Quantitative ultrasound spectroscopy and a kernel-based metric in clinical cancer response monitoring
Bibliographic record
Abstract
In this study, a metric based on Hilbert-Schmidt independence criterion (HSIC) is introduced in conjunction with quantitative ultrasound (QUS) spectroscopy methods for cancer response monitoring in locally advanced breast cancer (LABC) patients receiving neoadjuvant chemotherapy. Midband fit spectral parametric maps were computed using QUS radiofrequency data, which were obtained from 56 LABC patient before treatment and at three different times during the course of chemotherapy, i.e., on weeks 1, 4, and 8. Histograms of intensities were computed using 2D parametric maps to represent the images. Subsequently, the baseline features, i.e., the features extracted from “pre-treatment” parametric maps, were compared with those extracted from the parametric maps during the course of treatment using a kernel-based metric as an indication of chemotherapy effectiveness. As a result, dissimilarity measures were obtained between “pre-” and “during-treatment” images, which were used in a supervised learning paradigm to estimate whether a patient is a responder or a non-responder. High accuracy, sensitivity, and specificity were obtained on weeks 1 and 4, which demonstrated that the proposed system can effectively discriminate between the two patient populations early after start of treatment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".