Machine learning based on automated breast volume scanner (<scp>ABVS</scp>) radiomics for differential diagnosis of benign and malignant <scp>BI‐RADS</scp> 4 lesions
Bibliographic record
Abstract
Abstract BI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.001 | 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".