fan, Shunwu

doi:10.1038/s41598-021-85223-4

Cited by 35 publications

(32 citation statements)

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“…Extreme gradient boosting (XGBoost), a typical boosting algorithm, is an integrated technology that can be applied to adjust the errors generated by existing models (21,22). XGBoost models have been used for effective and precise survival prediction in several cancers, including breast cancer (23), osteosarcoma (24), and non-small-cell lung cancer (25); however, their applicability to ICC is unknown.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Xu¹,

Lü²

2022

J Gastrointest Oncol

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Background: Nomograms have been established to predict survival in postoperative or elderly intrahepatic cholangiocarcinoma (ICC) patients. There are no models to predict postoperative survival in elderly ICC patients. Extreme gradient boosting (XGBoost) can adjust the errors generated by existing models. This retrospective cohort study aimed to develop and validate an XGBoost model to predict postoperative 5-year survival in elderly ICC patients. Methods: The Surveillance, Epidemiology, and End Results (SEER) program provided data on elderly ICC patients aged 60 years or older and undergoing surgery. The median follow-up time was 20 months. Totally 1,055 patients were classified as training (n=738) and testing (n=317) sets at a ratio of 7:3. The outcome was postoperative 5-year survival. Demographic, tumor-related and treatment-related variables were collected. Variables were screened using the XGBoost model. The predictive performance of the model was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kaplan-Meier curve. Cox regression analysis was conducted to estimate the risk of death in the predicted populations. The predictive abilities of the XGBoost model and the American Joint Commission on Cancer (AJCC) system (7th edition) were compared.Results: The XGBoost model achieved an AUC of 0.811, a sensitivity of 0.573, a specificity of 0.890, and a PPV of 0.849 in the training set. In the testing set, the model had an AUC of 0.713, a sensitivity of 0.478, a specificity of 0.814, and a PPV of 0.726. The 5-year mortality risk of patients predicted to die was 2.91 times that of patients predicted to survive [hazard ratio (HR) =2.91, 95% confidence interval (CI): 2.42-3.50]. The XGBoost model showed a better predictive performance than the AJCC staging system both in the training and testing sets. AJCC stage, multiple (satellite) tumors/nodules, tumor-node-metastasis (TNM) stage, more than one lobe invaded, direct invasion of adjacent organs, tumor size, and radiotherapy were relatively important features in survival prediction. Conclusions:The XGBoost model exhibited some predictive capacity, which may be applied to predict postoperative 5-year survival for elderly ICC patients.

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Section: Introductionmentioning

confidence: 99%

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Xu¹,

Lü²

2022

J Gastrointest Oncol

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“…Several predictive models for osteosarcoma have been constructed in previous studies ( 9 , 26 , 40 , 41 ); however, the corresponding results will be more credible if they can be verified externally. In addition, whether they exhibit universal applicability in Asian/Chinese populations is also questionable.…”

Section: Discussionmentioning

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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“…ML algorithms are now being successfully applied to predict cancer survival (Angraal et al, 2020). The XGBoost algorithm (XGB), in particular, is shown to have excellent prediction performance in previous studies (Senders et al, 2020;Jiang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model for the 10-year Overall Survival Status of Patients With Distant Metastases From Differentiated Thyroid Cancer Using XGBoost Algorithm-A Population-Based Analysis

et al. 2022

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Purpose: To explore clinical and non-clinical characteristics affecting the prognosis of patients with differentiated thyroid cancer with distant metastasis (DTCDM) and establish an accurate overall survival (OS) prognostic model.Patients and methods: Study subjects and related information were obtained from the National Cancer Institute’s surveillance, epidemiology, and results database (SEER). Kaplan‐Meier analysis, log-rank test, and univariate and multivariate Cox analysis were used to screen for factors influencing the OS of patients with DTCDM. Nine variables were introduced to build a machine learning (ML) model, receiver operating characteristic (ROC) was used to evaluate the recognition ability of the model, calibration plots were used to obtain prediction accuracy, and decision curve analysis (DCA) was used to estimate clinical benefit.Results: After applying the inclusion and exclusion criteria, a total of 3,060 patients with DTCDM were included in the survival analysis from 2004 to 2017. A machine learning prediction model was developed with nine variables: age at diagnosis, gender, race, tumor size, histology, regional lymph node metastasis, primary site surgery, radiotherapy, and chemotherapy. After excluding patients who survived <120 months, variables were sub-coded and machine learning was used to model OS prognosis in patients with DTCDM. Patients 6–50 years of age had the highest scores in the model. Other variables with high scores included small tumor size, male sex, and age 51–76. The AUC and calibration curves confirm that the XGBoost model has good performance. DCA shows that our model can be used to support clinical decision-making in a 10-years overall survival model.Conclusion: An artificial intelligence model was constructed using the XGBoost algorithms to predict the 10-years overall survival rate of patients with DTCDM. After model validation and evaluation, the model had good discriminative ability and high clinical value. This model could serve as a clinical tool to help inform treatment decisions for patients with DTCDM.

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Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Cited by 35 publications

References 35 publications

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

Development and validation of an XGBoost model to predict 5-year survival in elderly patients with intrahepatic cholangiocarcinoma after surgery: a SEER-based study

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

A Predictive Model for the 10-year Overall Survival Status of Patients With Distant Metastases From Differentiated Thyroid Cancer Using XGBoost Algorithm-A Population-Based Analysis

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