Diagnostic value of a power Doppler ultrasound-based malignancy index for differentiating malignant and benign solid breast lesions
Bibliographic record
Abstract
BACKGROUND: Power Doppler ultrasound (PDUS) can provide useful information regarding the vascularity of breast lesions. The aim of this study was to investigate the diagnostic performance of a new PDUS-driven malignancy index in differentiating between malignant and benign causes of solid breast lesions. MATERIALS AND METHODS: Patients with solid breast lesions were enrolled consecutively and evaluated first by PDUS and subsequently by histopathologic assessment after undergoing surgical biopsy. A custom-made software was used to extract data from images for calculating malignancy index formula. RESULTS: A total of 87 patients with solid breast lesions were enrolled. Histopathologic evaluation identified 49 patients as benign and 38 patients as malignant. Malignancy index was significantly higher in the malignant group as compared to benign tumors (6.31 vs 0.30,P < 0.001). Area under the receiver operating characteristics (ROC) curve (AUC) was 0.98 (95% confidence interval (CI) 0.95-1.00). According to the ROC curve analysis, the cut-off point of 1.23 for malignancy index had a sensitivity and specificity of 94.7% (95% CI 82.2-99.3) and 94.0% (95% CI 83.1-98.7), respectively. CONCLUSION: Comparing with the histopathologic evaluation as the gold standard for diagnosing breast lesions, PDUS-driven malignancy index was shown to have a high discriminative performance in identifying malignant lesions with high sensitivity, specificity, and diagnostic accuracy. The noninvasive nature of PDUS is an important advantage that could prevent unnecessary biopsies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
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".