Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment
Abstract Positron emission tomography (PET) with gallium‐68 prostate‐specific membrane antigen (68Ga‐PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Precision Radiation Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pro6.70014 |
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| Summary: | Abstract Positron emission tomography (PET) with gallium‐68 prostate‐specific membrane antigen (68Ga‐PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and prognosis in PC patients. This review provides an overview of the current methods and challenges in the assessment of regional and total tumor volumes using 68Ga‐PSMA PET. Traditional manual segmentation methods are time‐consuming processes that are further challenged by inter‐observer variability. Artificial intelligence (AI)‐based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning‐based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. This review discusses the principles underlying AI‐based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. Furthermore, the advantages of AI‐based segmentation are highlighted, such as improved reproducibility, faster processing times, and potential for personalized medicine. Despite these advancements, AI‐based segmentation faces significant challenges, including the need for standardized protocols, extensive validation studies, and seamless integration into clinical workflows. Addressing these limitations is essential for the widespread adoption of AI‐based segmentation in 68Ga‐PSMA PET for PC, ultimately advancing the field and improving patient care. |
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| ISSN: | 2398-7324 |