The Usefulness of Spectral Mammography in Surgical Planning of Breast Cancer Treatment—Analysis of 999 Patients with Primary Operable Breast Cancer
Bibliographic record
Abstract
Contrast-enhanced spectral mammography (CESM) is a promising, digital breast imaging method for planning surgeries. The study aimed at comparing digital mammography (MG) with CESM as predictive factors in visualizing multifocal-multicentric cancers (MFMCC) before determining the surgery extent. We analyzed 999 patients after breast cancer surgery to compare MG and CESM in terms of detecting MFMCC. Moreover, these procedures were assessed for their conformity with postoperative histopathology (HP), calculating their sensitivity and specificity. The question was which histopathological types of breast cancer were more frequently characterized by multifocality-multicentrality in comparable techniques as regards the general number of HP-identified cancers. The analysis involved the frequency of post-CESM changes in the extent of planned surgeries. In the present study, MG revealed 48 (4.80%) while CESM 170 (17.02%) MFMCC lesions, subsequently confirmed in HP. MG had MFMCC detecting sensitivity of 38.51%, specificity 99.01%, PPV (positive predictive value) 85.71%, and NPV (negative predictive value) 84.52%. The respective values for CESM were 87.63%, 94.90%, 80.57% and 96.95%. Moreover, no statistically significant differences were found between lobular and NST cancers (27.78% vs. 21.24%) regarding MFMCC. A treatment change was required by 20.00% of the patients from breast-conserving to mastectomy, upon visualizing MFMCC in CESM. In conclusion, mammography offers insufficient diagnostic sensitivity for detecting additional cancer foci. The high diagnostic sensitivity of CESM effectively assesses breast cancer multifocality/multicentrality and significantly changes the extent of planned surgeries. The multifocality/multicentrality concerned carcinoma, lobular and invasive carcinoma of no special type (NST) cancers with similar incidence rates, which requires further confirmation.
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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 | 0.001 |
| 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".