ArticleNature communications2023
Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Gene-structured histology for deriving and predicting pancreatic cancer molecular subtypes.Signal transduction and targeted therapy · 2026Article
- Decoding neuro-tumor interactions in pancreatic cancer: mechanisms, immunosuppressive networks and therapeutic opportunities.Molecular biomedicine · 2026Review
- The Landmark Series: Mutation-Based Therapy of Pancreatic Cancer.Annals of surgical oncology · 2026Review
- Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- Cancer Heterogeneity and Cancer Cell Plasticity: Molecular Mechanisms and Precision Therapy.MedComm · 2026Review
- Spatially-resolved subtype progression reveals metabolic vulnerabilities in pancreatic ductal adenocarcinoma.Molecular cancer · 2026Article
- Article
- Molecular subtypes in pancreatic cancer: from academic promise to clinical reality.Molecular cancer · 2026Review
- Gene-Morphology Alignment via Graph-Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in Pancreatic Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.Research square · 2026Article
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- Engineering strategies to address immune and delivery barriers in pancreatic ductal adenocarcinoma: a barrier-matched translational framework.Frontiers in immunology · 2026Review
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Current Advances in Preclinical Patient-Derived Cultivation Models for Individualized Drug Response Prediction in Pancreatic Cancer.Oncology research · 2026Review
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- A deep learning-based multiscale integration of spatial omics with tumor morphology.Nature communications · 2025Article
Corrections and comments
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Authors and funding
24 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort (n = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n = 148; 97; 126 / biopsy cohort n = 25), all with transcriptomic data (n = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution.
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