Correlations between the Mammographic Features of Triple-Negative and Triple-Positive Breast Cancer
Bibliographic record
Abstract
Purpose:To comparative analyze the mammographic findings and clinical characteristics of triple negative breast cancer (estrogen receptor [ER] negative, progesterone receptor [PR] negative, and human epidermal growth factor receptor2 [HER2] negative) and triple positive breast cancer (ER positive, PR positive, and HER2 positive). Materials and Methods: The immunohistochemistry results of 174 cases of TNBC and 97 cases of TPBC were reviewed. All of the patients had undergone mammography. Retrospectively evaluate the visibility, morphology, distribution and size of the lesions (masses and calcifications) and breast density on mammography of TNBC, and to compare with those of TPBC. The age onset and pathologic type were also reviewed. Results: TNBC more frequently presented as merely a mass (95/150[63.3%]) than TPBC (34/88 [38.6%]) (P<0.01). TNBC were less frequently associated with microcalcifications (33/150[22%]) than were TPBC (39/88 [44.3%]) (P<0.01). Mammographic density and lesion visibility were similar between the two immunophenotypes. The mean age of TNBC (52[32~87]) was older than that of TPBC (48[26~68]) (P=0.002). Infiltrating ductal carcinoma was the main pathologic type of both groups. Basal-like breast cancer accounted for 47.7% (83/174) of TNBC but didnt express in TPBC (0/97). Conclusion: The mammographic features of TNBC that lesions showed merely a mass with obscured margins, and less associated with microcalcifications might be useful to diagnose triple negative breast cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".