Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network
BackgroundRemarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge.Materials and methodsA group of patients (N = 250) diagnosed with LUAD and receiving immunothera...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141408/full |
| _version_ | 1797858930243665920 |
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| author | Wei Li Siyun Fu Siyun Fu Xiang Gao Xiang Gao Zhendong Lu Zhendong Lu Renjing Jin Na Qin Xinyong Zhang Yuhua Wu Weiying Li Jinghui Wang Jinghui Wang |
| author_facet | Wei Li Siyun Fu Siyun Fu Xiang Gao Xiang Gao Zhendong Lu Zhendong Lu Renjing Jin Na Qin Xinyong Zhang Yuhua Wu Weiying Li Jinghui Wang Jinghui Wang |
| author_sort | Wei Li |
| collection | DOAJ |
| description | BackgroundRemarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge.Materials and methodsA group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients’ objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later.ResultsIn the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset.ConclusionsThis immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well. |
| first_indexed | 2024-04-09T21:21:18Z |
| format | Article |
| id | doaj.art-190060ccd2c84be69f4d6585a7dd7f0c |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-09T21:21:18Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-190060ccd2c84be69f4d6585a7dd7f0c2023-03-28T05:01:26ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-03-011410.3389/fimmu.2023.11414081141408Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural networkWei Li0Siyun Fu1Siyun Fu2Xiang Gao3Xiang Gao4Zhendong Lu5Zhendong Lu6Renjing Jin7Na Qin8Xinyong Zhang9Yuhua Wu10Weiying Li11Jinghui Wang12Jinghui Wang13Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, ChinaBackgroundRemarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge.Materials and methodsA group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients’ objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later.ResultsIn the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset.ConclusionsThis immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141408/fullimmunotherapylung adenocarcinomaneural networkdeep learningpredictive model |
| spellingShingle | Wei Li Siyun Fu Siyun Fu Xiang Gao Xiang Gao Zhendong Lu Zhendong Lu Renjing Jin Na Qin Xinyong Zhang Yuhua Wu Weiying Li Jinghui Wang Jinghui Wang Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network Frontiers in Immunology immunotherapy lung adenocarcinoma neural network deep learning predictive model |
| title | Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| title_full | Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| title_fullStr | Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| title_full_unstemmed | Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| title_short | Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| title_sort | immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network |
| topic | immunotherapy lung adenocarcinoma neural network deep learning predictive model |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141408/full |
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