ArticleFrontiers in neuroinformatics2022
A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Pooled it
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Metabolic syndrome prediction model using Bayesian optimization and XGBoost based on traditional Chinese medicine features.Heliyon · 2023Article
- A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.Journal of translational medicine · 2023Article
- Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis. Methods: We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC ( Results: The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set. Conclusion: Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.