Artificial intelligence and radiomics applied to prostate cancer bone metastasis imaging: A review
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | iRADIOLOGY |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ird3.99 |
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| Summary: | 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. |
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| ISSN: | 2834-2860 2834-2879 |