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Artificial Intelligence and Radiomics Applied to Prostate Cancer Bone Metastasis Imaging: A Review
S J Pawan1,2, Joseph Rich3,4, Jonathan Le4
1Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
Abstract:
The skeletal system is the most common site of metastatic prostate cancer, and these lesions are associated with poor outcomes. Diagnosing these osseous metastatic lesions relies on radiologic imaging, making early detection, diagnosis, and monitoring crucial for clinical management. However, the literature lacks a detailed analysis of various approaches and future directions. To address this gap, we present a scoping review of quantitative methods from diverse domains, including radiomics, machine learning, and deep learning, applied to imaging analysis of prostate cancer with clinical insights. Our findings highlight the need for developing clinically significant methods to aid in the battle against prostate bone metastasis.
Insights
This study reviews quantitative imaging methods for prostate cancer bone metastasis. Developing advanced techniques like radiomics and machine learning is crucial for early detection and better patient outcomes.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Prostate cancer frequently metastasizes to the skeletal system, leading to poor patient prognoses.
- Radiologic imaging is essential for diagnosing and monitoring bone metastases, impacting clinical management.
- A comprehensive analysis of current and future quantitative imaging approaches for prostate cancer bone metastasis is lacking.
Purpose of the Study:
- To conduct a scoping review of quantitative methods for analyzing prostate cancer bone metastasis in medical imaging.
- To identify and analyze approaches including radiomics, machine learning, and deep learning.
- To provide clinical insights and highlight future research directions for combating prostate bone metastasis.
Main Methods:
- Scoping review methodology.
- Inclusion of quantitative methods from radiomics, machine learning, and deep learning.
- Analysis of imaging data for prostate cancer osseous metastatic lesions.
Main Results:
- Identified diverse quantitative methods applied to prostate cancer imaging.
- Highlighted the potential of radiomics, machine learning, and deep learning in this domain.
- Emphasized the need for clinically relevant advancements.
Conclusions:
- Quantitative imaging analysis holds significant promise for managing prostate cancer bone metastasis.
- Further development of advanced methods is required to improve early detection, diagnosis, and monitoring.
- Bridging the gap between quantitative analysis and clinical application is essential for improving patient outcomes.
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