Functional imaging of neoadjuvant chemotherapy response in women with locally advanced breast cancer using diffuse optical spectroscopy
Bibliographic record
Abstract
3591 Background: Functional imaging with tomographic near infrared diffuse optical spectroscopy (DOS) can quantitatively measure tissue parameters such as the concentration of deoxy-hemoglobin (Hb), oxy-hemoglobin (HbO2), percent water (%water), and scattering power (SP). The purpose of this study was to evaluate the correlation between DOS functional parameters with pathologic outcomes. Methods: Patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy or chemoradiotherapy were recruited to this study (n=10). Five scans were conducted per patient: a baseline scan taken up to 3 days prior to treatment and at 1 week, 4 weeks, 8 weeks, and after neoadjuvant treatment prior to surgery. Pulsed near-infrared laser light was used to scan the suspended breast at four different wavelengths and data was used for tomographic reconstruction. Volume-of-interest (VOI) weighted tissue Hb, HbO2, %water, and SP corresponding to the tumour was calculated and compared to pathological response as determined from full mount mastectomy specimens. Results: For all 10 patients the tumour-based VOI was significantly different than background tissue for all functional parameters (p<0.001). Five patients had a good pathologic response. Four patients were considered non-responders. One patient initially had a poor clinical response to chemotherapy but after a change in chemotherapy had a good clinical response. Responders and non-responders were significantly different for all of the functional parameters (p<0.05) at the 4 week scan. In the 5 patients with a good response the mean drop in Hb, HbO2, %water, and SP from baseline to the 4 week scan was 70.4% (SD=18.6), 66.5% (SD=24.5), 59.6% (SD=30.9), and 60.7% (SD=29.2), respectively. In contrast, the 4 non- responders had a mean drop of 17.7% (SD=9.8), 18.0% (SD=20.8), 15.4% (SD=11.7), and 12.6% (SD=10.2), for Hb, HbO2, %water and SP, respectively. Conclusions: Functional imaging using tomographic DOS parameters of Hb, HbO2, %water and SP could be used as an early detector of final pathologic tumour response. This could be evaluated in the future to assess response and potentially adjust chemotherapy regimens. No significant financial relationships to disclose.
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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) | 0.001 | 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".