AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits
Abstract Aims Recent advances in Radiotherapy have led to the development of online adaptive RT (oART), a procedure addressing inter-fraction anatomical variations. Integrating artificial intelligence (AI) into the oART procedure speeds up the process and reduces user dependency. This study investig...
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BMC
2025-07-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-025-02697-6 |
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| author | Francesco Preziosi Althea Boschetti Francesco Catucci Claudio Votta Luca Vellini Sebastiano Menna Flaviovincenzo Quaranta Elisa Pilloni Andrea D’Aviero Michele Aquilano Carmela Di Dio Martina Iezzi Alessia Re Antonio Piras Marco Marras Francesca Gruosso Domenico Piro Danila Piccari Luca Tagliaferri Maria Antonietta Gambacorta Luca Indovina Gian Carlo Mattiucci Davide Cusumano |
| author_facet | Francesco Preziosi Althea Boschetti Francesco Catucci Claudio Votta Luca Vellini Sebastiano Menna Flaviovincenzo Quaranta Elisa Pilloni Andrea D’Aviero Michele Aquilano Carmela Di Dio Martina Iezzi Alessia Re Antonio Piras Marco Marras Francesca Gruosso Domenico Piro Danila Piccari Luca Tagliaferri Maria Antonietta Gambacorta Luca Indovina Gian Carlo Mattiucci Davide Cusumano |
| author_sort | Francesco Preziosi |
| collection | DOAJ |
| description | Abstract Aims Recent advances in Radiotherapy have led to the development of online adaptive RT (oART), a procedure addressing inter-fraction anatomical variations. Integrating artificial intelligence (AI) into the oART procedure speeds up the process and reduces user dependency. This study investigates the dosimetric advantage of implementing AI-driven oART in prostate cancer. Methods A total of 31 prostate cancer patients treated with oART on an AI-integrated Linac were analyzed. Patients were categorized by nodal involvement. For prostate-only cases, the Clinical Target Volume (CTV) included the prostate and seminal vesicles (CTV1), with a 5 mm margin (8 mm caudally) for Planning Target Volume (PTV), named PTV1. For nodal cases, pelvic lymph nodes were added (and categorized as CTV2) with a 5 mm isotropic margin (PTV2). Daily CBCTs were acquired, with OARs (rectum, bladder, bowels) automatically segmented by the AI system, while targets were manually delineated. Two plans were generated: a predicted one, calculating the original plan’s fluence on daily anatomy, and an adapted one, with complete fluence re-optimization. Daily DVH indicators for PTV(V95%), CTV(D98%), bladder (V65Gy), bowel (V45Gy), and rectum (V50Gy) were compared between predicted and adapted plans using the Wilcoxon-Mann-Whitney test. Total session time, from CBCT acquisition to treatment completion, was also recorded. Results oART treatment improved prostate coverage in both patient groups (+10.4% and +11.8% in PTV V95% for patients with and without lymph nodes) and CTV D98% (+2.6% with lymph nodes, +2.9% without). Improvements for arm 2 were smaller (+3.1% in PTV2 V95%, +2.2% in CTV2 D98%). Statistical differences were insignificant in OAR DVH indicators (p > 0.1). Median treatment time was 25 min and 32 min for prostate-only and lymph node cases, respectively. Conclusion This study demonstrates that oART in prostate cancer results in a significant improvement in target coverage with no significant difference in OARs. |
| format | Article |
| id | doaj-art-f022f4dceb7f4e6db3e3cd95a9106522 |
| institution | DOAJ |
