Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis
Abstract Background Epidermal growth factor receptor (EGFR) mutations are present in 10–60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicia...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14221-w |
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| author | Bardia Hajikarimloo Ibrahim Mohammadzadeh Salem M. Tos Mohammad Amin Habibi Rana Hashemi Ehsan Bahrami Hezaveh Dorsa Najari Arman Hasanzade Mehdi Hooshmand Sara bana |
| author_facet | Bardia Hajikarimloo Ibrahim Mohammadzadeh Salem M. Tos Mohammad Amin Habibi Rana Hashemi Ehsan Bahrami Hezaveh Dorsa Najari Arman Hasanzade Mehdi Hooshmand Sara bana |
| author_sort | Bardia Hajikarimloo |
| collection | DOAJ |
| description | Abstract Background Epidermal growth factor receptor (EGFR) mutations are present in 10–60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicians in decision-making and, through optimizing treatment strategies, can result in more favorable outcomes. This systematic review and meta-analysis evaluated the predictive performance of machine learning (ML)-based models in EGFR status in NSCLC patients with brain metastasis. Methods On December 20, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated EGFR status in patients with brain metastasis from NSCLC were included. Results Twenty studies with 3517 patients with 6205 NSCLC brain metastatic lesions were included. The majority of the best-performance models were ML-based (70%, 7/10), and deep learning (DL)-based models comprised 30% (6/20) of models. The area under the curve (AUC) and accuracy (ACC) of the best-performance models ranged from 0.765 to 1 and 0.69 to 0.93, respectively. The meta-analysis of the best-performance model revealed a pooled AUC of 0.91 (95%CI: 0.88–0.93) and ACC of 0.82 (95%CI: 0.79–0.86) along with a pooled sensitivity of 0.87 (95%CI: 0.83–0.9), specificity of 0.86 (95%CI: 0.79–0.9), and diagnostic odds ratio (DOR) of 35.2 (95%CI: 21.2–58.4). The subgroup analysis did not show significant differences between ML and DL models. Conclusion ML-based models demonstrated promising predictive outcomes in predicting EGFR status. Applying ML-based models in daily clinical practice can optimize treatment strategies and enhance clinical and radiological outcomes. |
| format | Article |
| id | doaj-art-97e482444a734873a33a5a31fa1f8f37 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-97e482444a734873a33a5a31fa1f8f372025-08-20T03:52:20ZengBMCBMC Cancer1471-24072025-05-0125111410.1186/s12885-025-14221-wMachine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysisBardia Hajikarimloo0Ibrahim Mohammadzadeh1Salem M. Tos2Mohammad Amin Habibi3Rana Hashemi4Ehsan Bahrami Hezaveh5Dorsa Najari6Arman Hasanzade7Mehdi Hooshmand8Sara bana9Department of Neurological Surgery, Shohada Tajrish Hospital, Shahid Beheshti University of Medical SciencesSkull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical SciencesDepartment of Neurological Surgery, University of VirginiaDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical SciencesAbstract Background Epidermal growth factor receptor (EGFR) mutations are present in 10–60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicians in decision-making and, through optimizing treatment strategies, can result in more favorable outcomes. This systematic review and meta-analysis evaluated the predictive performance of machine learning (ML)-based models in EGFR status in NSCLC patients with brain metastasis. Methods On December 20, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated EGFR status in patients with brain metastasis from NSCLC were included. Results Twenty studies with 3517 patients with 6205 NSCLC brain metastatic lesions were included. The majority of the best-performance models were ML-based (70%, 7/10), and deep learning (DL)-based models comprised 30% (6/20) of models. The area under the curve (AUC) and accuracy (ACC) of the best-performance models ranged from 0.765 to 1 and 0.69 to 0.93, respectively. The meta-analysis of the best-performance model revealed a pooled AUC of 0.91 (95%CI: 0.88–0.93) and ACC of 0.82 (95%CI: 0.79–0.86) along with a pooled sensitivity of 0.87 (95%CI: 0.83–0.9), specificity of 0.86 (95%CI: 0.79–0.9), and diagnostic odds ratio (DOR) of 35.2 (95%CI: 21.2–58.4). The subgroup analysis did not show significant differences between ML and DL models. Conclusion ML-based models demonstrated promising predictive outcomes in predicting EGFR status. Applying ML-based models in daily clinical practice can optimize treatment strategies and enhance clinical and radiological outcomes.https://doi.org/10.1186/s12885-025-14221-wMachine learningDeep learningEpidermal growth factor receptorEGFRLung neoplasmsBrain neoplasms |
| spellingShingle | Bardia Hajikarimloo Ibrahim Mohammadzadeh Salem M. Tos Mohammad Amin Habibi Rana Hashemi Ehsan Bahrami Hezaveh Dorsa Najari Arman Hasanzade Mehdi Hooshmand Sara bana Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis BMC Cancer Machine learning Deep learning Epidermal growth factor receptor EGFR Lung neoplasms Brain neoplasms |
| title | Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis |
| title_full | Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis |
| title_fullStr | Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis |
| title_full_unstemmed | Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis |
| title_short | Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis |
| title_sort | machine learning in prediction of epidermal growth factor receptor status in non small cell lung cancer brain metastases a systematic review and meta analysis |
| topic | Machine learning Deep learning Epidermal growth factor receptor EGFR Lung neoplasms Brain neoplasms |
| url | https://doi.org/10.1186/s12885-025-14221-w |
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