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Record W2891370352 · doi:10.1016/j.nicl.2018.09.005

Early changes in white matter predict intellectual outcome in children treated for posterior fossa tumors

2018· article· en· W2891370352 on OpenAlexafffund
Marita Partanen, Éric Bouffet, Suzanne Laughlin, Douglas Strother, Juliette Hukin, Jovanka Skocic, Kamila U. Szulc, Donald Mabbott

Bibliographic record

VenueNeuroImage Clinical · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaSickKids FoundationAlberta Children's HospitalHospital for Sick Children
FundersCanadian Cancer Society Research InstituteCanadian Cancer Society
KeywordsWhite matterDiffusion MRIMedicineTractographyVoxelOptic radiationNeuroimagingMagnetic resonance imagingRadiologyNuclear medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: Prospective and longitudinal neuroimaging studies of posterior fossa tumors are scarce. Here we evaluate the early changes in white matter and intellectual outcome up to 3 years after diagnosis. PATIENTS AND METHODS: Twenty-two children with posterior fossa tumors and 24 similarly-aged healthy children participated. Patients included: (a) 12 individuals who received surgery, cranial-spinal radiation (CSR), and focal radiation to the tumor bed (CSR group) and (b) 10 individuals who received local therapy, either surgery only or surgery and focal radiation to the tumor bed (Local group). Diffusion tensor imaging (DTI) and intelligence measures were obtained an average of 3 months after diagnosis and then at 12, 24, and 36 months later. DTI tractography and voxel-wise approaches were employed. The Neurological Predictor Scale was used to summarize the type and amount of treatment for PF tumor patients. Linear mixed modelling was used to evaluate group differences at baseline and changes over time in DTI metrics for both the specific white matter tracts and voxel-wise, as well as for intelligence measures. RESULTS: Based on tractography, patients treated with CSR had significantly higher Axial and Mean diffusivity in the cortical-spinal tracts (CST) 3 month after diagnosis - particularly on the right side, p < .003, compared to healthy children. Mean diffusivity in right CST decreased over time in this group of patients, p = .001. No differences compared to controls were evident in specific tracts for the Local group, p > .10. Voxel-wise analyses revealed multiple areas of white matter compromise in both patients groups. Notably, both patient groups had lower scores on intelligence measures compared to the Control group: The CSR group displayed lower performance 3 months following diagnosis, ps < 0.001, and their performance remained stable over time ps > 0.10, whereas the Local group displayed no differences at 3 months, ps> 0.10, but their performance declined over time, ps < 0.01. At baseline, higher MD in right CST predicted lower Perceptual Reasoning scores across all participants, p = .001. Furthermore, lower FA in left IFOF at baseline predicted decline in Processing Speed over time, p = .001. In patients, more aggressive treatment protocols and presence of mutism were related to lower performance on intelligence measures at baseline, ps < 0.04. CONCLUSIONS: Children treated with CSR displayed diffuse white matter compromise and poor intellectual outcome shortly after radiation treatment. There was evidence of subsequent growth of white matter structure, but stable intellectual insult. Conversely, in children treated with either surgery only or surgery and focal radiation to the tumor bed we observed less compromise of white matter early following treatment and no intellectual insult compared to healthy children. However, declines in intellectual function were evident for these children, though their performance remained within the average normative range. Overall, results suggest that early intervention is necessary to circumvent these deficits.

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 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)0.001
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 score0.003
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.116
GPT teacher head0.422
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2018
Admission routes2
Has abstractyes

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