Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer
Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.
Authors
- Dóra Lakatos (ORCID: https://orcid.org/0000-0002-6461-5800)
- Júlia Déri
- Richard B. Schwab (ORCID: https://orcid.org/0000-0002-0488-1226)
- István Vályi‐Nagy (ORCID: https://orcid.org/0000-0003-3581-113X)
- Ákos Boldizsár (ORCID: https://orcid.org/0000-0002-0729-9944)
- Gábor Kalmár
- Réka Szalkai-Dénes
- Dóra Mathiász
- Róbert Dóczi (ORCID: https://orcid.org/0000-0001-5305-8036)
- István Peták (ORCID: https://orcid.org/0000-0003-0422-9286)
- Maud Kamal (ORCID: https://orcid.org/0000-0003-1466-0950)
- László Urbán (ORCID: https://orcid.org/0000-0002-1275-0293)
- Dóra Kormos
- Christophe Le Tourneau (ORCID: https://orcid.org/0000-0001-9772-4686)
- Viktor Kardos
- Barbara Vodicska (ORCID: https://orcid.org/0000-0003-1191-234X)
- Anna Dirner (ORCID: https://orcid.org/0000-0002-8032-7104)
- Christian Diego Rolfo (ORCID: https://orcid.org/0000-0002-7860-8417)
- Edit Várkondi
- Dóra Tihanyi (ORCID: https://orcid.org/0000-0003-0213-7273)
- Gábor Pajkos
- Arkadiusz Z. Dudek (ORCID: https://orcid.org/0000-0002-9114-8945)
- Márton Bolyácz
- Mária Kocsis-Steinbach
- Ákos Takács
Institutions
- Semmelweis University (HU)
- Mayo Clinic (US)
- University of Matanzas (CU)
- The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US)
- University of Illinois Chicago (US)
- National Institute of Oncology (HU)
- International Drug Development (FR)
- Genomic Health (United States) (US)
- Széchenyi István University (HU)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1038/s41698-025-00943-4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal