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Abstract P3-10-17: The Fractal Analysis of Tumor Histology Is an Independent Prognostic Factor in Breast Cancer Patients

2010· article· en· W2041154020 on OpenAlexaff
Anthony M. Magliocco, Misha Eliasziw, Mauro Tambasco

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineBreast cancerInternal medicineOncologyFractal dimensionUnivariateProportional hazards modelFractal analysisMultivariate analysisLymph nodeUnivariate analysisFractalCancerPathologyMultivariate statisticsMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Background: Despite the usefulness of standard clinical prognosticators for breast cancer patients, recommendations for systemic adjuvant therapy are not entirely straightforward. Hence, there is a need for a more accurate independent prognosticator that provides additional information to improve risk assessment and associated therapeutic decisions. Methods: We used fractal dimension analysis to quantitatively assess the morphologic complexity of breast epithelium. To investigate the relationship between fractal dimension and patient survival we applied fractal analysis to pan-cytokeratin stained tissue microarray (TMA) cores derived from a series of 408 patients with over 10 years follow up, and analyzed three TMA cores for each patient. A data-oriented approach was used to stratify patients according to low (<1.56), intermediate (1.56-1.75), and high (>1.75) fractal dimension. Univariate statistical analyses were performed to show the relationship between outcome and fractal dimension, tumour size, tumour grade, lymph node status, estrogen receptor status, and HER-2/neu status. Multivariate analysis was performed to assess the relative effect of these prognosticators on disease-specific and overall survival. Results: Patients with higher fractal score had significantly lower disease-specific 10-year survival (69.4%, 56.4%, and 25.0%, for low, intermediate, and high fractal dimension, respectively, P<0.001). Overall 10-year survival showed a similar association with fractal dimension. Cox regression analysis showed fractal dimension, lymph node status, and grade to be the only significant (P<0.05) independent predictors for both disease-specific and overall survival. Fractal dimension had the highest hazard ratio for overall survival 2.7(95% confidence interval (CI)=1.6-4.7); P<0.001), and the second highest for disease-specific survival 2.6(95% CI=1.4-4.8; P=0.002) versus 3.1(95% CI=1.9-5.1; P<0.001) for lymph node status. Discussion: Except for lymph node status, morphologic complexity of breast epithelium as measured by fractal dimension is more strongly and significantly associated with disease-specific and overall survival than standard clinical prognosticators. Furthermore, it is independent of standard prognosticators, and unlike tumour grade, it provides prognostic information that can be objectively assessed from TMA cores. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-10-17.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.402
Teacher spread0.369 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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