Pathologic characteristics of breast cancer with special emphasis on prevalence of triple-negative breast cancer from Kenya: A 4-year experience.
Bibliographic record
Abstract
35 Background: Previous sub classification of breast cancer in Kenya has been fraught by small sample size, non uniform staining methodology and lack of independent review. Triple Negative Breast Cancer (TNBC) is a “special interest” cancer since it represents a significant proportion of breast cancer patients and is associated with a poorer prognosis. We aimed to determine the estrogen receptor (ER), progesterone receptor (PR) and Her2/neu receptor characteristics of breast cancers and the prevalence of TNBC diagnosed at Aga Khan University Hospital, Nairobi (AKUHN) between 2007 to date. Methods: Slides and blocks of archived invasive breast cancers diagnosed at AKUHN were identified, retrieved and reviewed by two independent pathologists. Histological type, grade and pathological stage were documented. Representative sections from available blocks were stained for ER, PR, Her2 with appropriate internal controls. Scores for ER/PR were interpreted based on the ALLRED system, Her2 /neu scoring followed CAP guidelines. The initial 111 cases were validated and confirmed at Sunnybrook Health Sciences Centre, Toronto. Results: 456 cases of invasive breast cancers were diagnosed at AKUHN during the study period. 91% of cases were invasive ductal carcinomas (NOS).The rest were special types. 37% of the tumors were grade 3 and 63% were grade 2. Blocks for 318 of 456 cases were available for receptor analysis. 54% were ER and/or PR positive, with 52% of these in women < 50 yrs. 86% of the ER and/or PR positive tumors were grade 2. Only 12% were Her2/neu positive. Of the 318 cases studied, 111 (32%) were identified as TNBC. Median age was 53 yrs. 88% were grade 3. Conclusions: Invasive ductal carcinoma (NOS) was the most common breast cancer in our study. Nearly half of our cases were ER and/or PR positive and a third were TNBC. Both occurred predominantly in women less than 50 yrs. This represents the largest validated pathologic sub classification of breast cancer from a tertiary academic hospital in Kenya. Expansion of this study to encompass all breast cancers diagnosed in Kenya is underway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".