{"doi":"10.1016/j.brachy.2025.08.004","title":"Integration of single-click, AI-based brachytherapy auto-planning for cervical cancer within a treatment planning system","abstract":"PURPOSE: Previous work developed an automated cervical brachytherapy treatment planning pipeline consisting of a U-Net dose prediction model and dwell time optimizer. While this method can produce clinically acceptable plans, it relies on time-consuming, manual export and import of DICOM data. This study proposes to increase efficiency by combining scripts into an all-in-one tool that can be used directly within the BrachyVision treatment planning system, producing automated plans in a single click. MATERIALS AND METHODS: We developed an AI-based planning tool through four main tasks; data retrieval, model inference, dwell time optimization, and auto-plan import. First, a C# plug-in interacts with the currently open patient in BrachyVision. Next, for data retrieval, model inference, and dwell time optimization, a Python executable operates on the data before the optimized dwell times are copied back into the open plan in BrachyVision. The script was tested on 28 brachytherapy plans spanning 7 applicator types, and auto-plans were compared to clinical plans using mean absolute error (MAE) in voxel-based 3D dose and dwell times. RESULTS: The average (± standard deviation) MAE in 3D dose and dwell times were 3.8 ± 0.7% (normalized to the prescribed dose) and 10.3 ± 7.4 s (2.1 ± 0.9% of the total plan dwell time), respectively. The average runtime of the script was 3.5 ± 1.2 minutes. CONCLUSIONS: We developed a script that enables efficient, streamlined auto-planning directly within BrachyVision. After contouring and digitization are performed, the script can be run to produce high-quality, customized plans with a single button-click in a few minutes.","journal":"Brachytherapy","year":2025,"id":547853,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9505,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1283660,"name":"Lance C. Moore","orcid":null,"position":1,"is_corresponding":false},{"id":1441079,"name":"Aranyo Mitra","orcid":null,"position":2,"is_corresponding":false},{"id":987511,"name":"Karoline Kallis","orcid":"0000-0002-0889-004X","position":3,"is_corresponding":false},{"id":375707,"name":"Kelly Kisling","orcid":"0000-0002-0313-6558","position":4,"is_corresponding":false},{"id":71472,"name":"Nuno Vasconcelos","orcid":"0000-0002-9024-4302","position":5,"is_corresponding":false},{"id":1283263,"name":"Sandra M. Meyers","orcid":"0000-0002-0159-9110","position":6,"is_corresponding":false},{"id":1441078,"name":"Ryan Truong","orcid":null,"position":0,"is_corresponding":true}],"reference_count":14,"raw_metadata":null,"created_at":"2026-07-19T02:53:53.962932Z","pmid":"41130872","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}