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Record W2593091679

Methodological and analytical aspects of proliferation assessment in breast cancer

2016· dissertation· en· W2593091679 on OpenAlexfundno aff
CM Focke

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2016
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
FundersCure Brain Cancer FoundationNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchGovernment of OntarioBreast Cancer Research Foundation
KeywordsBreast cancerConcordanceMitotic indexImmunohistochemistryProliferation MarkerMedicineBiopsyOncologyPathologyCancerProliferation indexKi-67Internal medicineBiologyMitosis
DOInot available

Abstract

fetched live from OpenAlex

Proliferation is a major determinant of prognosis in breast cancer and plays a key role in individualised treatment of the disease. While patients with tumours showing low proliferative activity can be spared chemotherapy in the early stages of the disease, a more aggressive treatment should be considered in patients with highly proliferating breast cancers. The most commonly used markers of proliferation are histological grade (including mitotic count), and Ki67- and PHH3-Index. However, a generally accepted and standardised proliferation assessment method has not yet been established for clinical decision making, and little is known about factors that could potentially compromise its standardisation. Therefore, we tested the impact of different Ki67 assessment methods on resulting Ki67 levels, investigated the extent of intratumoral heterogeneity of Ki67 expression and surveyed the status quo of interlaboratory variability of Ki67 staining in breast cancer. Then, as tumour proliferation in core needle biopsies had been previously reported to be lower than in subsequent surgical excisions, we also investigated whether increased biopsy volume would result in higher concordance rates between the specimens. In addition, we tested the performance of four commercially available immunohistochemical PHH3 antibodies for marking mitotic figures in a series of highly proliferating breast cancers. Our data show that Ki67 levels are highly influenced by both laboratory-specific analytic variables and selection of assessment protocols. For the latter, the major cause for differing results was intratumoral heterogeneity of Ki67 expression, which exists across all breast cancer subtypes and exceeded even variability between tumours. Differences in Ki67 staining performance between pathology labs, however, may be attributed to a lack of a morphologic correlate for proliferating cells (except for mitoses), which could be used as an internal control for Ki67 staining. Interestingly, the reliability of histological grade in core needle biopsies improved with increasing biopsy sample size while reliability of Ki67 levels in core biopsies was unaffected by biopsy volume but did not show higher concordance between core biopsies and subsequent surgical excisions than for histological grade in general. Sensitivity and specificity of the tested PHH3 antibodies to detect mitoses varied substantially in our study, showing insufficient performance in two of four antibodies. In conclusion, our data support the recommendation of international expert panels to use Ki67 levels for therapeutic decisions only in the context of laboratory specific reference values and in full awareness of analysed specimen type. Furthermore, our results emphasize the importance of correlation with histomorphology when immunohistochemical stains are used and interpreted for proliferation assessment. Our findings may contribute to a better understanding of methodological issues in individualised patient care that are currently highly debated, and may help to develop an international standard for proliferation assessment in breast cancer.

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)0.113
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.887
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.190
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.005
Science and technology studies0.0020.005
Scholarly communication0.0040.001
Open science0.0040.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.363
Teacher spread0.323 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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