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Pract Radiat Oncol. 2021 Oct 17:S1879-8500(21)00279-4. doi: 10.1016/j.prro.2021.10.001. Online ahead of print.


PURPOSE: To describe the feasibility of the online adaptive radiotherapy (oART) method developed at the Hospital Quirónsalud Barcelona (HQSB) for prostate cancer, using a standard C-arm linac and without the support of artificial intelligence (AI).

MATERIALS AND METHODS: The first eighteen patients treated at the HQSB with the developed oART method are included. An ultra-hypofractionated radiotherapy (UHRT) scheme consists of 7 × 6.1 Gy was used. Patients were treated on two conventional Varian C-arm linacs. For each patient, a reference plan based on a planning CT (pCT) was generated using the Eclipse system. On each treatment session, the pCT scan was rigidly registered with the daily CBCT scan. The pCT-based target (prostate) and organs-at-risk (OARs) were mapped onto the CBCT images and manually adapted to take into account the anatomy-of-the-day. The reference plan was then copied to the CBT scan and full re-optimization is done for the current anatomy (adapted plan). For each treatment session, the unaltered reference plan recomputed on the daily CBCT scan by mimicking the soft-tissue alignment performed in our standard procedure (non-adapted plan). Over the 126 adapted sessions from the 18 patients, dosimetric comparison of adapted against non-adapted plans wasdone.

RESULTS: A significant difference in the target coverage was found between the adapted and non-adapted plans (97.1 vs. 90.4, p < 0.001), in favor of adapting. Without online adaptation, the optimal coverage of the prostate was not attained in 35% of fractions. Adapting allows improving the target coverage with compliance of all OAR dose constraints in all treatment fractions CONCLUSIONS: The oART technique described in this study is technically feasible with a C-arm linac. To our knowledge, this is the first clinical experience with oART for prostate cancer including full re-planning and delivered with a C-arm linac without AI capability.

PMID:34670139 | DOI:10.1016/j.prro.2021.10.001