Imaging features and conspicuity of invasive lobular carcinomas on digital breast tomosynthesis
Bibliographic record
Abstract
OBJECTIVE: To review the imaging features of invasive lobular carcinoma (ILC) seen on digital breast tomosynthesis (DBT) in comparison with invasive ductal carcinoma (IDC), and to evaluate whether DBT could improve conspicuity and tumour size assessment of ILC in comparison with digital mammography (DM). METHODS: Institutional review board with waiver of informed consent was obtained for this retrospective study. Patients with ILC or IDC who underwent DBT and DM at the time of diagnosis were included. DM and DBT images were reviewed in consensus by two breast radiologists in order to assess imaging features, conspicuity and maximum tumour diameter of ILC and IDC. Pathology on the surgical specimen was considered the standard of reference for assessment of tumour size. RESULTS: 43 patients (20 patients with ILC and 23 patients with IDC) were included. On DBT, compared with IDC, ILC presented less frequently as masses (40% vs 78%) (p = 0.01) and more frequently as isolated distortion (20% vs 0%) (p = 0.03). ILC presented more often as asymmetries (60%) than masses (20%) on DM (p = 0.02) but not on DBT (35% vs 40%; p = 1.00). Conspicuity of ILC was significantly higher on DBT than on DM (p = 0.002), while the difference between the two techniques was not significant for IDC (p = 0.2). Regarding ILC, concordance in tumour size measurement between DBT and pathology was fair (intraclass correlation coefficient = 0.24). CONCLUSION: ILC rarely presented as dense masses but frequently demonstrated architectural distortion on DBT. DBT increased lesion conspicuity but failed to accurately assess tumour size of ILC. Advances in knowledge: (1) This study describes specific features of ILC on DBT. (2) It shows that DBT can improve conspicuity of ILC.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| 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".