Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Open accessAbstract
The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.
AI snapshot
Written from the paper’s full text.
TL;DR
The authors built a weakly supervised multimodal deep learning model that combines pathology whole-slide images with molecular profiles to predict cancer survival across 14 TCGA cancer types. They report improved risk stratification over unimodal models for most cancers and provide an interactive platform for exploring image and molecular explanations.
Key findings
- Multimodal fusion achieved an overall cross-validated c-index of 0.645, compared with 0.585 for image-only AMIL and 0.607 for molecular-only SNN; Survival AUC was 0.662 for multimodal, versus 0.616 for SNN and 0.598 for AMIL.
- The multimodal model had the highest c-index in 12 of 14 cancer types, with statistically significant binary risk stratification in 10 of 14. KIRP showed the largest gain, reaching a c-index of 0.816 versus 0.539 for AMIL and 0.779 for SNN.
- Whole-slide images contributed an average of 16.8% of input attributions across cancers, but this varied widely, including 55.1% for UCEC, 32.4% for PAAD, and 5.8% for LUSC.
- Interpretability analyses linked low-risk attention regions to greater immune-cell presence and high-risk regions to tumor presence, higher grade, or invasion; lymphocyte fractions differed significantly in 8 of 14 cancers and tumor-cell fractions in 6 of 14. TIL presence was significantly higher in predicted low-risk patients for 9 of 14 cancers.
Methods
- The study used 6,592 diagnostic H&E whole-slide images from 5,720 patients across 14 TCGA cancer types, with paired mutation, copy-number, and RNA-Seq data where available.
- The multimodal model combined an attention-based multiple-instance-learning branch for images, a self-normalizing network for molecular features, and a Kronecker-product fusion layer. Training used patient-level five-fold cross-validation with 80/20 train-test splits, and performance was assessed with c-index, Survival AUC, Kaplan-Meier curves, and logrank tests.
- Interpretability used attention heatmaps for image regions and Integrated Gradients for molecular features, with HoVerNet-based cell segmentation to quantify cell types and TILs in high-attention regions. Results were made available through the PORPOISE platform.
Limitations
- The paper states that the approach can identify “what” and “where” but not always “why,” so discovered features require further human quantification and biological interpretation.
- The paper notes that attention sometimes shifted away from tumor regions toward stroma or normal tissue, and speculates this may reflect training dynamics favoring the simpler modality; molecular data are bulk and lack spatial resolution.
- Evident from the text: the study is retrospective and TCGA-based, with no independent external validation; SKCM includes metastatic cases that may optimistically bias performance, and data-alignment criteria were relaxed for SKCM and UCEC due to missingness.
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- Finding
The paper reports that the multimodal model generally improved performance for nearly all cancer types after integrating modalities.
“Overall, however, model performances were found to improve following multimodal integration for almost all cancer types”
Quote in the paperNot yet classified - Finding
Across the 14 cancer types, the multimodal model outperformed the two unimodal baselines on overall c-index and survival AUC.
“Across the 14 cancer types, MMF achieved an overall c-Index of 0.645, whereas AMIL and SNN had overall c-Indices of 0.585 and 0.607 respectively. On Survival AUC, similar improvement in multimodal integration was found with an overall performance of 0.662 in comparison to 0.616 and 0.598 in SNN and AMIL respectively (Table S2).”
Quote in the paperNot yet classified - Finding
The multimodal model achieved the highest c-index in 12 of the 14 cancer types.
“In one-versus-all model performance comparisons on individual cancer types, MMF achieved the highest c-Index on 12 out of 14 (12/14) cancer types, with models for 10/14 cancer types demonstrating statistical significance in binary patient stratification (Figure 2A,2B, 2C).”
Quote in the paperNot yet classified - Finding
Its multimodal model can fuse the two data types to predict outcomes and identify prognostic features associated with poor and favorable survival.
“Our weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes.”
