A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing’s Sarcoma

Li, Wenle; Qian, Zhou; Liu, Wencaı; Xu, Chan; Tang, Zhi-Ri; Dong, Shengtao; Wang, Haosheng; Li, Wanying; Zhang, Kai; Li, Rong; Zhang, Wenshi; Hu, Zhaohui; Su, Shibin; Liu, Qiang; Kuang, Sirui; Yin, Chengliang

doi:10.3389/fmed.2022.832108

Cited by 17 publications

(10 citation statements)

References 38 publications

(51 reference statements)

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“…There are various statistical methods for estimating the probability of occurrence of binary events such as NVCF, the most commonly used being logistic regression (LR). With the rapid development in the field of artificial intelligence, machine learning (ML) is increasingly used in the medical field [ 8 , 9 ], in particular, when using large datasets for prediction [ 10 , 11 ]. However, it is unclear whether ML methods can provide better predictive power than traditional logistic regression algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

Yuan

et al. 2023

J Orthop Surg Res

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Background The use of machine learning has the potential to estimate the probability of a second classification event more accurately than traditional statistical methods, and few previous studies on predicting new fractures after osteoporotic vertebral compression fractures (OVCFs) have focussed on this point. The aim of this study was to explore whether several different machine learning models could produce better predictions than logistic regression models and to select an optimal model. Methods A retrospective analysis of 529 patients who underwent percutaneous kyphoplasty (PKP) for OVCFs at our institution between June 2017 and June 2020 was performed. The patient data were used to create machine learning (including decision trees (DT), random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), neural networks (NNET), and regularized discriminant analysis (RDA)) and logistic regression models (LR) to estimate the probability of new fractures occurring after surgery. The dataset was divided into a training set (75%) and a test set (25%), and machine learning models were built in the training set after ten cross-validations, after which each model was evaluated in the test set, and model performance was assessed by comparing the area under the curve (AUC) of each model. Results Among the six machine learning algorithms, except that the AUC of DT [0.775 (95% CI 0.728–0.822)] was lower than that of LR [0.831 (95% CI 0.783–0.878)], RA [0.953 (95% CI 0.927–0.980)], GBM [0.941 (95% CI 0.911–0.971)], SVM [0.869 (95% CI 0.827–0.910), NNET [0.869 (95% CI 0.826–0.912)], and RDA [0.890 (95% CI 0.851–0.929)] were all better than LR. Conclusions For prediction of the probability of new fracture after PKP, machine learning algorithms outperformed logistic regression, with random forest having the strongest predictive power. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

Yuan

et al. 2023

J Orthop Surg Res

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“…As one of the components of artificial intelligence, ML has been widely used in clinical practice, such as for epidemic prediction (23, 24), and survival analysis (25, 26). ML models have also been proposed for the prediction of lymph node metastasis in a variety of malignancies (27,28). Based on these promising clinical applications of ML models, the present study aimed to develop and validate a novel ML-based model for predicting the risk of early-stage ILNM in patients with SCCP.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis

et al. 2023

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ObjectiveInguinal lymph node metastasis (ILNM) is significantly associated with poor prognosis in patients with squamous cell carcinoma of the penis (SCCP). Patient prognosis could be improved if the probability of ILNM incidence could be accurately predicted at an early stage. We developed a predictive model based on machine learning combined with big data to achieve this.MethodsData of patients diagnosed with SCCP were obtained from the Surveillance, Epidemiology, and End Results Program Research Data. By combing variables that represented the patients' clinical characteristics, we applied five machine learning algorithms to create predictive models based on logistic regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor. Model performance was evaluated by ten-fold cross-validation receiver operating characteristic curves, which were used to calculate the area under the curve of the five models for predictive accuracy. Decision curve analysis was conducted to estimate the clinical utility of the models. An external validation cohort of 74 SCCP patients was selected from the Affiliated Hospital of Xuzhou Medical University (February 2008 to March 2021).ResultsA total of 1,056 patients with SCCP from the SEER database were enrolled as the training cohort, of which 164 (15.5%) developed early-stage ILNM. In the external validation cohort, 16.2% of patients developed early-stage ILNM. Multivariate logistic regression showed that tumor grade, inguinal lymph node dissection, radiotherapy, and chemotherapy were independent predictors of early-stage ILNM risk. The model based on the eXtreme Gradient Boosting algorithm showed stable and efficient prediction performance in both the training and external validation groups.ConclusionThe ML model based on the XGB algorithm has high predictive effectiveness and may be used to predict early-stage ILNM risk in SCCP patients. Therefore, it may show promise in clinical decision-making.

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“…(Mirza et al, 2019;Oliveira, 2019) Compared with traditional regression analysis, ML algorithm has signi cant advantages in prediction performance in large databases. (Bi et al, 2019;Wang et al, 2020) (Li and Zhou et al, 2022) To our knowledge, there is no effective ML model for predicting risks of LNM of PCa. Therefore, in this study, we established a new model for predicting risks of LNM in patients with intermediate and high-risk PCa through six ML methods based on the clinical and histopathological parameters that are closely related to the prognosis of the PCa in the SEER database.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer

wang

zhang

et al. 2023

Preprint

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Purpose This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals. Methods A total of 24,470 patients with intermediate and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely, random forest (RF), naive bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity. Results In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate and high-risk PCa. Conclusion T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate and high-risk PCa. The prediction model established in this study performs well, however, the GBM model is the best one.

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A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing’s Sarcoma

Cited by 17 publications

References 38 publications

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis

Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer

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