ArticleNature cancer2022
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer.
Article in Nature cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07417800 (Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning), which is not on this map. Cited by 226 papers, 2 of them syntheses that pooled it.
What it found
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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.
Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning
Who cites it
226 citing papers in PubMed, 2 syntheses or guidelines pooled it, 337 citations in OpenAlex.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Noninvasive early identification of durable clinical benefit from immune checkpoint inhibition: a prospective multicenter study (NCT04566432).Signal transduction and targeted therapy · 2024Trial
- Dynamics of tumor in situ fluid circulating tumor DNA in recurrent glioblastomas forecasts treatment efficacy of immune checkpoint blockade coupled with low-dose bevacizumab.Journal of cancer research and clinical oncology · 2024Trial
- Early On-Treatment Assessment of T Cells, Cytokines, and Tumor DNA with Adaptively Dosed Nivolumab + Ipilimumab: Final Results from the Phase 2 ADAPT-IT Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Trial
- Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.Journal of cancer research and clinical oncology · 2024Trial
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Article
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Mutant p53 sensitizes KRAS-driven lung adenocarcinoma to immunotherapy by repressing SPP1.Science advances · 2026Article
- Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial.European radiology · 2026Article
- A multimodal murmuration for immunotherapy.Nature medicine · 2026Article
- Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC.Nature medicine · 2026Observational
- AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer.ESMO real world data and digital oncology · 2026Article
- Review
- 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
- Generalizable AI predicts immunotherapy outcomes across cancers and treatments.Nature medicine · 2026Article
- Regulation of immune checkpoint molecules in cancer immune evasion and therapy.Nature reviews. Cancer · 2026Review
- AI-driven dual-task prediction model for co-stratifying efficacy and toxicity in NSCLC immunotherapy.PLOS digital health · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
166 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
22 authors at 1 institution in 1 country.
Funding
Abstract
Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74-0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52-0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65-0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.
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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.