Strain Elastography & Tissue Characterisation as a Tool to Differentiate Tumor from Healthy Tissue
Bibliographic record
Abstract
The purpose of this study was to compare diagnostic values of normal and effected tissues with two techniques using strain elastography and tissues characterization. This study was carried out on a breast phantom containing all human body parameters. Analysis was performed using a lone phantom to correlate a relation between the values of Strain Elastography (SE) and first order texture parameters results. For SE SonixTouch Q+ (Ultrasonix Medical Corporation, 130-4311 Viking Way, Richmond, Canada) device using a linear-array ultrasound probe at a frequency of 10MHz with a gain of 40%. Elastography breast phantom was purchased from CAE healthcare USA, 3600 Edgelake Drive Sarasota FL, USA. For tissue characterization a Region of Interest (ROI) that encompasses both (normal and stiffer) areas were selected. MAZDA software was used to carry out the image analysis (mean and variance) of the tumour and healthy tissue, ROI of 1600 pixels at both regions was selected. An affirmative and resilient outcome was observed between the numerals of normal and tumor tissues, both for SE and first order texture parameters values. After our study we suggest that SE and tissue characterisation via first order texture parameter is a reliable technique to highlight normal and tumor tissue (with respect to same reference, for SE technique only). SE and first order texture parameters (mean and variance) paved way in highlighting the breast tumors fully. It is suggested that SE being more reliable approach in determining the stiffness for breast lesion, as it produces the results with real time imaging. However texture parameter gives an objective assessment of the image with a discriminating feature of the tissue.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".