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AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision

Qiqi Kong1, Yunqing Ban1

  • 1Department of Radiology, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

Frontiers in Oncology
|February 20, 2026
PubMed

Insights

Artificial intelligence (AI) and radiomics offer a "digital biopsy" for gynecologic cancers. This approach non-invasively predicts molecular features and therapeutic response, advancing precision medicine.

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Tumor heterogeneity poses a significant challenge in gynecologic oncology, contributing to treatment resistance and limitations of traditional biopsies.
  • Artificial intelligence (AI) and radiomics are emerging as non-invasive methods, termed "digital biopsy," to analyze medical imaging (MRI, CT, PET) and infer tumor biology.

Purpose of the Study:

  • To review the current applications of AI in predicting key molecular features in gynecologic cancers.
  • To explore advanced AI architectures for understanding the tumor microenvironment and predicting treatment outcomes.
  • To discuss challenges and future directions for AI implementation in clinical practice.

Main Methods:

  • Review of existing literature on AI and radiomics in gynecologic oncology.
  • Synthesis of studies predicting molecular markers such as HRD in ovarian cancer, MSI and PI3K activation in endometrial cancer, and HPV integration/DNA methylation in cervical cancer.
  • Exploration of advanced AI models including Vision Transformers (ViTs) and Graph Neural Networks (GNNs).

Main Results:

  • AI and radiomics show promise in non-invasively predicting critical molecular subtypes across various gynecologic cancers.
  • Advanced AI architectures can potentially delineate the tumor microenvironment and forecast patient response to therapies.
  • Key challenges include model generalizability, the need for causal AI, data limitations, and the development of foundation models and digital twins.

Conclusions:

  • AI-driven "digital biopsy" has the potential to revolutionize gynecologic oncology by bridging the gap between imaging phenotypes and molecular genotypes.
  • This technology paves the way for a new era of precision medicine, improving diagnostic accuracy and treatment strategies.
  • Overcoming current hurdles is essential for the successful clinical translation of AI in gynecologic cancer care.
Keywords:
artificial intelligencedigital biopsyfoundation modelsgynecologic oncologyradiogenomicstumor heterogeneity

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