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Record W2959871824 · doi:10.1016/j.adro.2019.07.006

Pretreatment Volume of MRI-Determined White Matter Injury Predicts Neurocognitive Decline After Hippocampal Avoidant Whole-Brain Radiation Therapy for Brain Metastases: Secondary Analysis of NRG Oncology Radiation Therapy Oncology Group 0933

2019· article· en· W2959871824 on OpenAlexaff
Joseph Bovi, Stephanie L. Pugh, David S. Sabsevitz, Clifford G. Robinson, E.S. Paulson, Minesh P. Mehta, Vinai Gondi, Vijayananda Kundapur, Mark S. Shahin, Samuel T. Chao, Mitch Machtay, Albert S. DeNittis, Nadia N. Laack, Jeffrey Greenspoon, Kathleen N. Moore, Jiayi Huang, M.M. Dominello, Lisa A. Kachnic

Bibliographic record

VenueAdvances in Radiation Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsHamilton Health SciencesJuravinski Cancer CentreUniversity of Saskatchewan
FundersGenentechNational Cancer InstituteNRG Oncology
KeywordsMedicineNeurocognitiveVerbal learningRadiation therapyMagnetic resonance imagingInternal medicineWhite matterCognitive declineOncologyVerbal memoryHippocampal formationNuclear medicineDementiaDiseaseRadiologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

PurposeNRG Oncology's RTOG 0933 demonstrated benefits to memory preservation after hippocampal avoidant whole-brain radiation therapy (HA-WBRT), the avoidance of radiation dose to the hippocampus (using intensity modulated radiation planning and delivery techniques) during WBRT, supporting the hypothesis of hippocampal radiosensitivity and associated memory specificity. However, some patients demonstrated cognitive decline, suggesting mechanisms outside hippocampal radiosensitivity play a role. White matter injury (WMI) has been implicated in radiation therapy–induced neurocognitive decline. This secondary analysis explored the relationship between pretreatment WMI and memory after HA-WBRT.Methods and MaterialsVolumetric analysis of metastatic disease burden and disease-unrelated WMI was conducted on the pretreatment magnetic resonance image. Correlational analyses were performed examining the relationship between pretreatment WMI and Hopkins Verbal Learning Test-Revised (HVLT-R) outcomes at baseline and 4 months after HA-WBRT.ResultsIn the study, 113 patients received HA-WBRT. Of 113 patients, 33 underwent pretreatment and 4-month posttreatment HVLT testing and pretreatment postcontrast volumetric T1 and axial T2/fluid-attenuated inversion recovery magnetic resonance imaging. Correlation was found between larger volumes of pretreatment WMI and decline in HVLT-R recognition (r = 0.54, P < .05), and a correlational trend was observed between larger volume of pretreatment WMI and decline in HVLT-R delayed recall (r = 0.31, P = .08). Patients with higher pretreatment disease burden experienced a greater magnitude of stability or positive shift in HVLT-R recall and delayed recall after HA-WBRT (r = –0.36 and r = –0.36, P < .05), compared to the magnitude of stability or positive shift in those with lesser disease burden.ConclusionsIn patients receiving HA-WBRT for brain metastases, extent of pretreatment WMI predicts posttreatment memory decline, suggesting a mechanism for radiation therapy–induced neurocognitive toxicity independent of hippocampal stem cell radiosensitivity. Stability or improvement in HVLT after HA-WBRT for patients with higher pretreatment intracranial metastatic burden supports the importance of WBRT-induced intracranial control on neurocognition.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.331
Teacher spread0.321 · 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.

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

Citations30
Published2019
Admission routes1
Has abstractyes

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