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Record W4416928116 · doi:10.1016/j.trecan.2025.11.007

The evolving landscape of brain metastasis: volume II

2025· article· en· W4416928116 on OpenAlexafffund
Manuel Valiente, Carey K. Anders, Adrienne Boire, Benjamin Izar, Nuria Kotecki, Srinivas Malladi, Joan Massagué, Nelson S. Moss, Josh Neman, Matthias Preusser, Sanne B. Schagen, Peter M. Siegel, Hussein A. Tawbi, Varun Venkataramani, Frank Winkler, Gelareh Zadeh, Johanna A. Joyce

Bibliographic record

VenueTrends in cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsMcGill University
FundersAgencia Estatal de InvestigaciónBreast Cancer Research FoundationEuropean Regional Development FundInstituto de Salud Carlos IIINextGenerationEUCanadian Institutes of Health ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institutes of HealthMinisterio de Ciencia e InnovaciónEngineering Research CentersFundación Científica Asociación Española Contra el CáncerUniversité de LausanneLudwig Institute for Cancer Research
KeywordsClinical trialCentral nervous systemPrecision medicineBrain metastasisCancerTherapeutic approachKey (lock)Metastasis

Abstract

fetched live from OpenAlex

Brain metastasis (BrM) represents the most common intracranial malignancy, arising in up to 30% of all adult cancer patients and contributing significantly to cancer-related morbidity and mortality. BrM is now recognized as a biologically distinct condition with unique mechanisms of organotropism, colonization, and therapeutic vulnerability. We highlight recent progress in omic and spatial profiling, which has revealed key drivers of brain tropism. These findings have reshaped therapeutic strategies, leading to clinical trials that specifically address central nervous system (CNS) involvement. Emerging approaches now include efforts to prevent brain relapse. Preclinical models increasingly provide sophisticated platforms to evaluate next-generation therapies. Collectively, these advances are transforming the clinical landscape, offering new hope for the prevention and management of BrM through precision medicine and integrated therapeutic strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0060.007
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.351
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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