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Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.
Zhou, Cheng-Mao; Wang, Ying; Xue, Qiong; Zhu, Yu.
Afiliación
  • Zhou CM; Perioperative Medical Big Data Research Group, Department of Anaesthesiology, 637250Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
  • Wang Y; Department of Anesthesiology, Pain and Perioperative Medicine, 191599First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Xue Q; Department of Anesthesiology, Pain and Perioperative Medicine, 191599First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Zhu Y; Perioperative Medical Big Data Research Group, Department of Anaesthesiology, 637250Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
Cancer Control ; 30: 10732748231167958, 2023.
Article en En | MEDLINE | ID: mdl-37010850
OBJECTIVE: We tested the performance of general machine learning and joint machine learning algorithms in the classification of bone metastasis, in patients with lung adenocarcinoma. METHODS: We used R version 3.5.3 for statistical analysis of the general information, and Python to construct machine learning models. RESULTS: We first used the average classifiers of the 4 machine learning algorithms to rank the features and the results showed that race, sex, whether they had surgery and marriage were the first 4 factors affecting bone metastasis. Machine learning results in the training group: for area under the curve (AUC), except for RF and LR, the AUC values of all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC for any single machine learning algorithm. Among the results related to accuracy and precision, the accuracy of other machine learning classifiers except the RF algorithm was higher than 70%, and only the precision of the LGBM algorithm was higher than 70%. Machine learning results in the test group: Similarly, for areas under the curve (AUC), except RF and LR, the AUC values for all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC value for any single machine learning algorithm. For accuracy, except for the RF algorithm, the accuracy of other machine learning classifiers was higher than 70%. The highest precision for the LGBM algorithm was .675. CONCLUSION: The results of this concept verification study show that machine learning algorithm classifiers can distinguish the bone metastasis of patients with lung cancer. This will provide a new research idea for the future use of non-invasive technology to identify bone metastasis in lungcancer. However, more prospective multicenter cohort studies are needed.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies Límite: Aged / Humans Idioma: En Revista: Cancer Control Asunto de la revista: NEOPLASIAS Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies Límite: Aged / Humans Idioma: En Revista: Cancer Control Asunto de la revista: NEOPLASIAS Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos