Evaluating Apparent Diffusion Coefficient (ADC) as a Non-Invasive Imaging Biomarker for Breast Cancer Prognosis: Correlation with Histopathological and Molecular Biomarkers
Bibliographic record
Abstract
Abstract Background: This study evaluates the potential of apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) as non-invasive imaging biomarkers for breast cancer prognosis, correlating them with key histopathological and molecular features. Materials and Methods: In this prospective study, 35 patients with histologically confirmed breast cancer underwent 1.5T MRI, including DWI sequences. ADC values were measured from manually selected regions of interest, and their associations with prognostic markers—ER/PR status, HER2 expression, Ki-67 index, lymph node metastasis, tumor grade, and size—were statistically analyzed. Receiver operating characteristic (ROC) curves were used to determine diagnostic performance thresholds. Results: Significantly lower ADC values were observed in tumors with lymph node metastasis (P = 0.016), high Ki-67 expression (P = 0.042), and positive ER/PR status (P = 0.031). ROC analysis demonstrated high diagnostic performance of ADC for identifying metastatic lymph nodes (AUC = 0.879), ER/PR-positive tumors (AUC = 0.864), and Ki-67-positive tumors (AUC = 0.837). No significant correlations were found between ADC and HER2 status, tumor grade, or size. Conclusion: ADC values significantly correlate with several key prognostic factors in breast cancer, including hormone receptor status, tumor proliferation, and lymph node involvement. These findings highlight ADC as a promising non-invasive imaging biomarker for early risk stratification and treatment planning in breast cancer management. Larger multicenter studies are warranted to validate these results and support broader clinical application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 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".