STIFMap employs a convolutional neural network to reveal spatial mechanical heterogeneity and tension-dependent activation of an epithelial to mesenchymal transition within human breast cancers
Bibliographic record
Abstract
Abstract Intratumor heterogeneity in breast cancer associates with poor patient outcome. Tissue fibrosis and stromal stiffening accompany breast cancer development and associate with the aggressiveness of human breast cancer subtypes. Whether human breast cancers demonstrate stiffness heterogeneity, and if this is linked to breast tumor aggression remains unclear. To answer these questions, we developed a spatial method to measure the stiffness heterogeneity in human breast tumor tissues that also quantifies the local stromal stiffness each cell experiences and permits correlation with biomarkers of tumor aggression. Here, we present Spatially Transformed Inferential Force Maps (STIFMaps) to predict the elasticity across whole tissue sections with micron-resolution. The method exploits computer vision to precisely automate AFM indentation and then uses a trained convolutional neural network to predict matrix elasticity using collagen morphological features and ground truth AFM data. Because STIFMaps is compatible with biomarker staining we used the approach to register high-elasticity regions within sections of human breast tumors with markers of mechanical activation and an epithelial to mesenchymal transition (EMT) that associated with tumor aggression. The findings herein highlight the utility of STIFMaps for assessing the mechanical heterogeneity of human breast tissues across length scales from single cells to whole tissues. The method also reveals, for the first time, a direct association between stromal stiffness and EMT, thereby implicating stromal stiffness as a driver of human breast cancer aggression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".