Quote in the paperNot yet classified - Finding
The paper finds that TIL presence is higher in low-risk patients for 9 of the 14 cancer types.
“we found that 9 out of 14 cancer types had a statistically significant increase in TIL presence amongst patients with predicted low-risk, indicating that model attention was localized to more immune-hot regions.”
Quote in the paperNot yet classified
- Finding
The paper reports that UCEC had a much larger WSI contribution than most other cancer types.
“However, we note that for MMF models evaluated on UCEC, WSIs contributed to 55.1% of all input attributions, which corroborates with high AMIL performance on this cancer type.”
Quote in the paperNot yet classified - Finding
The multimodal interpretability analysis identified known oncogenes and immune-related genes among the most attributed molecular features.
“Across all cancer types, gradient-based interpretability was able to identify many well-known oncogenes and immune-related genes established in existing biomedical literature and used in targeted molecular therapies (Uhlen et al.,2017).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Uhlen et al.,2017 source
- Finding
The authors state that TIL presence supports favorable prognosis in several immunotherapy-relevant cancer types.
“For cancer types in our dataset that have been FDA approved immune checkpoint inhibitor therapies, TIL presence was used as model explanations for favorable prognosis in BRCA (P-Value, 5.17×10−11, t-test), HNSC (P-Value, 1.97×10−18, t-test), KIRC (P-Value, 1.86×10−3, t-test), LIHC (P-Value, 2.54 × 10−17, t-test), LUAD (P-Value, 1.54 × 10−21, t-test), LUSC (P-Value, 2.92 × 10−12, t-test), PAAD (P-Value, 3.77×10−6, t-test), STAD (P-Value, 1.09×10−9, t-test), and SKCM (P-Value, 6.29×10−10, t-test).”
Quote in the paperNot yet classified - Finding
The multimodal WSI contribution analysis suggested that molecular features dominated predictions overall, but the WSI contribution varied by cancer type.
“WSIs on average accounted for 16.8% of input attributions in MMF for all cancer types, which suggests that molecular features drive most of the risk prediction in MMF (Figure 2C, Table S3).”
Quote in the paperNot yet classified - Finding
In the interpretability analysis, low-risk patients generally showed more immune-cell-rich regions and lower tumor grade in high-attention areas than high-risk patients.
“Across all cancer types, we generally observed that high attention regions in low-risk patients corresponded with greater immune cell presence and lower tumor grade than that of high-risk patients, with 8/14 cancer types demonstrating statistically significant differences in lymphocyte cell fractions in high attention regions (Figure 6F, S3-10).”
Quote in the paperNot yet classified - Finding
High-risk patients tended to have high-attention regions with more tumor cells, higher grade, and invasion in several cancer types.
“we also observed that high attention regions in high-risk patients corresponded with increased tumor presence, higher tumor grade and tumor invasion in certain cancer types, with 6/14 cancer types demonstrating statistically significance differences in tumor cell fractions (Figure S3, S5, S6, S7, S9, S11).”
Quote in the paperNot yet classified - Method
The study makes its morphological and molecular prognostic analyses available in an interactive open-access database for further exploration and biomarker discovery.
“We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and patient-level in an interactive open-access database (http://pancancer.mahmoodlab.org) to allow for further exploration, biomarker discovery and feature assessment.”
Quote in the paperNot yet classified - Method
The authors evaluated the multimodal fusion model with 5-fold cross-validation on paired WSI-molecular datasets from 14 cancer types.
“We first evaluated our proposed multimodal fusion deep learning model (MMF) using 5-fold cross validation on the paired WSI-molecular datasets from 14 cancer types.”
Quote in the paperNot yet classified - Method
They compared the multimodal model against an attention-based WSI-only model and an SNN molecular-only model.
“We also compared our model with unimodal deep learning models trained with one modality: an Attention-based Multiple Instance Learning (AMIL) model that uses only WSIs, and a Self-Normalizing Network (SNN) model that uses only molecular features.”
