Abstract B66: Optical spectroscopy of the breast: Association between breast cancer risk factors and breast tissue composition
Bibliographic record
Abstract
Abstract B66 Understanding breast tissue development to identify when it is at high risk for tumorigenesis, particularly in younger women, can lead to increased prevention efforts. Current technologies to evaluate breast tissue are not an optimal option for young women due to high costs, invasiveness and risk of ionizing radiation. We are using a novel technique called optical spectroscopy (OS) to examine breast tissue. OS uses light at the red and near-infrared wavelengths shined through the breast and the light scattering and absorption spectra obtained provide information on the breast content of water, lipid, oxyhemoglobin and deoxyhemoglobin. OS measures have been shown to be related to quantitative mammographic measures (i.e. percent density) and association between mammographic density and breast cancer risk factors has been established previously. The objective of this study was to explore the relationship of OS with breast cancer risk factors (anthropometric measures, reproductive factors, hormonal factors, physical activity and family history). Women were recruited in three groups: nulliparous women aged 18-21 (group 1), nulliparous women aged 31-40 (group 2), and parous women aged 31-40 who had given birth prior to age 30 (group 3). All women completed a brief questionnaire on their breast cancer risk factors and underwent OS examination. Measurements were made at four standard positions on each breast for each woman (8 total). Principal components analysis (PCA) was used to derive the four main components from the spectral data and four scores were derived indicating the contribution of each of the four principal components to each individual’s spectra. The scores were averaged over all four positions on both breasts for each woman resulting in four scores, t1-t4, for each woman. We present results from an initial group of 259 women, 137 in group 1, 94 in group 2, and 28 in group 3. Linear regression models were used to look for associations between breast cancer risk factors and the t scores for all groups combined, in groups 2 and 3 combined and in group 1 alone. Multivariate linear regression models were created to adjust for BMI, ethnicity and group and parity differences where applicable. Preliminary results demonstrated that when considering all three groups together, t1 and t2 measures were associated with body mass index (p<0.0001); t2 (p=0.001),t3 (0.0005) and t4 (0.002) showed an association with ethnicity. In addition t3 was associated with physical activity in childhood (β=0.17(SE 0.09), p=0.05). T4 was inversely associated with age at menarche (β=-0.02 (SE 0.01), p=0.02), and positively associated with a first degree family history of breast cancer (β=0.09(SE 0.05), p=0.07) and age (β=0.01(SE 0.01), p=0.05). In group 1, t1 showed an inverse relationship with the luteal phase of the menstrual cycle (β=5.1(SE 2.3), p=0.03). In a combined analysis of groups 2 and 3, t2 was inversely associated with the luteal phase of the menstrual cycle (β=-0.34 (SE 0.14), p=0.02) and duration of hormonal contraceptive use (p=0.05). The association between breast cancer risk factors and OS measures from our analysis indicates that the different components of the breast tissue could be a potential indicator of high risk breast tissue. OS may be a useful non-invasive technique to determine breast tissue characteristics associated with breast cancer risk in young women. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B66.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".