Abbreviated breast MRI combining FAST protocol and high temporal resolution (HTR) dynamic contrast enhanced (DCE) sequence
Bibliographic record
Abstract
PURPOSE: We evaluated the diagnostic value of a high temporal resolution (HTR) dynamic contrast enhanced (DCE) sequence added to a FAST protocol. MATERIALS AND METHODS: 120 women (mean age = 55 years (28-88)) who underwent breast MRI between July 2016 and March 2017 and in whom a biopsy was performed (i.e., gold standard) (n = 179: 69 benign, 7 borderline and 103 malignant lesions) were retrospectively and consecutively included. Two readers classified lesions according to the Breast Imaging-Reporting and Data System (BI-RADS) by reading: a FAST protocol (T1W, T2W, T1W-fat saturated 2 min after injection) and then a FULL standard protocol. Independently they determined if lesions were visible and when (Time To Enhancement (TTE)) on the HTR-DCE sequence. An Abbreviated protocol was then built using data from the HTR-DCE sequence added to the FAST protocol. RESULTS: All lesions were visible with the FAST protocol. 171/179 (95.5%) lesions were detected by reading theHTR-DCE sequence. There were a higher number of cancers rated BI-RADS 3 (PPV of malignancy of 27.6% (8/29) in FAST versus 18.7% (3/16) FULL protocol). An early enhancement on the HTR-DCE sequence (TTE < 31 s) was associated with malignancy with an OR 5.6 (CI 95%: 3.3-20.4) (p < 0.0001). Adding a TTE < 31 s to FAST analysis (AUROC = 0.826) significantly improved lesion characterization with a diagnostic gain of 10.6% (19/179) lesions correctly reclassified (p = 0.0034) compared to FAST protocol; with shorter acquisition time (7 min 48 s versus 13 min 54 s). CONCLUSION: Adding an HTR-DCE sequence to a FAST protocol increases diagnostic performance reaching that of the FULL protocol while reducing acquisition time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".