The Society for Immunotherapy of Cancer Perspective on Liquid Biopsy– and Radiomics-Based Technologies for Immuno-oncology Biomarker Discovery and Application
Bibliographic record
Abstract
Immuno-oncology is increasingly becoming the standard of care for cancers, with the identification of biomarkers that reliably classify immune checkpoint inhibitor response, resistance, and toxicity becoming the next frontier toward improvements in immunomodulatory treatment regimens. Recent advances in multiparametric, multiomics, and computational data platforms generating an unprecedented depth of data may assist in the discovery of increasingly robust biomarkers for enhanced patient selection and more personalized or longitudinal treatment approaches. Which emerging technologies to implement in future research and clinical settings, used alone or in combination, relies on weighing the pros and cons that aid in maximizing data outputs while minimizing patient sampling, with high reproducibility and representativeness, and minimal turnaround time and data fragmentation toward later private and public dataset harmonization strategies. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and highlight advances in biomarker discovery using liquid biopsy and in vivo imaging technologies. We address advances in liquid biopsy technologies monitoring cells, proteins, nucleic acids, antibodies, and drugs or analytes and radiomics technologies monitoring whole host-level imaging methods, including immuno-PET and MRI technologies, which are able to couple biomarkers with physical location. We include a summary of key metrics obtained by these technologies and their ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons. By highlighting some of the most interesting recent examples contributed by these technologies and providing examples of improved outputs, we hope to guide correlative research directions and assist in their becoming clinically useful in immuno-oncology.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.033 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".