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EditorialEditorial
Open Access

Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis

Sunyi Zheng, Xiaonan Cui and Zhaoxiang Ye
Cancer Biology & Medicine January 2025, 22 (1) 6-13; DOI: https://doi.org/10.20892/j.issn.2095-3941.2024.0422
Sunyi Zheng
Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin 300060, China
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Xiaonan Cui
Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin 300060, China
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Zhaoxiang Ye
Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin 300060, China
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  • ORCID record for Zhaoxiang Ye
  • For correspondence: zye{at}tmu.edu.cn
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    Figure 1

    Overview of AI-driven methods in radiological cancer imaging. This schematic illustrates the foundational concepts of machine learning with radiomics, deep learning, and large models, and their roles in cancer diagnosis, treatment response, and prognosis prediction.

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    Table 1

    Comparison of the 3 main AI methods applied in cancer imaging

    CharacteristicsMachine learning with radiomicsDeep learningLarge models
    Data requirementModerateAdequateEnormous
    Hardware requirementModerateHighVery high
    AnnotationManual delineationFlexibleFlexible
    Image featuresPredefinedLearned automaticallyLearned automatically
    PerformanceModerateHighVery high
    ExplainabilityGoodPoorPoor
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Cancer Biology & Medicine: 22 (1)
Cancer Biology & Medicine
Vol. 22, Issue 1
15 Jan 2025
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Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis
Sunyi Zheng, Xiaonan Cui, Zhaoxiang Ye
Cancer Biology & Medicine Jan 2025, 22 (1) 6-13; DOI: 10.20892/j.issn.2095-3941.2024.0422

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Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis
Sunyi Zheng, Xiaonan Cui, Zhaoxiang Ye
Cancer Biology & Medicine Jan 2025, 22 (1) 6-13; DOI: 10.20892/j.issn.2095-3941.2024.0422
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    • AI technologies applied in radiology
    • AI for cancer diagnosis
    • AI for treatment response evaluation
    • AI for cancer prognosis
    • Potential of AI beyond clinical practice
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