Optimal Timing of Organs-at-risk-sparing Adaptive Radiation Therapy for Head-and-neck Cancer under Re-planning Resource Constraints.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
07 Oct 2024
Historique:
medline: 17 10 2024
pubmed: 17 10 2024
entrez: 17 10 2024
Statut: epublish

Résumé

Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-plannings, which represent a one-size-fits-all approach and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, specifically for patients with head and neck cancer (HNC). A novel Markov decision process (MDP) model was developed to determine optimal timing of re-plannings based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated at the University of Texas MD Anderson Cancer Center between 2007 and 2013. Optimal re-planning strategies were obtained when the permissible number of re-plannings throughout the treatment was limited to 1, 2, and 3. The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3). The MDP model determines the optimal timing for implementing patient-specific adaptive re-planning. This approach incorporates ΔNTCP thresholds and considers varying total re-plannings. The methods are versatile and applicable across cancer types, institutional settings, and different OARs and NTCP models.

Sections du résumé

Background and Purpose UNASSIGNED
Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-plannings, which represent a one-size-fits-all approach and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, specifically for patients with head and neck cancer (HNC).
Methods and Materials UNASSIGNED
A novel Markov decision process (MDP) model was developed to determine optimal timing of re-plannings based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated at the University of Texas MD Anderson Cancer Center between 2007 and 2013. Optimal re-planning strategies were obtained when the permissible number of re-plannings throughout the treatment was limited to 1, 2, and 3.
Results UNASSIGNED
The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3).
Conclusion UNASSIGNED
The MDP model determines the optimal timing for implementing patient-specific adaptive re-planning. This approach incorporates ΔNTCP thresholds and considers varying total re-plannings. The methods are versatile and applicable across cancer types, institutional settings, and different OARs and NTCP models.

Identifiants

pubmed: 39417124
doi: 10.1101/2024.04.01.24305163
pmc: PMC11482873
pii:
doi:

Types de publication

Journal Article Preprint

Langues

eng

Auteurs

Classifications MeSH