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Editorial
. 2025 Nov 10;16(1):250.
doi: 10.1186/s13244-025-02136-w.

Critical dialogue: Can AI-based imaging predict tumor biology? Infer metastasis, microvascular invasion, and growth patterns from pixels

Affiliations
Editorial

Critical dialogue: Can AI-based imaging predict tumor biology? Infer metastasis, microvascular invasion, and growth patterns from pixels

Yashbir Singh et al. Insights Imaging. .
No abstract available

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Yashbir Singh is a member of the scientific editorial board of Insights into Imaging (section: Artificial Intelligence and Radiomics), and as such, he did not participate in the selection or review processes for this article. The other authors do not have any competing interests.

References

    1. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446 - DOI - PMC - PubMed
    1. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577 - DOI - PMC - PubMed
    1. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006 - DOI - PMC - PubMed
    1. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510 - DOI - PMC - PubMed
    1. Kather JN, Pearson AT, Halama N et al (2019) Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 25:1054–1056 - DOI - PMC - PubMed

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