Quote in the paperNot yet classified - Method
The paper proposes a multimodal deep learning approach that combines whole-slide histology images with molecular profile data from 14 cancer types for prognosis analysis.
“We use multimodal deep learning to jointly examine pathology whole slide images and molecular profile data from 14 cancer types.”
Quote in the paperNot yet classified - Method
The study uses weakly supervised multimodal fusion to integrate histology and genomic data for patient-level survival prediction.
“PORPOISE (Pathology-Omics Research Platform for Integrated Survival Estimation) uses a high-throughput, interpretable, weakly-supervised, multimodal deep learning algorithm (MMF) designed for integrating whole slide images and molecular profile data in weakly-supervised learning tasks such as patient-level cancer prognosis via survival analysis.”
Quote in the paperNot yet classified - Method
The authors built PORPOISE to provide prognostic explanations for both WSI and molecular data across thousands of patients and multiple cancer types.
“Using our multimodal network, we developed the Pathology-Omics Research Platform for Integrative Survival Estimation (PORPOISE), an interactive platform that directly yields prognostic markers learned by our model for thousands of patients across multiple cancer types, available at http://pancancer.mahmoodlab.org (Interactive Demo).”
Quote in the paperNot yet classified - Method
WSI processing included tissue segmentation, non-overlapping patch extraction, and ResNet50 feature encoding.
“For each WSI, automated segmentation of tissue was performed using the public CLAM (Lu et al., 2021) repository for WSI analysis. Following segmentation, image patches of size 256 × 256 were extracted without overlap at the 20× equivalent pyramid level from all tissue regions identified. Subsequently, a ResNet50 model pretrained on ImageNet is used as an encoder to convert each 256 × 256 patch into 1024-dimensional feature vector, via spatial average pooling after the 3rd residual block.”
Quote in the paperNot yet classified0 of 1 sources checked- cites Lu et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Method
The study used attention- and gradient-based interpretability to explain how image and molecular features contribute to prognosis.
“For interpretation and further validation of our models, we applied attention- and gradient-based interpretability to our trained SNN, AMIL, and MMF models in order to explain how WSI and molecular features are respectively used to predict prognosis.”
Quote in the paperNot yet classified
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Chen et al. (2022) model incorporated an explainability component with heatmaps for histopathology and SHAP values for genomic markers.
“Notably, a vital explainability component was incorporated with heatmaps for histopathology and SHAP values for genomic markers.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Chen et al., 2022
The Results section, “Multimodal Interpretability for Joint Image-Omic Biomarker Discovery,” describes attention heatmaps over histopathology slides and attribution plots for molecular features (Figures 3–6). It calls the plots SHAP-styled, but says the attribution values were calculated using Integrated Gradients, not SHAP. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn digital pathology applications, whole slide images have been integrated with genomic features for prognosis prediction.
“and integrated with genomic features for prognosis prediction [20].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [20]
In the PORPOISE framework, Chen et al. introduced a method to integrate WSIs with genomic and molecular profiles for prognosis prediction and helped identify poor prognosis-related joint biomarkers.
“In the PORPOISE framework, Chen et al. introduced a method to integrate WSIs with genomic, and molecular profiles for prognosis prediction and helped identify poor prognosis-related joint biomarkers [20].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [20]
Chen et al. (2022) developed a multimodal deep learning model to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types to predict outcomes and discover prognostic features correlating with poor and favorable outcomes.
“Chen et al. (2022) developed a multimodal deep learning model to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types to predict outcomes and discover prognostic features correlating with poor and favorable outcomes.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Chen et al., 2022
The fundamental criterion for evaluating a medical AI agent is its precision in generating diagnostic and treatment recommendations.
