Quantitative Ultrasound and Texture Predictors of Breast Tumour Response to Chemotherapy
Bibliographic record
Abstract
Previous studies have demonstrated that quantitative ultrasound (QUS)is an effective tool for monitoring breast cancer patients undergoing neo-adjuvant chemotherapy (NAC). Here, we demonstrate the clinical utility of pre-treatment and early stage treatment QUS texture features in predicting the response of breast cancer patients to NAC. Radiofrequency (RF)ultrasound data were acquired from 100 locally advanced breast cancer (LABC)patients prior to treatment, and during the first, fourth and eighth week of treatment. QUS Spectral and backscatter parameters were computed from regions of interest (ROI)in the tumour core and its margin. Subsequently, employing gray-level co-occurrence matrices (GLCM), four textural features and image quality features including core-to-margin ratio (CMR)and core-to-margin contrast ratio (CMCR)were extracted from the parametric images as potential predictive indicators. QUS results were compared with the clinical and pathological response of each patient determined at the end of their NAC. Results from the 100 patients indicate that a combined QUS and texture feature model demonstrated a favourable clinical and pathological based response prediction with area under the receiver operating characteristics curves (AUC)of 82%, 80%, 87%, and 92 % prior to treatment, during treatment at week 1, 4, and 8, respectively. Best results were obtained using a radial-basis-function support vector machine (RBF -SVM)machine learning algorithm. Only four features were selected in each binary response group classification. The findings of this study suggest that QUS features of a breast tumour are strongly linked to tumour responsiveness. The ability to identify patients that would not benefit from NAC would facilitate salvage therapy and clinical management that has minimum toxicity and maximum outcome in terms of patient survival.
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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 | 0.001 | 0.004 |
| 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".