Sci—Thur AM: YIS ‐ 02: Radiogenomic Modeling of Normal Tissue Toxicities in Prostate Cancer Patients Receiving Hypofractionated Radiotherapy
Bibliographic record
Abstract
Inter‐patient radiation sensitivity variability has recently been shown to have a genetic component. This genetic component may play a key role in explaining the fluctuating rates of radiation‐induced toxicities (RITs). Single nucleotide polymorphisms (SNPs) have thus far yielded inconsistent results in delineating RITs while copy number variations (CNVs) have not yet been investigated for such purposes. We explore a radiogenomic modeling approach to investigate the association of CNVs and SNPs, along with clinical and dosimetric variables, in radiation induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients treated with curative hypofractionated irradiation. A cohort of 62 prostate cancer patients who underwent hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 to 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Late toxicity rates for RB grade 2 & 3 and grade 3 alone were 29.0% and 12.9%, respectively. ED toxicity was found to be 62.9%. Radiogenomic model performance was evaluated using receiver operating characteristic area under the curve (AUC) and resampling by cross‐validation. Binary variables were evaluated using Chi‐squared contingency table analysis and multivariate models by Spearman's rank correlation coefficient (rs). Ten patients were found to have three copies of xrcc1 CNV (RB: χ 2 =14.6, p<0.001 and ED: χ 2 =4.88, p=0.0272) and twelve had heterozygous rs25489 SNP (RB: χ 2 =0.278, p=0.599 and ED: χ 2 =0.112, p=0.732). Radiogenomic modeling yielded significant, cross‐validated NTCP models for RB (AUC=0.665) and ED (AUC=0.754). These results indicate that CNVs may be potential predictive biomarkers of both late ED and RB.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".