Abstract P2-02-10: Evaluation of apparent diffusion coefficient to predict grade, micro-invasion and invasion in DCIS
Bibliographic record
Abstract
Abstract PURPOSE: To evaluate the role of apparent diffusion coefficient (ADC) values in distinguishing DCIS grades and identifying the presence of micro-invasive/invasive disease. METHODS AND MATERIALS: REB approved study with informed consent obtained from 70 women (age 36-84) scheduled for core-biopsy with results of 71 non-invasive/high-risk breast lesions. All patients underwent surgery and were assessed pre-operatively using diffusion weighted (DWI)-MRI. Lesion size, morphology and ADC values were recorded. The Kruskal Wallis or one-way ANOVA test and Pearson correlation coefficient were used to study the association between ADC values and the analyzed MRI lesion characteristics. Logistic regression analysis was used to evaluate the ability of ADC values to predict the presence of invasion. RESULTS: Of 71 cases, 45.1% were imaged on a 3T magnet and 54.9% on 1.5 T. Final pathology demonstrated invasive cancer in 26.8%, micro-invasion in 18.3% and pure DCIS in 59.2%. On 3T, mean ADC value was 1.20 ×10-3 mm2/s ± 0.48 (SD) (range, 0.47 - 1.78 ×10-3 mm2/s) for non-high grade DCIS, 1.23 × 10-3 mm2/s ± 0.40 (SD) (range, 0.26 - 1.77 ×10-3 mm2/s) for high-grade DCIS, and 1.15 × 10-3 mm2/s ± 0.45 (SD) (range, 0.26 - 1.75 ×10-3 mm2/s) for invasive/microinvasive disease. On 1.5T, mean ADC value was 1.04 × 10-3 mm2/s ± 0.41 (SD) (range, 0.15 - 1.85 ×10-3 mm2/s) for non-high grade DCIS, 1.01 × 10-3 mm2/s ± 0.37 (SD) (range, 0.06 - 1.76 ×10-3 mm2/s) for high-grade DCIS, and 1.11 × 10-3 mm2/s ± 0.30 (SD) (range, 0.64 - 1.76 ×10-3 mm2/s) for invasive/microinvasive disease. Based on logistic regression analysis, mean ADC value was not a significant predictor for invasiveness using 1.5 T [OR = 2.6 (95% CI (0.409, 17.12)), p = 0.3] or 3T [OR = 0.4 (95% CI (0.076, 2.399)), p = 0.3] CONCLUSION: Mean ADC acquired using a 1.5T or 3T MRI was unable to predict high-grade or invasive disease in biopsy-proven DCIS lesions. Further work is exploring voxel-based approaches that may better appreciate tumor heterogeneity and identify sub-regions of tumor with these higher risk features. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-02-10.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".