Assessing residual cancer burden in breast cancer patients undergoing neoadjuvant chemotherapy using the OptiScan probe and machine learning
Bibliographic record
Abstract
Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) estimation is a critical prognostic tool used in breast cancer outcomes assessment. In this pioneering study, we introduce the OptiScan probe, an innovative machine-learning-based optical biosensor, to assess residual cancer burden in patients undergoing NAC. The study enrolled 32 patients (mean age: 61.8 years), with comprehensive data collected pre- and post-each NAC cycle. Using the Modified Diffusion Equation (MDE) algorithm and advanced regression analysis, we meticulously calculated the optical properties and generated functional images of both healthy and affected breast tissues. Leveraging these insights, a robust machine learning model was developed, harnessing optical parameter values and breast cancer imaging features to predict RCB values. The predictive model showcased exceptional performance, achieving an impressive accuracy of 96.875% and 96.88% sensitivity in predicting RCB based on optical property alterations. These findings underscore the potency of the OptiScan probe as a pivotal tool for assessing breast cancer response post-NAC. This innovative approach holds promise as a noninvasive and accurate method for monitoring patient responses, contributing to improved treatment decisions and patient outcomes. By combining the power of machine learning with cutting-edge optical imaging, this study marks a significant stride toward personalized and effective breast cancer management.
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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 | 0.000 | 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) | 0.000 | 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".