MétaCan
Menu
Back to cohort
Record W4293193031 · doi:10.1016/j.jpi.2022.100090

Quantitative Nuclear Histomorphometry Predicts Molecular Subtype and Clinical Outcome in Medulloblastomas: Preliminary Findings

2022· article· en· W4293193031 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.

fundA Canadian funder is recorded on the work.
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

VenueJournal of Pathology Informatics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsnot available
FundersDOD Peer Reviewed Cancer Research ProgramDOD Prostate Cancer Research ProgramNational Center for Advancing Translational SciencesNational Center for Research ResourcesInstituto Nacional del CáncerNational Institutes of HealthMilitary Health SystemNational Cancer InstituteAstraZeneca CanadaHartwell FoundationEli Lilly and CompanyBristol-Myers SquibbNational Institute of Biomedical Imaging and BioengineeringBiomedical Laboratory Research and Development, VA Office of Research and DevelopmentNational Heart, Lung, and Blood InstituteBoehringer Ingelheim
KeywordsMedulloblastomaDigital pathologySonic hedgehogBiologyFeature (linguistics)PathologyImmunohistochemistryComputational biologyComputer sciencePattern recognition (psychology)Artificial intelligenceGeneCancer researchGeneticsMedicine

Abstract

fetched live from OpenAlex

Molecular subtypes of medulloblastoma [Sonic Hedgehog (SHH), Wingless/INT (WNT), Group 3, and Group 4] are defined by common patterns of gene expression. These differential gene expression patterns appear to result in different histomorphology and prognosis. Quantitative histomorphometry is a well-known method of computer-aided pathology image analysis. The hypotheses we sought to examine in this preliminary proof of concept study were whether computer extracted nuclear morphological features of medulloblastomas from digitized tissue slide images could independently: (1) distinguish between molecularly determined subgroups and (2) identify patterns within these subgroups that correspond with clinical outcome. Our dataset was composed of 46 medulloblastoma patients: 16 SHH (5 dead, 11 survived), 3 WNT (0 dead, 3 survived), 12 Group 3 (4 dead, 8 survived), and 15 were Group 4 (5 dead, 10 survived). A watershed-based thresholding scheme was used to automatically identify individual nuclei within digitized whole slide hematoxylin and eosin tissue images. Quantitative histomorphometric features corresponding to the texture (variation in pixel intensity), shape (variations in size, roundness), and architectural rearrangement (distances between, and number of connected neighbors) of nuclei were subsequently extracted. These features were ranked using feature selection schemes and these top-ranked features were then used to train machine-learning classifiers via threefold cross-validation to separate patients based on: (1) molecular subtype and (2) disease-specific outcomes within the individual molecular subtype groups. SHH and WNT tumors were separated from Groups 3 and 4 tumors with a maximum area under the receiver operating characteristic curve (AUC) of 0.7, survival within Group 3 tumors was predicted with an AUC of 0.92, and Group 3 and 4 patients were separated into high- and low-risk groups with p = 0.002. Model prediction was quantitatively compared with age, stage, and histological subtype using univariate and multivariate Cox hazard ratio models. Age was the most statistically significant variable for predicting survival in Group 3 and 4 tumors, but model predictions had the highest hazard ratio value. Quantitative nuclear histomorphometry can be used to study medulloblastoma genetic expression phenotypes as it may distinguish meaningful features of disease pathology.

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)0.002
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 score0.140
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.310
Teacher spread0.285 · 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