Functional imaging of neoadjuvant chemotherapy response in women with locally advanced breast cancer using diffuse optical spectroscopy
Bibliographic record
Abstract
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. 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 was taken up to 3 days prior to treatment and at 1 week, 4 weeks, 8 weeks, and after neoadjuvant treatment prior to surgery. At each scan the patient lay prone with the breast suspended between immobilization plates in optical coupling medium. Pulsed near-infrared laser light was used to scan the 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 clinical and pathological response as determined from full mount mastectomy pathology. 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 clinical and 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 and radiographic 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. Functional imaging using tomographic diffuse optical spectroscopy parameters of Hb, HbO2, %water and SP could be used as an early detector of final clinical and pathologic tumor response. This could be evaluated in the future to assess responses and potentially adjust chemotherapy regimins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".