Breast elastography: Identification of benign and malignant cancer based on absolute elastic modulus measurement using vibro-elastography
Bibliographic record
Abstract
Breast cancer is a public health problem with ~ 1.7 million new cases per year worldwide and with several limitations in the state-of-art screening techniques. Ultrasound elastography involves a set of techniques intended to facilitate the noninvasive diagnosis of cancer. Among these, Vibro-elastography is an ultrasound-based technique that employs external mechanical excitation to infer the elastic properties of soft tissue. In this paper, we evaluate the Vibro-elastography performance in the differentiation of benign and malignant breast lesions. For this study, a group of 18 women with clinically confirmed tumors or suspected malignant breast lesions were invited to participate. For each volunteer, an elastogram was obtained, and the mean elasticity of the lesion and the adjacent healthy tissue were calculated. After the acquisition, the volunteers underwent core-needle biopsy. The histopathological results allowed to validate the Vibro-elastography diagnosis, which ranged from benign to malignant lesions. Results indicate that the mean elasticity value of the benign lesions, malignant lesions and healthy breast tissue were 39.4 ± 12 KPa, 55.4 ± 7.02 KPa and 23.91 ± 4.57 kPa, respectively. The classification between benign and malignant breast cancer was performed using Support Vector Machine based on the measured lesion stiffness. A ROC curve permitted to quantify the accuracy of the differentiation and to define a suitable cutoff value of stiffness, obtaining an AUC of 0.90 and a cutoff value of 44.75 KPa. The results obtained suggest that Vibro-elastography allows differentiating between benign and malignant lesions. Furthermore, the elasticity values obtained for benign, malignant and healthy tissue are consistent with previous reports.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".