Machine learning-based prediction of molecular subtypes of breast cancer using DCE MRI
Bibliographic record
Abstract
Breast cancer is a prevalent disease that can be classified into four molecular subtypes based on genetic and molecular markers. This study aimed to develop a machine learning-based approach to classify molecular subtypes of breast cancer using radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE MRI). The comprehensive dataset used in this study included 4428 radiomics features per patient, as well as clinical features, making it a valuable resource for future research. Our methodology involved several stages, including image preprocessing, feature extraction, initial and final feature selection, and data cleaning techniques, such as data imputation and Local Outlier Factor (LOF), to ensure the quality of the dataset. We conducted hyperparameter tuning and robustness analysis to optimize the performance of the machine learning algorithms. The results were evaluated in three scenarios: 4-label classification, binary, and 3-label classifications. Our approach achieved up to 85% F1 score in binary classifications and improved the overall accuracy of classifying the four molecular subtypes of breast cancer by 12%, which represents a significant improvement over the original study. These findings suggest that machine learning algorithms can be a powerful tool for improving the diagnosis and treatment of breast cancer, paving the way for personalized medicine approaches. Furthermore, the proposed approach can be applied to other datasets and may be useful in other areas of medical research that rely on radiomics features extracted from medical images.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".