ArticleCancer medicine2023
Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review.Frontiers in oncology · 2025Pooled it
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning.Cancer innovation · 2025Article
- Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.Cancer innovation · 2024Article
- Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.Breast cancer research and treatment · 2024Article
- Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.Journal of cancer research and clinical oncology · 2024Article
- Personalized chemotherapy selection for patients with triple-negative breast cancer using deep learning.Frontiers in medicine · 2024Article
- Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.PloS one · 2024Article
- Deep learning to optimize radiotherapy decisions for elderly patients with early-stage breast cancer: a novel approach for personalized treatment.American journal of cancer research · 2024Article
- Optimizing adjuvant treatment options for patients with glioblastoma.Frontiers in neurology · 2024Article
- Individualized survival prediction and surgery recommendation for patients with glioblastoma.Frontiers in medicine · 2024Article
- Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.Cancer medicine · 2023Article
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundDue to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial.
aimTo determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity.
methodsPatients were enrolled from the SEER database. We used a novel DL approach to make treatment recommendations for patients with LGG. We also made causal inference of the average treatment effect (ATE) of GTR compared with STR.
resultsThe patients were divided into the Consis. and In-consis. groups based on whether their actual treatment and model recommendations were consistent. Better brain cancer-specific survival (BCSS) outcomes in the Consis. group was observed. Overall, we also identified two subgroups that showed strong heterogeneity in response to GTR. By interpreting the models, we identified numerous variables that may be related to treatment heterogeneity.
conclusionsThis is the first study to infer the individual treatment effect, make treatment recommendation, and guide surgical options through deep learning approach in LGG research. Through causal inference, we found that heterogeneous responses to STR and GTR exist in patients with LGG. Visualization of the model yielded several factors that contribute to treatment heterogeneity, which are worthy of further discussion.
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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.