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Deep learning with multimodal representation for pancancer prognosis prediction
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Abstract
Motivation: Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this problem, we constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data and histopathology whole slide images (WSIs). We developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type—using deep highway networks to extract features from clinical and genomic data, and convolutional neural networks to extract features from WSIs. Results: We used pancancer data to train these feature encodings and predict single cancer and pancancer overall survival, achieving a C-index of 0.78 overall. This work shows that it is possible to build a pancancer model for prognosis that also predicts prognosis in single cancer sites. Furthermore, our model handles multiple data modalities, efficiently analyzes WSIs and represents patient multimodal data flexibly into an unsupervised, informative representation. We thus present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients. Availability and implementation: https://github.com/gevaertlab/MultimodalPrognosis
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“Different approaches have been proposed, such as multi-task learning [70], generative models [63] and multimodal dropout [78, 101].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [78]
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“Cheerla and Gevaert (2019) constructed a multimodal neural network-based model to predict the survival of patients for 20 different cancer types using clinical data, mRNA expression data, microRNA expression data, and histopathology whole slide images (WSIs).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Cheerla and Gevaert, 2019
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“Indeed, it has been shown that training a model on multiple tissues or cancer variants can improve detection performance for each cancer53, as many morphological features are observed across cancers from different topographies54.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [53]
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“although we posit that increased diversity helps to learn robust and rare features. Indeed, it has been shown that training a model on multiple tissues or cancer variants can improve detection performance for each cancer53, as many morphological features are observed across cancers from different topographies54.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [53]
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- Momeni, 2018, International MICCAI Brainlesion Workshop, 407
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Metadata
- Authors
- Cheerla, A., & Gevaert, O.
- Affiliations
- Monta Vista High School, Cupertino, CA, USA
- Stanford University (US)
- Corresponding author
- Gevaert, Olivier
- ORCID
- Gevaert, Olivier
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-07
- Volume
- 35
- Issue
- 14
- Pages
- i446-i454
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6612862
- OpenAlex
- W2954499361
- Cited by
- 421
- References
- 52
- Citations by year
- 2026: 69 · 2025: 74 · 2024: 68 · 2023: 64 · 2022: 59 · 2021: 69 · 2020: 17 · 2019: 1
- FWCI
- 16.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01EB020527)
- National Institute of Dental and Craniofacial Research (U01DE025188)
- National Cancer Institute (U01CA199241, U01CA217851)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- cancer prognosis prediction, pan-cancer analysis, multimodal data integration, overall survival prediction, clinical data, mRNA expression, microRNA expression, convolutional neural networks, missing data handling, precision oncology
- MeSH terms
- Deep Learning, Humans, Neoplasms, Neural Networks, Computer, Genome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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