ArticleNature cancer2025
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Mapping the frontiers: a bibliometric perspective on t cell-based immunotherapy in pancreatic cancer since the twenty-first century.Journal of cancer research and clinical oncology · 2025Pooled it
- AI Agents for Multimodal Oncology Diagnosis: Toward Transparent and Traceable Clinical Decision Support.JMIR cancer · 2026Review
- A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL.Blood advances · 2026Article
- Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis.Patterns (New York, N.Y.) · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Towards an ecosystem of clinical decision support tools for precision cancer medicine.NPJ precision oncology · 2026Review
- AI and Blood Banking: Predicting Transfusion Demand - A Systematic Review of Forecasting Approaches.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Review
- Network resilience of the human interactome to pan-cancer mutations reveals conserved pathway vulnerabilities.iScience · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- LANTERN-XGB: An Interpretable Multi-Modal Machine Learning for Improving Clinical Decision-Making in Lung Cancer.International journal of molecular sciences · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy.NPJ precision oncology · 2026Article
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- MMHC-OCPR: Prediction of Platinum Response and Recurrence Risk in Ovarian Cancer with Multimodal Deep Learning.Biomedicines · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
38 authors.
Funding
Abstract
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network's decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.
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.