BI-RADS assessment of solid breast lesions based on quantitative ultrasound and machine learning
Bibliographic record
Abstract
Due to promising results of shear wave ultrasound (US) elastography (SWE) and homodyned-K (HK) features in assessing solid breast lesions prior to biopsy, there is a high interest in developing a multi-parametric approach in this context. The goal was to assess the added value of quantitative US (QUS) parameters to SWE and BI-RADS category, based on random forests classifier. US evaluation of breast lesions was performed as per standard clinical practice. For women who gave written consent, 3 SWE images were obtained with an Aixplorer US system (Supersonic Imagine, Aix-en-Provence, France) using a SL15-4 probe. Then, a 1-second cine-loop of radiofrequency images was acquired with the same system and probe, using a different acquisition mode. Then, the radiologist performed percutaneous biopsy as per standard procedure. Breast pathologists performed histopathology analyses. Lesions were anonymized; data analysis was performed on a workstation. The maximum elasticity Emax on each of three regions-of-interest (ROI) - supra-, intra-, and infratumoral - was computed directly from acquired SWE images, for a total of 3 features. For each of the three ROIs on B-mode images, the following HK parametric maps were considered based on previous work: 1) the infratumoral total signal power normalized by the maximal intensity in the ROI; 2) the intratumoral reciprocal of the scattering clustering parameter; 3) the supratumoral coherent-to-diffuse signal ratio; and 4) the supratumoral diffuse-to-total signal power ratio. Eight features were extracted from these maps. The total attenuation coefficient slope, assessed with the spectral fit method, was also computed in the intra- and infratumoral ROIs, for a total of 13 features. One hundred and three women with suspicious solid breast lesions (BI-RADS categories 4-5) were enrolled. When considering all types of features for these 103 lesions, specificity was 55.9% at 98.4% sensitivity. In contrast, BI-RADS category alone yielded a specificity of 27.5% at 97.5% sensitivity, showing the added value of the machine learning classification. Combination of SWE and QUS features yielded a specificity of 16.3% at 97.6% sensitivity.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".