Lymphopenia Induced by Different Neoadjuvant Chemo-Radiotherapy Schedules in Patients with Rectal Cancer: Bone Marrow as an Organ at Risk
Bibliographic record
Abstract
Radiotherapy (RT)-induced lymphopenia may hinder the anti-tumor immune response. Preoperative RT or chemo-RT (CRT) for locally advanced rectal cancer is a standard therapeutic approach, while immunotherapy has been approved for mismatch repair-deficient rectal tumors. We retrospectively analyzed 98 rectal adenocarcinoma patients undergoing neoadjuvant CRT with VMAT (groups A, B, C) or IMRT (group D) techniques, with four different RT schemes: group A (n = 24): 25 Gy/5 Gy/fraction plus a 0.2 Gy/fraction rectal tumor boost; group B (n = 22): 34 Gy/3.4 Gy/fraction, with a 1-week treatment break after the first five RT fractions; group C (n = 20): 46 Gy/2 Gy/fraction plus a 0.2 Gy/fraction rectal tumor boost; group D (n = 32): 45 Gy/1.8 Gy/fraction followed by 5.4 Gy/1.8 Gy/fraction to the rectal tumor. We examined the effect of the time-corrected normalized total dose (NTD-T) to the BM on lymphopenia. Groups A and B (hypofractionated RT) had significantly higher lymphocyte counts (LCs) after RT than groups C and D (p < 0.03). An inverse association between the LCs after RT and NTD-T was demonstrated (p = 0.01). An NTD-T threshold of 30 Gy delivered to 30% of the BM volume emerged as a potential constraint for RT planning, which could be successfully integrated in the RT plan. Hypofractionated and accelerated RT schemes, and BM-sparing techniques may reduce lymphocytic damage and prove critical for immuno-RT clinical trials.
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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".