The Efficacy of MRI-Based ADC Measurements in Detecting Axillary Lymph Node Metastasis: Evaluation of a Prospective Study
Bibliographic record
Abstract
Objective: This study aimed to evaluate the efficacy of MRI-based Apparent Diffusion Coefficient (ADC) measurements in detecting axillary lymph node metastasis in breast cancer patients. By comparing preoperative MRI findings with intraoperative sentinel lymph node biopsy (SLNB) and postoperative pathological results, we sought to explore the potential of ADC values as a non-invasive alternative to axillary interventions. Methods: A total of 104 female patients diagnosed with breast cancer between 2019 and 2021 were included in this prospective study. ADC values of axillary lymph nodes, tumors, and muscle tissues were measured using a 3T MRI system. The correlation between these measurements and pathological outcomes was analyzed. Statistical analyses, including t-tests, ANOVA, and ROC curve analysis, were employed to assess the diagnostic performance of ADC values. Results: The results indicated that, while the mean ADC values of metastatic lymph nodes were lower than those of benign nodes, the sensitivity and specificity of MRI-based ADC measurements were inferior to the expected standards. The tumor ADC value and the tumor-to-lymph node ADC ratio were found to be more reliable indicators of metastasis than the lymph node ADC value alone. The diagnostic power of the tumor ADC value was significant, with a sensitivity of 75% and a specificity of 73%. Conclusions: MRI-based ADC measurements, particularly the tumor ADC value and the tumor-to-lymph node ADC ratio, show promise as potential non-invasive markers for axillary lymph node metastasis in breast cancer patients. However, the current results suggest that ADC measurements cannot yet replace SLNB in clinical practice.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".