Histological Grade and Immunohistochemical Biomarkers of Breast Cancer: Correlation to Ultrasound Features
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study is to correlate various features of breast cancers on ultrasound to their histological grade and immunohistochemical biomarkers. METHODS: Seventy-three patients with 77 invasive breast cancers, diagnosed between August 2011 and December 2014, were included in this prospective analysis. Margin, posterior features, shape, and vascularity were determined from ultrasound and classified according to the Breast Imaging Reporting and Data System lexicon. Histological grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status (positive [+] or negative [-]) were determined from surgical pathology reports. The cancers were categorized into low grade (grades 1 or 2) and high grade (grade 3). Correlation of ultrasound features of the cancers to their histological grade and receptor status was performed. RESULTS: There were 47 low-grade and 29 high-grade cancers. There was a significant difference in margin and posterior features between the low and high grade, ER + and ER-, and PR + and PR- (all P < .05), but not between HER2 + and HER2- cancers (both P > .05). There was no significant difference in shape and vascularity among the different subtypes (all P > .05). Spiculated margin was significantly associated with low-grade, ER+, PR + status; angular margin with high grade; microlobulated margin with ER- status; shadowing with PR + status; and enhancement with high grade, ER- status (all P < .05, all odds ratios ≥ 3.94). CONCLUSIONS: There was significant association of margin and posterior features of breast cancers with their histological grade and receptor status.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".