[18F]FDG PET/CT Radiomics in Untreated Breast Carcinoma: A Review of the Current State and Future Directions
Bibliographic record
Abstract
Background/Objectives: [18F]FDG PET/CT radiomics could improve risk stratification in untreated breast carcinoma. Methods: PubMed Central was accessed for full-text English articles (2015–2025) evaluating radiomic features from pretreatment [18F]FDG PET/CT. The Newcastle-Ottawa Scale (NOS) was used to evaluate the risk of bias. Results: Seven studies (1394 patients with a median cohort of about 150 patients) met the inclusion criteria. Radiomics outperformed conventional metabolic measures at predicting pCR to NAC (with the best AUC 0.83 when combining intra- and peritumoral features); differentiating molecular subtypes (AUC 0.856 luminal vs. non-luminal; 0.818 HER2+ vs. HER2−, and 0.888 triple negative vs. others); and assessing androgen receptor (AR) expression. No additional value was found for ER/PR status. Age influenced SUV and texture metrics, especially in triple-negative lesions. Methodological variation was notable: all studies were retrospective, the majority were single-center, only two provided external validation with different protocols of acquisition and segmentation, and at least four distinct software platforms were used for feature extraction and statistical analysis. Conclusions: [18F]FDG PET/CT radiomics shows good potential for predicting neoadjuvant response and molecular profile in breast cancer. However, small, diverse cohorts and non-standardized methodologies limit the evidence. Prospective multicenter studies with standardized acquisition, segmentation and feature extraction are required before clinical use.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".