The R.O.A.D. to precision medicine Journal Article
| Authors: | Bertsimas, D.; Koulouras, A. G.; Margonis, G. A. |
| Article Title: | The R.O.A.D. to precision medicine |
| Abstract: | We propose a novel framework that addresses the deficiencies of Randomized clinical trial data subgroup analysis while it transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data through a two-step process that adjusts predicted outcomes under treatment. These adjusted predictions train decision trees, optimizing treatment assignments for patient subgroups based on their characteristics, enabling intuitive treatment recommendations. Implementing this framework on gastrointestinal stromal tumors (GIST) data, including genetic sub-cohorts, showed that our tree recommendations outperformed current guidelines in an external cohort. Furthermore, we extended the application of this framework to RCT data from patients with extremity sarcomas. Despite initial trial indications of universal treatment necessity, our framework identified a subset of patients who may not require treatment. Once again, we successfully validated our recommendations in an external cohort. © The Author(s) 2024. |
| Keywords: | adult; cancer survival; controlled study; treatment outcome; aged; major clinical study; adjuvant therapy; cancer radiotherapy; follow up; sensitivity and specificity; evidence based medicine; treatment indication; gastrointestinal stromal tumor; imatinib; platelet derived growth factor alpha receptor; gastrointestinal stromal tumors; randomized controlled trial; cohort analysis; soft tissue sarcoma; cross-sectional study; observational study; patient treatment; liposarcoma; biological factor; recurrence free survival; randomized clinical trials; decision tree; machine learning; local recurrence free survival; medicinal chemistry; extremity sarcoma; human; male; female; article; overtreatment; personalized cancer therapy; observational data; 'current; two-step process; treatment effect heterogeneity |
| Journal Title: | npj Digital Medicine |
| Volume: | 7 |
| ISSN: | 2398-6352 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2024-11-03 |
| Start Page: | 307 |
| Language: | English |
| DOI: | 10.1038/s41746-024-01291-6 |
| PROVIDER: | scopus |
| PMCID: | PMC11532393 |
| PUBMED: | 39489814 |
| DOI/URL: | |
| Notes: | Source: Scopus |
