Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma
Open accessAbstract
Motivation: As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance. Results: We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728–0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data. Availability and implementation: https://github.com/zhang-de-lab/zhang-lab? from=singlemessage Supplementary information: Supplementary data are available at Bioinformatics online.
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Feature selection can be used on concatenated marginal representations to choose latent features that most strongly influence the target variable.
“Feature selection can also be used on the concatenated marginal representations, choosing latent features that most strongly influence the target variable, as done in [65] to predict prognosis of clear cell renal cell carcinoma patients.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [65]
Ning et al. (2020) developed a cross-modal system to create a prognostic model for clear renal cell carcinoma.
“One study focused on renal diseases developed a cross-modal system to create a prognostic model for clear renal cell carcinoma (Ning et al., 2020).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Ning et al., 2020
The Ning et al. (2020) model integrates deep features from CT and histopathological images with eigengenes from genomic data, demonstrating significant ability to stratify patients into high- and low-risk categories for disease progression.
“This model integrates deep features extracted from computed tomography and histopathological images with eigengenes derived from genomic data, demonstrating a significant ability to stratify patients into highand low-risk categories for disease progression.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Ning et al., 2020
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- Authors
- Ning, Z., Pan, W., Chen, Y., Xiao, Q., Zhang, X., Luo, J., Wang, J., & Zhang, Y.
- Affiliations
- Key Laboratory of Guangdong Province (CN)
- Nanfang Hospital (CN)
- Corresponding author
- Wang, Jian
- Zhang, Yu
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-27
- Volume
- 36
- Issue
- 9
- Pages
- 2888-2895
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W3003269599
- Cited by
- 53
- References
- 57
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 7 · 2023: 10 · 2022: 7 · 2021: 11 · 2020: 1
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61971213, 61671230)
- Basic and Applied Basic Research Foundation of Guangdong Province (2019A1515010417)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Renal cell carcinoma treatment
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Ferroptosis and cancer prognosis
- Keywords
- clear cell renal cell carcinoma, outcome prediction, computed tomography, histopathological images, eigengenes, functional genomic data, multimodal fusion, risk stratification
- MeSH terms
- Carcinoma, Renal Cell, Humans, Kidney Neoplasms, Tomography, X-Ray Computed, Neural Networks, Computer, Genome
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