Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Xu, Chan; Liu, Wencai; Yin, Chengliang; Li, Wanying; Liu, Jingjing; Sheng, Wanli; Tang, Haotong; Li, Wenle; Zhang, Qingqing

doi:10.1155/2022/5676570

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Xu et al used the ML method to predict bone metastasis in renal cell carcinoma. The comparison of evaluation indexes among various ML methods showed that the XGB ML model had the highest AUC (0.891) in clinical prediction 33 . In addition, Jiang et al used the XGB model combined with deep learning for early identification of preoperative microvascular invasion in HCC patients, and the differential rate of the AUC could reach 0.906 34 .…”

Section: Discussionmentioning

confidence: 99%

“…The comparison of evaluation indexes among various ML methods showed that the XGB ML model had the highest AUC (0.891) in clinical prediction. 33 In addition, Jiang et al used the XGB model combined with deep learning for early identification of preoperative microvascular invasion in HCC patients, and the differential rate of the AUC could reach 0.906. 34 In our study, because the probability of OM in PLC patients is low, even when the data of PLC patients were collected for 14 years, the incidence of OM was only 1.3%.…”

Section: Modelmentioning

confidence: 99%

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Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

Sun,

Wu,

Mou

et al. 2023

Cancer Medicine

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BackgroundOcular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML).MethodsWe retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non‐ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10‐fold cross‐validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM.ResultsSix variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA.ConclusionWe used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

Sun,

Wu,

Mou

et al. 2023

Cancer Medicine

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“…[81] Bone metastases occur in about 20% to 35% of patients with mRCC, and these patients have a 5-year survival rate of <11%. [82] Predominantly osteolytic, these bone metastases lead to extensive skeletal destruction. Consequently, many patients with RCC experience skeletal-related events (SREs), such as pain, fractures, and nerve compression, particularly in advanced disease stage.…”

Section: The Site Of Rcc Metastasismentioning

confidence: 99%

Therapeutic options for different metastatic sites arising from renal cell carcinoma: A review

Wang,

Qian,

Qian

et al. 2024

Medicine

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Renal cell carcinoma (RCC) stands among the top 10 malignant neoplasms with the highest fatality rates. It exhibits pronounced heterogeneity and robust metastatic behavior. Patients with RCC may present with solitary or multiple metastatic lesions at various anatomical sites, and their prognoses are contingent upon the site of metastasis. When deliberating the optimal therapeutic approach for a patient, thorough evaluation of significant risk factors such as the feasibility of complete resection, the presence of oligometastases, and the patient’s functional and physical condition is imperative. Recognizing the nuanced differences in RCC metastasis to distinct organs proves advantageous in contemplating potential treatment modalities aimed at optimizing survival outcomes. Moreover, discerning the metastatic site holds promise for enhancing risk stratification in individuals with metastatic RCC. This review summarizes the recent data pertaining to the current status of different RCC metastatic sites and elucidates their role in informing clinical management strategies across diverse metastatic locales of RCC.

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“…Due to the huge data in the SEER database and the scientific nature of the new algorithm, the model performance has been improved. However, the rationality and completeness of the included variables still need to be improved, and the performance evaluation also needs to be supplemented ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Zhang

Huang

et al. 2022

Front. Public Health

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BackgroundBone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.MethodsThe study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.ResultsOur prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083–0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979–0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.ConclusionOur developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

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Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Cited by 5 publications

References 40 publications

Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

Therapeutic options for different metastatic sites arising from renal cell carcinoma: A review

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

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