The Role of Ultrasound Features in Predicting the Breast Cancer Response to Neoadjuvant Chemotherapy
Bibliographic record
Abstract
Background Neoadjuvant chemotherapy (NACT) has become the standard of care for locally advanced breast cancer. This study investigates whether baseline ultrasound features can predict complete pathological response (pCR) after NACT. Methods This retrospective study was approved by the Institutional Review Board of King Abdulaziz University Hospital, Jeddah, Saudi Arabia, with a waiver of informed consent. Records of female patients aged over 18 years with locally advanced breast cancer treated with NACT from 2018 to 2020 were reviewed. Baseline ultrasound parameters were assessed, including posterior effect, echo pattern, margin, and maximum lesion diameter. Tumor grade and immunophenotype were documented from the core biopsy. pCR was defined as the absence of invasive residual disease in the breast and axilla. Univariate and multivariate analyses assessed the association between ultrasound features and pathological response. Results A total of 110 breast cancer cases were analyzed: 36 (32.7%) were estrogen receptor (ER)-positive/human epidermal growth factor 2 (HER-2) negative, 49 (44.5%) were HER-2 positive, and 25 (22.7%) were triple-negative (TN). A pCR was achieved in 20 (18%) of cancers. Lesion diameter was significantly different between pCR and non-pCR groups, 28.5 ± 12 mm versus 39 ± 18 mm, respectively, with an area under the curve (AUC) of 0.7, a confidence interval (CI) of 0.55-0.81, and a p-value of 0.01. No significant association was observed between ultrasound features, tumor grade, and immunophenotype with pCR. Conclusion Ultrasound features could not predict pCR. A smaller tumor diameter was the only significant factor associated with pCR. Further prospective studies combining imaging features from different modalities are needed to explore the potential of varying imaging features in predicting post-NACT pathological response more comprehensively.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 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".