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Predictive imaging in abdominal oncology: current trends and future directions
Maxime Barat1,2, Stylianos Tzedakis2,3, Anna Pellat2,4
1Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
Japanese Journal of Radiology
|October 30, 2025
Summary
Predictive imaging, using radiomics and AI, enhances abdominal oncology by personalizing cancer care beyond human vision. Integrated models improve diagnosis, staging, and treatment for better patient outcomes.
Area of Science:
- Oncology
- Radiology
- Artificial Intelligence
Background:
- Predictive imaging utilizes advanced analytics like radiomics and machine learning.
- It offers capabilities beyond human visual interpretation in medical imaging.
- This technology is transforming abdominal oncology applications.
Purpose of the Study:
- To provide an overview of predictive imaging's capabilities in abdominal cancers.
- To highlight its role in personalizing cancer detection, diagnosis, staging, treatment, monitoring, and prognostication.
- To discuss the integration of multimodal data for enhanced cancer assessment.
Main Methods:
- Review of advanced analytic tools including radiomics, delta radiomics, and artificial intelligence.
- Exploration of integrated models combining imaging data, clinical parameters, and biomarkers.
- Focus on applications in hepatic, pancreatic, colorectal, and gastric cancers.
Main Results:
- Predictive imaging offers personalized cancer detection, diagnosis, staging, and treatment planning.
- Integrated models show potential for superior prognostication and risk stratification.
- AI-powered predictive imaging is reshaping abdominal oncology.
Conclusions:
- Predictive imaging, powered by AI and multimodal data, is set to revolutionize abdominal oncology.
- It promises noninvasive means to guide individualized patient care.
- Overcoming current challenges will unlock actionable clinical insights from imaging data.
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