Evidence map›Paper›PMID 41646758›Full record

ArticlemedRxiv : the preprint server for health sciences2026

A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma.

Oz Kilim, Orsolya Pipek, Zsofia Sztupinszki, Miklos Diossy, Aurel Prosz, Cristina Naceur-Lombardelli, Selvaraju Veeriah, David Moore, Mariam Jamal-Hanjani, Allan Hackshaw and 5 more

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Oz KilimDepartment of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.
Orsolya PipekDepartment of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.
Zsofia SztupinszkiComputational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Miklos DiossyComputational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Aurel ProszDanish Cancer Institute, Copenhagen, Denmark.
Cristina Naceur-LombardelliCancer Research Institute, University College London Cancer Institute, London, UK.
Selvaraju VeeriahCancer Research Institute, University College London Cancer Institute, London, UK.
David MooreCancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
Mariam Jamal-HanjaniCancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Allan HackshawCancer Research UK & UCL Cancer Trials Centre, London, UK.
Janos FillingerDepartment of Pathology, National Koranyi Institute of Pulmonology, Budapest, Hungary.
Judit Moldvay1 Department of Pulmonology, National Koranyi Institute of Pulmonology, Budapest, Hungary.
Istvan CsabaiDepartment of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.ORCID 0000-0001-9232-9898
Charles SwantonCancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
Zoltan SzallasiDepartment of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary.

Funding

The Impact of DNA Damage Repair Abnormalities in Prostate CancerP01CA228696 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI SOLIT, DAVID B. · 2019 to 2024
$8.7M
NCI NIH HHS P01 CA228696Wellcome Trust CC2041
6 · The paper itself

Abstract

The standard treatment for stage I lung adenocarcinoma is surgical resection, in most cases without additional systemic adjuvant treatment. A significant proportion of stage I cases recur with a less than 50% 5-year survival rate. There are clinical data suggesting that adjuvant treatment may improve survival in such recurrent cases. However, previously evaluated predictors such as the IASLC grading system from histological sections and transcriptomic profiles have not been sufficiently accurate and consistent for risk stratification and to guide therapeutic interventions. We hypothesized that these previously investigated diverse diagnostic measurements carry complementary information that may provide higher prognostic power when combined. Here we describe a multimodal deep learning method, PATH-ORACLE. This biomarker is built on top of the prospectively validated transcriptomic-based ORACLE score with the addition of routine histological sections processed by pre-trained foundation models. PATH-ORACLE predicts recurrence with an accuracy of over 85% in two independent cohorts. Given further validation this predictor could be used to prioritize stage IB patients for adjuvant chemotherapy in a more consistent fashion. Furthermore, for stage IA cases, PATH-ORACLE, combined with liquid biopsy-based monitoring may help identify high-risk patients suitable for adjuvant targeted therapy.

Indexed as

Early-stage Lung adenocarcinomaMultimodal deep learningReccurance prediction

Identifiers

PMID41646758
PMCPMC12870720

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.