Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer

Wu, Yijun; Rao, Ke; Liu, Jianghao; Han, Chang; Gong, Liang; Chong, Yuming; Liu, Ziwen; Xu, Xiequn

doi:10.3389/fendo.2020.577537

Cited by 57 publications

(52 citation statements)

References 39 publications

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“…All results have been confirmed in ML algorithms. Our study suggests that males are frequently found to be more susceptible to CLNM, which is supported by findings of previous studies ( 12 , 29 ).…”

Section: Discussionsupporting

confidence: 93%

“…Compared with studies attempting to predict the risk of central compartment lymph node metastases in PTC ( 12 , 27 , 28 , 33 , 34 ), our work has several strengths. First, few studies have ever focused on the subgroup of patients who suffer from clinically low-risk PTC.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML), as a novel type of artificial intelligence (AI), is starting to be widely applied to health-care data analysis ( 11 , 12 ). By capitalizing on the robust prediction ability of ML algorithms, it may be possible to develop prediction tools which in some cases outperform traditional statistical modeling, and thus giving better prediction of CLNM status.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Zhu

Zheng

et al. 2021

Front. Med.

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Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms.Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/).Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Zhu

Zheng

et al. 2021

Front. Med.

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“…Algorithmic decision support tools, computer-assisted navigation and surgical robots have served in the clinic (3,4). Machine learning (ML) has been widely used in healthcare data analysis and some in spinal surgery (5,6). By the comparison among ML algorithms, a better predictive model can be constructed to predict cement leakage after percutaneous vertebroplasty.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Wang

Liu

et al. 2021

Front. Public Health

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Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.

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“…Zhou et al ( 29 ) studied one or a combination of machine learning methods in Logistic regression, Random forest, XGBoost, and GBDT to construct lymph node metastasis in patients with poorly differentiated intramucosal gastric cancer. Supervised machine learning methods including random forest classifier, artificial neural network, decision tree, gradient boosting decision tree, extreme gradient boosting, and adaptive boosting can also be used to predict central lymph node metastasis in patients with papillary thyroid cancer ( 30 ). Besides improving the accuracy of overall prediction, we further focus on boosting the performance on sensitivity by the designed “Two-Stage Cost Sensitive Hypergraph Learning.” One stage is to capture the cost sensitivity of negative cases in the latent feature spaces, which will enable the hypergraph model to allocate the lymph node involvement cases more weights.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

Yan

Zhang

et al. 2022

Front. Med.

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Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named “Multi-Uncertainty Measurement” to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.

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Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer

Cited by 57 publications

References 39 publications

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

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