| issn | 1748-717X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-f022f4dceb7f4e6db3e3cd95a91065222025-08-20T03:05:13ZengBMCRadiation Oncology1748-717X2025-07-012011810.1186/s13014-025-02697-6AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefitsFrancesco Preziosi0Althea Boschetti1Francesco Catucci2Claudio Votta3Luca Vellini4Sebastiano Menna5Flaviovincenzo Quaranta6Elisa Pilloni7Andrea D’Aviero8Michele Aquilano9Carmela Di Dio10Martina Iezzi11Alessia Re12Antonio Piras13Marco Marras14Francesca Gruosso15Domenico Piro16Danila Piccari17Luca Tagliaferri18Maria Antonietta Gambacorta19Luca Indovina20Gian Carlo Mattiucci21Davide Cusumano22Mater Olbia HospitalMater Olbia HospitalMater Olbia HospitalFondazione Policlinico Universitario “Agostino Gemelli” IRCCSMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalUO Radioterapia Oncologica, Villa Santa TeresaMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalMater Olbia HospitalFondazione Policlinico Universitario “Agostino Gemelli” IRCCSFondazione Policlinico Universitario “Agostino Gemelli” IRCCSFondazione Policlinico Universitario “Agostino Gemelli” IRCCSMater Olbia HospitalMater Olbia HospitalAbstract Aims Recent advances in Radiotherapy have led to the development of online adaptive RT (oART), a procedure addressing inter-fraction anatomical variations. Integrating artificial intelligence (AI) into the oART procedure speeds up the process and reduces user dependency. This study investigates the dosimetric advantage of implementing AI-driven oART in prostate cancer. Methods A total of 31 prostate cancer patients treated with oART on an AI-integrated Linac were analyzed. Patients were categorized by nodal involvement. For prostate-only cases, the Clinical Target Volume (CTV) included the prostate and seminal vesicles (CTV1), with a 5 mm margin (8 mm caudally) for Planning Target Volume (PTV), named PTV1. For nodal cases, pelvic lymph nodes were added (and categorized as CTV2) with a 5 mm isotropic margin (PTV2). Daily CBCTs were acquired, with OARs (rectum, bladder, bowels) automatically segmented by the AI system, while targets were manually delineated. Two plans were generated: a predicted one, calculating the original plan’s fluence on daily anatomy, and an adapted one, with complete fluence re-optimization. Daily DVH indicators for PTV(V95%), CTV(D98%), bladder (V65Gy), bowel (V45Gy), and rectum (V50Gy) were compared between predicted and adapted plans using the Wilcoxon-Mann-Whitney test. Total session time, from CBCT acquisition to treatment completion, was also recorded. Results oART treatment improved prostate coverage in both patient groups (+10.4% and +11.8% in PTV V95% for patients with and without lymph nodes) and CTV D98% (+2.6% with lymph nodes, +2.9% without). Improvements for arm 2 were smaller (+3.1% in PTV2 V95%, +2.2% in CTV2 D98%). Statistical differences were insignificant in OAR DVH indicators (p > 0.1). Median treatment time was 25 min and 32 min for prostate-only and lymph node cases, respectively. Conclusion This study demonstrates that oART in prostate cancer results in a significant improvement in target coverage with no significant difference in OARs.https://doi.org/10.1186/s13014-025-02697-6Online adaptive radiotherapyProstate CancerArtificial intelligence |
| spellingShingle | Francesco Preziosi Althea Boschetti Francesco Catucci Claudio Votta Luca Vellini Sebastiano Menna Flaviovincenzo Quaranta Elisa Pilloni Andrea D’Aviero Michele Aquilano Carmela Di Dio Martina Iezzi Alessia Re Antonio Piras Marco Marras Francesca Gruosso Domenico Piro Danila Piccari Luca Tagliaferri Maria Antonietta Gambacorta Luca Indovina Gian Carlo Mattiucci Davide Cusumano AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits Radiation Oncology Online adaptive radiotherapy Prostate Cancer Artificial intelligence |
| title | AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits |
| title_full | AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits |
| title_fullStr | AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits |
| title_full_unstemmed | AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits |
| title_short | AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits |
| title_sort | ai driven online adaptive radiotherapy in prostate cancer treatment considerations on activity time and dosimetric benefits |
| topic | Online adaptive radiotherapy Prostate Cancer Artificial intelligence |
| url | https://doi.org/10.1186/s13014-025-02697-6 |
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