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Molecular Diagnostics

Multiscale Pancancer Analysis Uncovers Intrinsic Imaging and Molecular Characteristics Prominent in Breast Cancer and Glioblastoma

British Journal of Cancer volume 134, pages 131–140 (2026)Cite this article

Subjects

Abstract

Background

Genomic traits are commonly observed across cancer types, yet current pan-cancer analyses primarily focus on shared molecular features, often overlooking potential imaging characteristics across cancers.

Methods

This retrospective study included 793 patients from the I-SPY1 breast cancer cohort (n = 145), Duke-UPenn glioblastoma (GBM) cohort (n = 452), and an external validation cohort (n = 196). We developed and validated multiparametric MRI-based radiomic and deep learning models to extract both cancer-type common (CTC) and cancer type-specific (CTS) features associated with the prognosis of both cancers. The biological relevance of the identified CTC features was investigated through pathway analysis.

Results

Seven CTC radiomic features were identified, demonstrating superior survival prediction compared to cancer type-specific (CTS) features, with AUCs of 0.876 for breast cancer and 0.732 for GBM. The deep feature model stratified patients into distinct survival groups (p = 0.00029 for breast cancer; p = 0.0019 for GBM), with CTC features contributing more than CTS features. Independent validation confirmed their robustness (AUC: 0.784). CTC-associated genes were enriched in key pathways, including focal adhesion, suggesting a role in breast cancer brain metastasis.

Conclusion

Our study reveals pan-cancer imaging phenotypes that predict survival and provide biological insights, highlighting their potential in precision oncology.

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Fig. 1
Fig. 2: Framework of multiscale pancancer analysis across breast cancer and glioblastoma.
Fig. 3: Survival risk prediction generated by deep features for predicting risk status.
Fig. 4: Feature importance analysis and feature distribution.
Fig. 5: Top deep learning feature-related biological pathways.

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Data availability

The gene expression data used in this study were sourced from the Gene Expression Omnibus (GEO) database and identified by the accession numbers GSE32603 and GPL14668.The imaging data of the tumor development datasets are available from GBM in TCIA on the website. [https://www.cancerimagingarchive.net/collection/upenn-gbm].The ISPY-1 trial is available in TCIA on the website [https://wiki.cancerimagingarchive.net/display/Public/ISPY1].PyRadiomics (https://pyradiomics.readthedocs.io/en/latest/) was used for radiomic feature analysis.

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Funding

This research was funded by the Natural Science Foundation of Zhejiang Province of China under award number LR23F010002 and the National Natural Science Foundation of China under award number U21A20521, W2411054 and 62271178.

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Authors and Affiliations

  1. Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Ming Fan, Xiling Wu, Da Pan, Jiaojiao Du & Lihua Li

  2. Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia

    Xin Gao

  3. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Virginia, VA, 24061, USA

    Yue Wang

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Contributions

MF and LL wrote the manuscript and analyzed the data. XW, DP, JD, XG and YW conducted the data collection and analysis. MF and LL had primary responsibility for the final content.

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Correspondence to Lihua Li.

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The authors declare no competing interests.

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This study was approved by the Institutional Review Board (IRB) of Hangzhou Dianzi University (IRB-2019001). All methods were conducted in accordance with relevant guidelines and regulations. The imaging data used in this study were obtained from The Cancer Imaging Archive (TCIA), a publicly accessible database. All data in TCIA were originally collected with approval from appropriate institutional review boards and with informed consent from the participants. No identifiable images of human participants are included in this manuscript.

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Fan, M., Wu, X., Pan, D. et al. Multiscale Pancancer Analysis Uncovers Intrinsic Imaging and Molecular Characteristics Prominent in Breast Cancer and Glioblastoma. Br J Cancer 134, 131–140 (2026). https://doi.org/10.1038/s41416-025-03235-7

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