Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer
BackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.817372/full |
| _version_ | 1818206263958306816 |
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| author | Huan Gao Huan Gao Zhi-yi He Xing-li Du Zheng-gang Wang Li Xiang |
| author_facet | Huan Gao Huan Gao Zhi-yi He Xing-li Du Zheng-gang Wang Li Xiang |
| author_sort | Huan Gao |
| collection | DOAJ |
| description | BackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.ResultsFor distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.ConclusionsOur study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system. |
| first_indexed | 2024-12-12T04:10:15Z |
| format | Article |
| id | doaj.art-58e0c35ee98146ddb9fbb3edb0c19ec0 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-12T04:10:15Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-58e0c35ee98146ddb9fbb3edb0c19ec02022-12-22T00:38:37ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-05-011210.3389/fonc.2022.817372817372Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung CancerHuan Gao0Huan Gao1Zhi-yi He2Xing-li Du3Zheng-gang Wang4Li Xiang5School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, ChinaTongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaTongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaTongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaTongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, ChinaBackgroundThis study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.MethodsA total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.ResultsFor distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.ConclusionsOur study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.https://www.frontiersin.org/articles/10.3389/fonc.2022.817372/fullmachine learningartificial neural networkSEERmetastasislung cancer |
| spellingShingle | Huan Gao Huan Gao Zhi-yi He Xing-li Du Zheng-gang Wang Li Xiang Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer Frontiers in Oncology machine learning artificial neural network SEER metastasis lung cancer |
| title | Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer |
| title_full | Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer |
| title_fullStr | Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer |
| title_full_unstemmed | Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer |
| title_short | Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer |
| title_sort | machine learning for the prediction of synchronous organ specific metastasis in patients with lung cancer |
| topic | machine learning artificial neural network SEER metastasis lung cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.817372/full |
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