MétaCan
Menu
← Back to cohort

Abstract P2-10-33: Mitotic Component of Grade Can Distinguish Breast Cancer Patients at Greatest Risk of Local Relapse

2012· article· en· W2943923947 on OpenAlexaff
SJ Done, N. Miller, Wei Shi, Melania Pintilie, DR McCready, F-F Liu, Anthony Fyles

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineBreast cancerLymphovascular invasionOncologyPathologyCancerInternal medicineMetastasis

Abstract

fetched live from OpenAlex

Abstract Background: With the recent recognition of many different molecular subtypes of breast cancer a desire to more specifically categorize tumors to allow tailoring of treatment to individual patients has developed. This has largely involved the development of molecular tests rather than the re-examination of current pathologic criteria. We wanted to evaluate standard pathologic features to determine their ability to predict for local recurrence. Materials and Methods: Slides were retrieved for review from 280 of 769 women who had participated in a trial of tamoxifen with or without breast irradiation between December 1992 and June 2000 and for whom outcome data up to 18 years was available. All women were 50 years of age or older at the time of enrollment and had T1 or T2 node negative breast cancer. The cases for which slides were obtained were representative of the whole group. The slides were reviewed by two breast pathologists (SJD and NAM). Several features were evaluated; modified Nottingham histologic grade and its components- degree of tubule formation, nuclear pleomorphism and mitotic count. Mitotic component of grade was calibrated to the microscopic field size used. The presence of lymphatic/vascular space invasion was also scored. A statistical analysis was performed to relate these pathologic features to local recurrence at up to 18 years. Results: The strongest predictor of local recurrence was the mitotic component of the Nottingham histologic grade with 5.7% for mitotic score 1/3 (n = 200), 19.6% for mitotic score 2/3 (n = 37) and 19.8% for mitotic score 3/3 (n = 43)(Gray's p-value = 0.0021). Overall grade was also able to predict for local recurrence with 2.6% for Grade 1 (n = 49), 10.6% for Grade 2 (n = 162) and 17.9% for Grade 3 (n = 71)(Gray's p-value=0.026). However, neither architecture (0% vs. 9.5% vs. 9.8%, Gray's p-value=0.74) nor degree of nuclear pleomorphism (0% vs. 7.9% vs. 11.5%, Gray's p-value=0.37), the other components of histologic grade, showed a statistically significant difference for recurrence. The presence or absence of endothelial lined space invasion was also found to be not statistically different (9.3% vs. 13%, Gray's p-value=0.55). Conclusion: Within this cohort of tamoxifen treated T1 and T2 breast cancer patients 50 years of age or older, mitotic index could stratify women into groups with high and low risk of recurrence. If validated this may be a useful way of allocating patients to different treatment groups. Additional validation studies are planned on similar groups of patients. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-33.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.341
Teacher spread0.306 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicBreast Cancer Treatment Studies→French-language works237,207→