Cited by 424AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMachine Learning in Healthcare

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Deep learning with multimodal representation for pancancer prognosis prediction

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Cheerla, Anika, Gevaert, Olivier·2019·Bioinformatics

DOI 10.1093/bioinformatics/btz342PMC PMC6612862

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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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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
DOI: 10.1093/bioinformatics/btz342
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: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor, 7 d)
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

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Deep learning with multimodal representation for pancancer prognosis prediction

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