Breast density measurements with ultrasound tomography: A comparison with film and digital mammography
Bibliographic record
Abstract
PURPOSE: To investigate the use of the whole-breast sound speed measurement as a marker of breast density (BD), a known risk factor for breast cancer. METHODS: As part of an ongoing study of breast cancer detection, 249 patients were scanned with a clinical prototype that operates on the principles of ultrasound tomography. Typically, 40-100 sound speed tomograms were reconstructed from the scan data, corresponding to the entire volume of the breast of each patient. The data were used to estimate the volume averaged sound speed (VASS) of the breast for each patient. The corresponding mammograms were used to calculate mammographic percent density (MPD) using CUMULUS software. Film mammograms were available for 164 patients while 85 digital mammograms were available for the remaining patients. Standard statistical techniques were used to determine associations of breast sound speed with a variety of mammographic measures such as percent density, area of dense tissue, and area of nondense tissue. Furthermore, associations of breast sound speed with continuous variables such as age and weight and dichotomous variables such as parity and menopausal status were also assessed. RESULTS: VASS was found to be significantly associated with MPD. The Spearman correlation coefficient (r(s)) between VASS and MPD was found to be 0.77 and 0.71 for film and digital mammography, respectively. VASS was positively correlated with dense areas by mammography, both digital (r(s) = 0.46) and film (r(s) = 0.56). VASS was negatively associated with nondense area by mammography, both digital (r(s) = -0.58) and film (r(s) = -0.63). BD by all methods was less in postmenopausal than in premenopausal women. The MPD was lower in the postmenopausal group (by 6.6%, p < 0.08, for the digital group and 7.73%, p < 0.007, for the film group). The VASS was also lower in the postmenopausal group (by 15 m∕s, p < 0.001 for the digital group and 8 m∕s, p < 0.08, for the film group). The association of MPD with age was characterized with r(s) = -0.06 (p < 0.6) for digital mammography and r(s) = -0.53 (p < 0.002) for film mammography. For weight, the MPD associations were characterized by r(s) = -0.53 (p < 0.0001) for digital mammography and -0.38 (p < 0.0001) for film mammography. The association of VASS with age was r(s) = -0.33 (p < 0.002) for the digital group and -0.17 (p < 0.03) for the film group. For weight, the relationship was characterized with r(s) = -0.45 (p < 0.001) for the digital group and -0.37 (p < 0.0001) for the film group. CONCLUSIONS: The association between VASS and MPD is strong for both film and digital mammography, suggesting that VASS is a viable measure of breast density. This result sets the stage for future work that will focus on directly testing the association of VASS with breast cancer risk.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".