ReviewNature reviews. Cancer2022
Harnessing multimodal data integration to advance precision oncology.
Review in Nature reviews. Cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 306 papers, 3 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
306 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- [Liquid Biopsy Revolutionizes the Precise Management of Tumors Across the Entire Course: Current Situation and Future Prospects].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Pooled it
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Morphology Predicts Grade, Transcriptomics Predicts Nodal Status: Task-Dependent Modality Contributions in Multimodal Prostate Cancer Classification.Journal of imaging · 2026Article
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Special Issue 'Molecular Mechanisms and New Markers of Cancer'-From Static Biomarkers to Context-Aware Precision Oncology.International journal of molecular sciences · 2026Article
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics.Briefings in bioinformatics · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
- Deep integration of clinical metadata with [European journal of nuclear medicine and molecular imaging · 2026Article
- Toward uncertainty-aware clinical decision support for treatment response prediction in metastatic NSCLC: integrating FDG-PET, T-cell repertoire, and cytokines with conformal prediction.Research square · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.Life (Basel, Switzerland) · 2026Review
- Review
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
246 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.
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.