ArticleFrontiers in genetics2020
A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Classification of different light colors applied during the incubation period based on small intestine morphology with XGBoost algorithm.BMC veterinary research · 2026Article
- Article
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- Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach.Research square · 2025Article
- Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.International journal of molecular sciences · 2025Article
- An interpretability heart disease prediction model based on stacking ensemble with SHAP.Frontiers in molecular biosciences · 2025Article
- Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study.Frontiers in oncology · 2024Article
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone.Frontiers in oncology · 2023Article
- Development and Validation of a Machine Learning-Based Prediction Model for Detection of Biliary Atresia.Gastro hep advances · 2023Article
- Diagnostic classification of cancers using DNA methylation of paracancerous tissues.Scientific reports · 2022Article
- XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer.Journal of translational medicine · 2022Article
- Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach.Computational and mathematical methods in medicine · 2022Article
- Using Machine Learning Techniques to Develop Risk Prediction Models for the Risk of Incident Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus: A Cohort Study.Frontiers in endocrinology · 2022Article
- A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data.Frontiers in oncology · 2022Article
- Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer.Journal of thoracic disease · 2021Article
- Predicting chemotherapy response using a variational autoencoder approach.BMC bioinformatics · 2021Article
- Evaluating the Risk of Breast Cancer Recurrence and Metastasis After Adjuvant Tamoxifen Therapy by Integrating Polymorphisms in Cytochrome P450 Genes and Clinicopathological Characteristics.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of cancer of unknown primary (CUP) is of great significance in designing more effective treatments and improving the diagnostic efficiency in cancer patients. In the study, we develop an appropriate machine learning model for tracing the tissue of origin of CUP with high accuracy after feature engineering and model evaluation. Based on a copy number variation data consisting of 4,566 training cases and 1,262 independent validation cases, an XGBoost classifier is applied to 10 types of cancer. Extremely randomized tree (Extra tree) is used for dimension reduction so that fewer variables replace the original high-dimensional variables. Features with top 300 weights are selected and principal component analysis is applied to eliminate noise. We find that XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10-fold cross-validation for training samples and 0.7421 on independent validation datasets for predicting tumor tissue of origin. Furthermore, by contrasting various performance indices, such as precision and recall rate, the experimental results show that XGBoost classifier significantly improves the classification performance of various tumors with less prediction error, as compared to other classifiers, such as K-nearest neighbors (KNN), Bayes, support vector machine (SVM), and Adaboost. Our method can infer tissue of origin for the 10 cancer types with acceptable accuracy in both cross-validation and independent validation data. It may be used as an auxiliary diagnostic method to determine the actual clinicopathological status of specific cancer.
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