ArticleImmuno-oncology technology2026
A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma.
Article in Immuno-oncology technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Attention-guided and MIL-constrained CycleGAN for high-fidelity virtual p16 and Ki-67 staining.Frontiers in medical technology · 2026Article
Corrections and comments
- Update of
Authors and funding
15 authors.
Funding
Abstract
Background: Surgical resection is the standard treatment for stage I lung adenocarcinoma, in most cases without additional systemic adjuvant treatment. A substantial proportion of stage I cases recur, with a 5-year survival rate <50%. Clinical data suggest that adjuvant treatment, including immune checkpoint inhibitor therapy, may improve survival in such recurrent cases. Previously evaluated predictors, including the International Association for the Study of Lung Cancer (IASLC) grading system applied to histological sections and transcriptomic profiles, have not been sufficiently accurate or consistent for risk stratification or to guide therapeutic intervention. We hypothesized that these diverse diagnostic measurements carry complementary information that may provide higher prognostic power when combined. Materials and methods: We developed PATH-ORACLE, a multimodal deep learning biomarker built on top of the prospectively validated transcriptomic-based Outcome Risk Associated Clonal Lung Expression (ORACLE) score, with the addition of routine histological sections processed by pretrained foundation models. Predictive performance was assessed in two independent cohorts. Results: The histology-only predictor outperformed automated IASLC grading and remained prognostic after adjustment for tumor size, grade and T-stage. PATH-ORACLE exceeded both constituent modalities, predicting 1-year recurrence with an area under the curve of 0.86 and 0.84 in the two independent validation cohorts, and separated high- and low-risk groups using a single prespecified cut-off. Conclusions: Extensive validation will be needed to convert PATH-ORACLE from a prognostic biomarker to a predictive biomarker that could be used to prioritize stage IB patients for adjuvant targeted therapy, chemotherapy, or immune checkpoint inhibitor therapy with or without liquid biopsy-based monitoring.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.