Diffuse Optical Spectroscopy Evaluation of Treatment Response in Women with Locally Advanced Breast Cancer Receiving Neoadjuvant Chemotherapy
Bibliographic record
Abstract
The aim of this study was to investigate the potential of diffuse optical spectroscopy for monitoring of patients with locally advanced breast cancer (LABC) undergoing neoadjuvant chemotherapy. Fifteen women receiving treatment for LABC had the affected breast scanned before; 1 week, 4 weeks, and 8 weeks after treatment initiation; and before surgery. Optical properties related to tissue microstructure and biochemical composition were obtained. Clinical and pathologic tumor response was evaluated using whole-mount pathology after mastectomy. Patients who responded to treatment demonstrated an initial increase followed by a drop in optical parameters measured in the whole breast, whereas nonresponding patients demonstrated only a drop in the same parameters 1 week after treatment initiation. Responding patients demonstrated a significant increase of 17% ± 7%, 8% ± 8%, 10% ± 7%, 11% ± 11%, and 16% ± 15% in deoxygenated hemoglobin, oxygenated hemoglobin, total hemoglobin concentrations, water percentage, and tissue optical index, 1 week after treatment initiation, respectively. In contrast, nonresponding patients had a decrease of 14% ± 9%, 18% ± 7%, 17% ± 7%, 29% ± 7%, and 32% ± 9% in their corresponding optical parameters. Deoxygenated hemoglobin concentration (with 100% sensitivity, 83% specificity) and water percentage (with 75% sensitivity, 100% specificity) were found to be the best predictors of treatment response at 1 week after starting treatment. The results of this study suggest that optical parameters can be potentially used to predict and monitor patients' responses to neoadjuvant chemotherapy and can form a basis for the customization of treatments in which inefficacious treatments can be switched to more efficacious therapies.
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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".