Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the commonest cancer diagnosed globally and the second leading cause of cancer-related mortality among women younger than 40 years. This study comparatively reviewed the demographic, pathologic and molecular features of Early-Onset Breast Cancer (EOBC) reported in Ghana in relation to Late Onset Breast Cancer (LOBC). METHODS: A descriptive, cross-sectional design was used, with purposive sampling of retrospective histopathology data from 2019 to 2021. Reports of core or incision biopsy, Wide Local Excision or Mastectomy with or without axillary lymph node dissection specimen and matched immunohistochemistry reports were merged into a single file and analysed with SPSS v. 20.0. Descriptive statistics of frequencies and percentages were used to describe categorical variables. Cross-tabulation and chi-square test was done at a 95% confidence interval with significance established at p < 0.05. RESULTS: A total of 2418 cases were included in the study with 20.2% (488 cases) being EOBCs and 79.8% (1930 cases) being LOBCs. The median age at diagnosis was 34.66 (IQR: 5.55) in the EOBC group (< 40 years) and 54.29 (IQR: 16.86) in the LOBC group (≥ 40 years). Invasive carcinoma-No Special Type was the commonest tumour type with grade III tumours being the commonest in both categories of patients. Perineural invasion was the only statistically significant pathologic parameter with age. EOBC was associated with higher DCIS component (24.8% vs 21.6%), lower hormone-receptor-positive status (52.30% vs 55.70%), higher proliferation index (Ki-67 > 20: 82.40% vs 80.30%) and a higher number of involved lymph nodes (13.80% vs 9.00%). Triple-Negative Breast cancer (26.40% vs 24.30%) was the most predominant molecular subtype of EOBC. CONCLUSION: EOBCs in our setting are generally more aggressive with poorer prognostic histopathological and molecular features when compared with LOBCs. A larger study is recommended to identify the association between relevant pathological features and early onset breast cancer in Ghana. Again, further molecular and genetic studies to understand the molecular genetic drivers of the general poorer pathological features of EOBCs and its relation to patient outcome in our setting is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".