WE‐C‐BRA‐09: Towards Treatment Monitoring of Tumour Radiation Response with Raman Spectroscopy
Bibliographic record
Abstract
Purpose: To use single‐cell Raman spectroscopy (RS) and principal component analysis (PCA) for early treatment monitoring of radiation response. Methods: Two human tumour cell lines, one known to be radioresistant (H460, SF2 = 0.64) and one radiosensitive (LNCaP, SF2 = 0.27), were treated with daily 2 Gy fractions of 6 MV photons, up to a maximum total dose of 10 Gy. Irradiated and unirradiated cultures were harvested and analyzed with RS each day after the previous daily fraction, and also at 2 and 3 days after the final fraction. Single‐cell Raman spectra were acquired from 20 cells per sample with a Raman microscope utilizing a 785 nm excitation laser. All spectra (280 per cell line) were post‐processed, and the total data set for each cell line was analyzed with PCA using standard algorithms. Results: One radiation‐induced PCA component was detected for each cell line by identification of statistically significant changes in the PCA score distributions for irradiated samples, as compared to unirradiated samples, at time points after the start of radiation treatment. The radioresistant cells (H460) exhibit a strong RS molecular radiation response signature, detectable as early as 2 days after the start of treatment, of which radiation‐induced glycogen synthesis is a significant contributor. The radiosensitive cells (LNCaP) exhibit a weak molecular radiation response signature that is not correlated with the radioresistant cell response, with no contributions from glycogen. The glycogen synthesis observed in the radioresistant cells is confirmed via Western blotting to be the result of radiation‐induced activation of the phosphatidylinositol‐3‐kinase/Akt (PI3K/Akt) signalling pathway, a molecular radiation response known to promote cell survival and confer radiation resistance. Conclusions: This work supports the feasibility of using RS for early treatment monitoring of tumour radiation response, leading to personalized therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".