{"doi":"10.1002/mp.17969","title":"NTCP‐constrained combined proton‐photon treatment planning to maximize utilization of limited proton slots","abstract":"Abstract Background Proton therapy provides superior organ‐at‐risk (OAR) sparing and normal tissue complication probabilities (NTCP) benefits compared to photon therapy, but is less accessible and more expensive. Purpose This study introduces an NTCP‐constrained combined proton‐photon treatment (NTCP–CPPT) planning method to optimize the utilization of limited proton resources. The method aims to determine minimal proton fractions and an optimal fluence map to achieve target dose quality, OAR sparing, and NTCP benefits comparable to proton therapy. Methods The bilevel optimization framework is employed to optimize the optimal proton fraction and fluence maps for each patient. In the lower‐level model, the fluence map optimization problem simultaneously optimizes the proton and photon fluence map to minimize biologically effective dose (BED) objectives while satisfying the BED constraint and NTCP hard constraints for a given proton fraction from 0 to N max , which is solved using the interior point method. In the upper‐level model, the optimal number of proton fractions and the corresponding fluence map are selected from the lower‐level optimized solutions to meet clinical dose goals. Results For four head‐and‐neck (HN) patients, NTCP–CPPT reduced the average number of proton fractions from 21 to 15 compared to conventional combined proton‐photon treatment (CONV–CPPT) without an NTCP hard constraint, while still maintaining the NTCP threshold for patient selection and ensuring tumor control. Furthermore, NTCP–CPPT required only 24 proton fractions to achieve a sum‐NTCP comparable to a 35‐fraction IMPT treatment. Conclusions NTCP–CPPT minimizes the required proton fractions while preserving adequate target dose quality and OAR sparing benefits. This method has the potential to reduce treatment costs and increase patient access to proton therapy.","journal":"Medical Physics","year":2025,"id":550883,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":1304059,"name":"Xu Liu","orcid":"0000-0002-9067-3393","position":1,"is_corresponding":false},{"id":1446422,"name":"Ji Li","orcid":"0000-0001-5990-8396","position":2,"is_corresponding":false},{"id":887029,"name":"Wangyao Li","orcid":"0000-0001-9312-4931","position":3,"is_corresponding":false},{"id":363817,"name":"Yuting Lin","orcid":"0000-0001-6227-8321","position":4,"is_corresponding":false},{"id":1446423,"name":"Bin Qin","orcid":"0000-0001-6208-5415","position":5,"is_corresponding":false},{"id":648436,"name":"Hao Gao","orcid":"0000-0003-1064-2149","position":6,"is_corresponding":false},{"id":1446421,"name":"Wei Wang","orcid":"0009-0006-4755-1194","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T02:54:20.915388Z","pmid":"40781837","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":[]}