The Integration of Radiomics and Artificial Intelligence in Modern Medicine
Abstract
:1. Introduction
Aim of the Review
2. Materials and Methods
3. Results
4. Radiomics and Its Involvement of Quantitative Feature Extraction from Medical Images
4.1. Introduction
4.2. Discussion
4.3. Conclusions
5. Machine Learning, Deep Learning and Computer-Aided Diagnostic (CAD) Systems Approaches in Radiomics
5.1. Introduction
5.2. Discussion
5.3. Conclusions
6. The Effect of Radiomics and AI on Improving Workflow Automation and Efficiency, Optimize Clinical Trials and Patient Stratification
6.1. Introduction
6.2. Discussion
6.3. Conclusions
7. Predictive Modeling Improvement by Machine Learning in Radiomics
7.1. Introduction
7.2. Discusson
7.3. Conclusions
8. Multimodal Integration and Enhanced Deep Learning Architectures in Radiomics
8.1. Introduction
8.2. Discussion
9. Regulatory and Clinical Adoption Considerations for Radiomics-Based CAD
9.1. Introduction
9.2. Discussion
9.3. Conclusions
10. Future Directions and Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | Imaging Modality, Cancer Type | AI Technique | Radiomic Outcomes | Values | Advantages | Limitations |
---|---|---|---|---|---|---|
Aerts et al. (2014) [3] | CT Lung, Head and Neck | Radiomics, Machine Learning | Tumor phenotyping, Prognostic modeling | Quantitative imaging features associated with tumor genotype and clinical outcomes | Noninvasive, reproducible assessment of tumor characteristics | Requires large, well-annotated datasets for model training |
Parmar et al. (2015) [11] | CT Lung | Radiomics, Machine Learning | Quantitative radiomic biomarkers | Identification of robust radiomic features predictive of clinical outcomes | Potential for risk stratification and personalized treatment planning | Feature selection and model validation remain challenging |
Antropova et al. (2017) [12] | Mammography, Ultrasound, MRI Breast | Deep Feature Fusion | Breast cancer diagnosis | Improved breast cancer detection and characterization using multimodal imaging data | Leverages complementary information from different imaging modalities | Computational complexity and interpretability of deep learning models |
Hosny et al. (2018) [13] | Multiple - | AI in Radiology | Potential of AI in radiology, challenges | Improved accuracy, efficiency, and consistency in medical image analysis | Opportunity to transform radiology practice and patient care | Concerns about data privacy, algorithmic bias, and ethical considerations |
Wang et al. (2020) [14] | Multiple - | Radiomics, Deep Learning | Synergy of radiomics and deep learning | Leveraging the complementary strengths of radiomics and deep learning for clinical decision-making | Potential to enhance personalized medicine and precision diagnostics | Need for standardized protocols and interpretable hybrid models |
Huang et al. (2016) [15] | CT Colorectal | Radiomics | Lymph node metastasis prediction in colorectal cancer | Preoperative prediction of lymph node involvement to guide treatment planning | Potential to improve surgical decision-making and avoid unnecessary procedures | Retrospective study design and need for prospective validation |
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Maniaci, A.; Lavalle, S.; Gagliano, C.; Lentini, M.; Masiello, E.; Parisi, F.; Iannella, G.; Cilia, N.D.; Salerno, V.; Cusumano, G.; et al. The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life 2024, 14, 1248. https://doi.org/10.3390/life14101248
Maniaci A, Lavalle S, Gagliano C, Lentini M, Masiello E, Parisi F, Iannella G, Cilia ND, Salerno V, Cusumano G, et al. The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life. 2024; 14(10):1248. https://doi.org/10.3390/life14101248
Chicago/Turabian StyleManiaci, Antonino, Salvatore Lavalle, Caterina Gagliano, Mario Lentini, Edoardo Masiello, Federica Parisi, Giannicola Iannella, Nicole Dalia Cilia, Valerio Salerno, Giacomo Cusumano, and et al. 2024. "The Integration of Radiomics and Artificial Intelligence in Modern Medicine" Life 14, no. 10: 1248. https://doi.org/10.3390/life14101248
APA StyleManiaci, A., Lavalle, S., Gagliano, C., Lentini, M., Masiello, E., Parisi, F., Iannella, G., Cilia, N. D., Salerno, V., Cusumano, G., & La Via, L. (2024). The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life, 14(10), 1248. https://doi.org/10.3390/life14101248