TH‐C‐213AB‐03: Bayesian Network Framework for Biophysical Radiation Pneumonitis Modeling
Bibliographic record
Abstract
Purpose: To build a biophysical risk model of radiation pneumonitis (RP) using a Bayesian Network (BN) framework for combining biomarker information with dosimetric parameters. Methods: 22 non‐small‐cell lung cancer patients who received radiotherapy (RT) between 2006 to 2009 were selected. Blood samples were collected from each patient before and during RT. From each sample the concentration of the following four candidate biomarkers were measured: alpha‐2‐macroglobulin (a2M), angiotensin converting enzyme (ACE), transforming growth factor ß(TGF‐ß), and interleukin‐6 (IL‐6). The biomarker information was reduced to a subset of variables whose linear combination showed the highest correlation with RP via logistic regression analysis. The selected biomarker variables were combined with two dosimetric known RP parameters (mean lung dose, tumor position in superior‐inferior direction) as features to learn a BN classifier for predicting RP onset. Predictive power of the learned BN classifier was compared with a logistic regression and Naïve Bayes classifiers in a simulated validation dataset. Results: The following 5 biomarker variables were selected for BN learning: 1) pre‐RT concentration level of a2M, 2) ratio of pre‐ to intra‐RT levels of a2M, 3) intra‐RT IL‐6 level, 4) ratio of pre‐ to intra‐RT levels of TGF‐β, and 5) pre‐RT ACE level. The learned BN structure identified the probabilistic relations amongst the 7 features and RP where the role of a2M as a mediator of biological interaction was noticed. Performance of the BN classifier was 0.873, 0.959, and 0.676 for overall classification accuracy, positive and negative predictive power, respectively. It outperformed a logistic regression and a Naïve Bayes counterpart in terms of overall accuracy and positive predictive value. Conclusions: The presented BN approach has a potential to enhance the prediction power of RP by explicitly modeling interactions between physical and biological variables, which can also be used to validate or guide further biomarker research of RP.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".