Evidence map›Paper›PMID 41653217›Full record

ArticleNeurosurgical review2026

Artificial intelligence improves survival prediction in patients with brain metastases submitted to radiosurgery.

Eliseu Becco Neto, João Paulo Mota Telles, Larissa Zaira Rafael Rolim, Francisco de Assis de Souza Filho, Vinicius Costa Becco de Souza, Letícia Costa Becco de Souza, Helvécio Neves Feitosa Filho, Rodrigo Becco de Souza, Dhiego Chaves de Almeida Bastos, Sujit Prabhu and 1 more

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Article in Neurosurgical review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Eliseu Becco NetoDivision of Neurosurgery, Department of Neurology, University of São Paulo, São Paulo, Brazil.
João Paulo Mota TellesDivision of Neurosurgery, Department of Neurology, University of São Paulo, São Paulo, Brazil.
Larissa Zaira Rafael RolimStrategic Center of Excellence in Water and Drought Policies, Federal University of Ceará, Ceará, Brazil.
Francisco de Assis de Souza FilhoStrategic Center of Excellence in Water and Drought Policies, Federal University of Ceará, Ceará, Brazil.
Vinicius Costa Becco de SouzaFaculty of Medicine, Federal University of Ceará, Ceará, Brazil.
Letícia Costa Becco de SouzaMedical School, Christus University, Ceará, Brazil.
Helvécio Neves Feitosa FilhoFaculty of Medicine, University of Fortaleza, Ceará, Brazil.
Rodrigo Becco de SouzaFaculty of Medicine, University of Fortaleza, Ceará, Brazil.
Dhiego Chaves de Almeida BastosNeurosurgery Department, Cleveland Clinic, Cleveland, OH, USA.
Sujit PrabhuDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eberval Gadelha FigueiredoDivision of Neurosurgery, Department of Neurology, University of São Paulo, São Paulo, Brazil. ebgadelha@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stereotactic radiosurgery (SRS) is effective for non-small cell lung cancer (NSCLC) brain metastases in deep or eloquent brain regions. Identifying predictors of treatment failure is crucial. Artificial intelligence (AI) models may improve prediction, but data on NSCLC BM are scarce. A retrospective study analyzed NSCLC patients with single brain metastases treated with SRS (Elekta Gamma Knife) from 2010 to 2015, with up to 10 years of follow-up. Clinical, radiological, and histological data were collected. Kaplan-Meier and Cox proportional hazards models assessed survival. Decision tree and random forest (RF) models predicted treatment failure, with feature importance analyzed. Among 133 patients (mean age 61.6, 56.4% male), most tumors were grade 1 (56.4%) and in the right hemisphere (60.2%). The mean tumor volume was 1.84 cm³. Decision trees identified metastasis volume and location as key predictors (AUC = 0.85). RF models improved prediction (AUC = 0.92). Tumor volume, diameter, and age were major predictors. AI models effectively identified patients at risk of treatment failure. A random forest based artificial intelligence model presented an excellent predictive ability for stereotactic radiosurgery success/failure in a population with NSCLC brain metastases, with an area under curve of 0.92. This predictive ability was superior to a decision tree or a simple diameter-to-volume ratio.

Indexed as

Artificial IntelligenceBrain NeoplasmsCarcinoma, Non-Small-Cell LungLung NeoplasmsRadiosurgeryAdultAgedDecision TreesFemaleHumansMaleMiddle AgedRandom ForestRetrospective StudiesTreatment OutcomeArtificial intelligenceBrain neoplasmsNeoplasm metastasisRadiosurgery

Identifiers

PMID41653217
PMCPMC12882965

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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.