Using ensemble learning and genetic algorithm on magnetic resonance imaging radiomics to classify molecular subtypes of breast cancer
Bibliographic record
Abstract
Abstract Breast cancer (BRCA) is one of the most frequent malignant tumors with the highest incidence of cancer and the second most common oncologic cause of death in women. BRCA can be classified into different molecular subtypes, such as basal‐like, represented by triple‐negative BRCA (estrogen receptor [ER] negative, progesterone receptor [PR] negative, and human epidermal growth factor receptor 2 [HER‐2] negative). This study aims to determine whether radiomics features extracted from magnetic resonance imaging (MRI) could be used to distinguish various BRCA molecular subtypes. This study retrospectively collected a dataset of 922 BRCA patients with MRIs and experimental genomic profiles. A genetic algorithm is then employed to select the optimal MRI features for each subproblem. Subsequently, stacking ensemble learning is implemented to learn these features and generate the prediction outcomes. Our model showed a significant performance of 0.700, 0.732, and 0.642 (area under the curve; AUC) in predicting ER, PR, and HER‐2 statuses. For multiclassification of Luminal A, Luminal B, HER2, and TNBC, the AUCs reached 0.672, 0.624, 0.639, and 0.669, respectively. Our model is superior in most subtypes compared to the state‐of‐the‐art predictors on the same dataset. In conclusion, genetic algorithm and ensemble learning can be suitable for BRCA subtype classification with high performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".