Biomedical informatics and panomics for evidence‐based radiation therapy
Bibliographic record
Abstract
More than half of all cancer patients receive ionizing radiation as part of their treatment. Treatment outcomes are determined by complex interactions between cancer genetics, treatment regimens, and patient‐related variables. A key component of modern radiation oncology research is to predict at the time of treatment planning or during the course of fractionated radiation treatment, the probability of tumor eradication and normal tissue risks for the type of treatment being considered for the individual patient. A typical radiotherapy treatment scenario can generate a large pool of panomics data that may comprise 3D/4D anatomical and functional imaging information (noted as radiomics), in addition to biological markers (genomics, proteomics, metabolomics, etc.) derived from peripheral blood and tissue specimens. Radiotherapy data informatics constitutes a unique interface between physical and biological processes. It can benefit from the general advances in biomedical informatics research while still requires the development of its own technologies within this framework to address specific issues related to its unique physics–biology interface. We review recent advances and discuss current challenges to interrogate panomics data in radiotherapy using bioinformatics tools for data aggregation, sharing, visualization, and outcomes modeling. We provide examples based on our and others experiences using systems radiobiology and machine learning to develop predictive models of outcomes in radiotherapy. We also highlight the potential opportunities in this field for evidence‐based personalized medicine research for bioinformaticians and clinical decision‐makers. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Health Care
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".