“The fundamental criterion for evaluating a medical AI agent is its precision in generating diagnostic and treatment recommendations.80,100,118,119,120”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [120]
The Results and Star Methods sections evaluate a cancer-prognosis model using patient risk stratification, c-index, and survival AUC (page not available in our copy). The Introduction and Discussion mention diagnostic tasks and possible future support for treatment decisions, but do not identify the precision of diagnostic and treatment recommendations as the fundamental criterion for evaluating a medical AI agent. Searched for that criterion and for accuracy of diagnostic and treatment recommendations. (LLM, conf=0.74)
Experimental — accuracy not yet measured
ML and AI allow for integration of multiple data types, including EHRs, genomic data, and environmental exposures, and of multimodal data, providing a comprehensive perspective of the determinants of health outcomes across different stages of the life course.
“Additionally, ML and AI allow for integration of multiple data types, including EHRs, genomic data, and environmental exposures, and of multimodal data, such as kinds of information from individual’s various types of records, thus providing a comprehensive perspective of the determinants of health outcomes across the different stages of life course [32].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [32]
Multimodal AI systems have been evaluated for various applications in precision oncology, such as outcome predictions.
“Multimodal AI systems have been evaluated for various applications in precision oncology, such as outcome predictions14,15.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [14]
Increasing efforts use digital pathology images to guide treatment selection by predicting prognosis and/or existing treatment biomarkers with AI.
“there are increasing efforts of using digital pathology images to guide treatment selection by predicting patient prognosis (73)(74)(75) and/or existing treatment biomarkers (76-79) using AI.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [75]
Chen and colleagues developed an approach that fuses genomic data and histopathology images to predict survival in a pan-cancer cohort.
“For instance, Chen and colleagues (75) developed an approach to fuse genomic data and histopathology images to predict survival in a pan-cancer cohort.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [75]
Several multimodal AI systems have been introduced, including models that analyze radiology images together with clinical data or integrate histopathology with genomic or text-based information.
“Recently, several such AI systems have been introduced6. Examples include models that analyze radiology images together with clinical data7 or integrate information from histopathology with genomic8 or text-based information9.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [8]
Learning the spatial relevance of non-imaging data such as bulk gene expression is not straightforward using late fusion.
“However, learning the spatial relevance of non-imaging data such as bulk gene expression is not straightforward using late fusion.”
From Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival (2024) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
High- and low-risk prognosis labels are reversed
MinorThe text assigns high risk to the top quartile and low risk to the bottom quartile, then says those groups have favorable and poor prognoses, respectively. Elsewhere, it associates high risk with poor survival and low risk with favorable survival.
“the top 25% (high-risk group) and bottom 25% (low-risk group) of predicted patient risks for each cancer type, which reflect favorable and poor cancer prognosis respectively.”
“WSIs with predicted high-risk scores would have high attention on high tumor grade or tumor invasion regions used in explaining poor survival, whereas WSIs with predicted low risk scores would have high attention on low tumor grade or lymphocyte-containing regions used in explaining favorable survival.”
PORPOISE has two different expanded names
MinorThe platform's name ends in "Integrative Survival Estimation" in the Results but "Integrated Survival Estimation" in Methods.
“Pathology-Omics Research Platform for Integrative Survival Estimation (PORPOISE)”
“PORPOISE (Pathology-Omics Research Platform for Integrated Survival Estimation)”
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- Authors
- Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 40
- Issue
- 8
- Pages
- 865-878.e6
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC10397370
- OpenAlex
- W4291021272
- Cited by
- 660
- References
- 88
- Citations by year
- 2026: 219 · 2025: 215 · 2024: 133 · 2023: 83 · 2022: 9 · 2021: 1
- FWCI
- 36.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. National Library of Medicine (T15LM007092)
- National Science Foundation
- Brigham and Women's Hospital
- National Institutes of Health
- National Human Genome Research Institute
- National Institute of General Medical Sciences (R35GM138216)
- National Cancer Institute (T32CA251062)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- pan-cancer, multimodal deep learning, whole slide images, prognostic model, weakly supervised learning, morphological features, molecular profiles, explainable features, biomarker discovery, interactive database
- MeSH terms
- Deep Learning, Algorithms, Humans, Neoplasms, Prognosis, Genomics
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