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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.26685 |
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| _version_ | 1866911351962599424 |
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| author | Yu, Xiyao Fu, Kai |
| author_facet | Yu, Xiyao Fu, Kai |
| contents | Background: Despite high in-silico performance (AUC >0.80), 85% of AI cancer biomarkers fail clinical translation, exposing a critical algorithm-to-outcome gap. Methods: We introduce the Algorithm-to-Outcome Concordance (AOC) framework, integrating model accuracy (AUC), clinical correlation (Corr), and trial heterogeneity. We validated AOC across 6 neoantigen vaccine trials (2017-2025) and 3 independent melanoma immunotherapy cohorts (n=188 patients). Results: AOC ranged 0.18-0.79 across trials, with failed trials (ORR <15%) showing AOC <0.40. External validation revealed unstable algorithm-outcome correlation (C-index: 0.49-0.61, p>0.05), demonstrating the necessity of explicit concordance assessment. Conclusions: AOC provides a quantitative framework for pre-trial risk assessment and adaptive trial design. Prospective validation is underway in KEYNOTE-942 extension studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying AI-to-Clinical Translation: The Algorithm-to-Outcome Concordance (AOC) Framework Yu, Xiyao Fu, Kai Quantitative Methods Background: Despite high in-silico performance (AUC >0.80), 85% of AI cancer biomarkers fail clinical translation, exposing a critical algorithm-to-outcome gap. Methods: We introduce the Algorithm-to-Outcome Concordance (AOC) framework, integrating model accuracy (AUC), clinical correlation (Corr), and trial heterogeneity. We validated AOC across 6 neoantigen vaccine trials (2017-2025) and 3 independent melanoma immunotherapy cohorts (n=188 patients). Results: AOC ranged 0.18-0.79 across trials, with failed trials (ORR <15%) showing AOC <0.40. External validation revealed unstable algorithm-outcome correlation (C-index: 0.49-0.61, p>0.05), demonstrating the necessity of explicit concordance assessment. Conclusions: AOC provides a quantitative framework for pre-trial risk assessment and adaptive trial design. Prospective validation is underway in KEYNOTE-942 extension studies. |
| title | Quantifying AI-to-Clinical Translation: The Algorithm-to-Outcome Concordance (AOC) Framework |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2510.26685 |