The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma
The integration of artificial intelligence (AI) into stereotactic ablative body radiotherapy (SABR) for hepatocellular carcinoma (HCC) is transforming the landscape of liver cancer treatment. SABR has emerged as a promising treatment option for patients with localized HCC, offering high local contro...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1576494/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850232306547032064 |
|---|---|
| author | Atsuto Katano |
| author_facet | Atsuto Katano |
| author_sort | Atsuto Katano |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) into stereotactic ablative body radiotherapy (SABR) for hepatocellular carcinoma (HCC) is transforming the landscape of liver cancer treatment. SABR has emerged as a promising treatment option for patients with localized HCC, offering high local control rates and favorable toxicity profiles. As evidence supporting SABR's clinical efficacy continues to grow, AI technologies are accelerating its adoption by enhancing precision, efficiency, and individualization of care. This review summarizes recent advances in AI applications across the SABR workflow, including automated contouring, knowledge-based planning, fluence prediction via deep learning, respiratory motion modeling, liver function estimation, and prognostic modeling. Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. However, critical limitations persist. Many AI models are trained on limited datasets without external validation, raising concerns about overfitting and generalizability. Future efforts should focus on improving model transparency, confirming their reliability across different institutions, and ensuring ethical use in real-world clinical practice. |
| format | Article |
| id | doaj-art-b2c7aadfb0f34b1ea8f4c88750d9fd49 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-b2c7aadfb0f34b1ea8f4c88750d9fd492025-08-20T02:03:14ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15764941576494The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinomaAtsuto KatanoThe integration of artificial intelligence (AI) into stereotactic ablative body radiotherapy (SABR) for hepatocellular carcinoma (HCC) is transforming the landscape of liver cancer treatment. SABR has emerged as a promising treatment option for patients with localized HCC, offering high local control rates and favorable toxicity profiles. As evidence supporting SABR's clinical efficacy continues to grow, AI technologies are accelerating its adoption by enhancing precision, efficiency, and individualization of care. This review summarizes recent advances in AI applications across the SABR workflow, including automated contouring, knowledge-based planning, fluence prediction via deep learning, respiratory motion modeling, liver function estimation, and prognostic modeling. Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. However, critical limitations persist. Many AI models are trained on limited datasets without external validation, raising concerns about overfitting and generalizability. Future efforts should focus on improving model transparency, confirming their reliability across different institutions, and ensuring ethical use in real-world clinical practice.https://www.frontiersin.org/articles/10.3389/fmed.2025.1576494/fullartificial intelligencestereotactic ablative body radiotherapy (SABR)hepatocellular carcinomaradiotherapytreatment planning |
| spellingShingle | Atsuto Katano The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma Frontiers in Medicine artificial intelligence stereotactic ablative body radiotherapy (SABR) hepatocellular carcinoma radiotherapy treatment planning |
| title | The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| title_full | The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| title_fullStr | The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| title_full_unstemmed | The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| title_short | The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| title_sort | use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma |
| topic | artificial intelligence stereotactic ablative body radiotherapy (SABR) hepatocellular carcinoma radiotherapy treatment planning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1576494/full |
| work_keys_str_mv | AT atsutokatano theuseofartificialintelligenceinstereotacticablativebodyradiotherapyforhepatocellularcarcinoma AT atsutokatano useofartificialintelligenceinstereotacticablativebodyradiotherapyforhepatocellularcarcinoma |