Optimizing adjuvant treatment options for patients with glioblastoma
BackgroundThis study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1326591/full |
| _version_ | 1797301351323009024 |
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| author | Enzhao Zhu Jiayi Wang Weizhong Shi Qi Jing Pu Ai Dan Shan Zisheng Ai Zisheng Ai |
| author_facet | Enzhao Zhu Jiayi Wang Weizhong Shi Qi Jing Pu Ai Dan Shan Zisheng Ai Zisheng Ai |
| author_sort | Enzhao Zhu |
| collection | DOAJ |
| description | BackgroundThis study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.MethodsWe trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).ResultsThe Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48–0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55–0.78; dRMST: 7.92, 95% CI, 7.81–8.15; NNT: 1.67, 95% CI, 1.24–2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT.ConclusionOur study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations. |
| first_indexed | 2024-03-07T23:21:18Z |
| format | Article |
| id | doaj.art-c936c87e23834758a7b59f6558f838fc |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-03-07T23:21:18Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-c936c87e23834758a7b59f6558f838fc2024-02-21T05:51:35ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-02-011510.3389/fneur.2024.13265911326591Optimizing adjuvant treatment options for patients with glioblastomaEnzhao Zhu0Jiayi Wang1Weizhong Shi2Qi Jing3Pu Ai4Dan Shan5Zisheng Ai6Zisheng Ai7School of Medicine, Tongji University, Shanghai, ChinaSchool of Medicine, Tongji University, Shanghai, ChinaShanghai Hospital Development Center, Shanghai, ChinaDepartment of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, ChinaSchool of Medicine, Tongji University, Shanghai, ChinaFaculty of Health and Medicine, Lancaster University, Lancaster, United KingdomDepartment of Medical Statistics, School of Medicine, Tongji University, Shanghai, ChinaClinical Research Center for Mental Disorders, Chinese-German Institute of Mental Health, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, ChinaBackgroundThis study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.MethodsWe trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).ResultsThe Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48–0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55–0.78; dRMST: 7.92, 95% CI, 7.81–8.15; NNT: 1.67, 95% CI, 1.24–2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT.ConclusionOur study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.https://www.frontiersin.org/articles/10.3389/fneur.2024.1326591/fullglioblastomaradiotherapychemoradiotherapydeep learningmachine learning |
| spellingShingle | Enzhao Zhu Jiayi Wang Weizhong Shi Qi Jing Pu Ai Dan Shan Zisheng Ai Zisheng Ai Optimizing adjuvant treatment options for patients with glioblastoma Frontiers in Neurology glioblastoma radiotherapy chemoradiotherapy deep learning machine learning |
| title | Optimizing adjuvant treatment options for patients with glioblastoma |
| title_full | Optimizing adjuvant treatment options for patients with glioblastoma |
| title_fullStr | Optimizing adjuvant treatment options for patients with glioblastoma |
| title_full_unstemmed | Optimizing adjuvant treatment options for patients with glioblastoma |
| title_short | Optimizing adjuvant treatment options for patients with glioblastoma |
| title_sort | optimizing adjuvant treatment options for patients with glioblastoma |
| topic | glioblastoma radiotherapy chemoradiotherapy deep learning machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1326591/full |
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