Characterizing tumour heterogeneous response to chemotherapy using low-frequency ultrasonic spectroscopy
Bibliographic record
Abstract
Textural analysis techniques in conjunction with low-frequency ultrasonic spectroscopy have been proposed for characterization of heterogeneous responses developed within tumours undergoing chemotherapy. Such characterization can be beneficial for early appraisal of tumour responses to the cancer therapy. Breast xenograft tumour-bearing animals were treated with chemotherapy. Animals were assessed with low-frequency ultrasound data acquired at different times after chemotherapy exposure. Following imaging, tumours underwent standard histological analysis for detecting cell-death effects. Several average and texture-based parameters were derived from normalized ultrasonic spectral parametric maps. Statistically significant differences were revealed between parameters extracted from treated and untreated tumours 12-24 hours after exposure. Regression analyses were also performed in order to assess levels of correlation between non-invasive ultrasonic surrogates of therapy response and standard histological findings, where strong correlations were obtained with a maximum r2 value of 0.98. Obtained results demonstrate that ultrasound-based textural properties of tissue can be used for characterizing the micro-structure alterations and heterogeneous responses within tumours early on during treatment. This is an important observation which can be applied clinically for treatment response monitoring and predicting patient responses to therapy early after therapy initiation.
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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 | 0.000 | 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".