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Record W3169087393 · doi:10.1186/s40644-021-00410-w

Exploration of spatial distribution of brain metastasis from small cell lung cancer and identification of metastatic risk level of brain regions: a multicenter, retrospective study

2021· article· en· W3169087393 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCancer Imaging · 2021
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsnot available
FundersJiangsu Provincial Key Research and Development ProgramNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsMedicineWhite matterMagnetic resonance imagingBrainstemBrain metastasisTemporal lobeBrain atlasPathologyPutamenLung cancerNuclear medicineRadiologyInternal medicineMetastasisCancerNeuroscienceBiology

Abstract

fetched live from OpenAlex

Abstract Objectives This study aimed to explore the spatial distribution of brain metastases (BMs) from small cell lung cancer (SCLC) a homogenous sample, and to identify the metastatic risk levels in brain regions. Methods T1-enhanced magnetic resonance imaging (MRI) from SCLC patients were retrospectively reviewed from three medical institutions in China. All images were registered to the standard brain template provided by the Montreal Neurological Institute (MNI) 152 database, followed by transformation of the location of all BMs to the space of standard brain. The MNI structural atlas and Anatomical Automatic Labeling (AAL) atlas were then used to identify the anatomical brain regions, and the observed and expected rates of BMs were compared using 2-tailed proportional hypothesis testing. The locations and sizes of brain lesions were analyzed after image standardization. Results A total of 215 eligible patients with 1033 lesions were screened by MRI, including 157 (73%) males and 58 (27%) females. The incidence of crucial structures were as follows: hippocampus 0.68%, parahippocampal 0.97%, brainstem 2.05%, cauate 0.68%, putamen 0.68%, pallidum 0.2%, thalamus 1.36%. No BMs were found in the amygdala, pituitary gland, or pineal gland. The cumulative frequency of the important structures was 6.62%. Based on the results of MNI structural atlas, the cerebellum, deep white matter and brainstem was identified as a higher risk region than expected for BMs ( P = 9.80 ×10 −15 , 9.04 ×10 −6 ), whereas temporal lobe were low-risk regions ( P = 1.65 ×10 −4 ). More detailed AAL atlas revealed that the low-risk regions for BMs was inferior frontal gyrus ( P = 6.971 ×10 −4 ), while the high-risk regions for BMs was cerebellar hemispheres ( P = 1.177 ×10 −9 ). Conclusion Many crucial structures including the hippocampus, parahippocampus, pituitary gland and thalamus etc. have low frequency of brain metastases in a population of SCLC patients. This study provides the help to investigate the clinical feasibility of HA-WBRT and non-uniform dose of PCI in a population of SCLC patients.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.347
Teacher spread0.279 · 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