A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

Abstract

Spatiotemporal optimization of patient plans has the potential to significantly improve local tumor control (larger BED/EUD) of patients with a favorable geometry, such as smaller tumors with larger distances between the tumor target and nearby OAR. In patients with a less favorable geometry and for fast growing tumors, plans optimized using spatiotemporal optimization and conventional (spatial-only) optimization are equivalent (negligible differences in tumor BED/EUD). However, spatiotemporal optimization yields shorter treatment courses than conventional spatial-only optimization. Personalized, spatiotemporal optimization of treatment schedules can increase patient convenience and help with the efficient allocation of clinical resources. Spatiotemporal optimization can also help identify a subset of patients that might benefit from nonconventional (large dose per fraction) treatments that are ineligible for the current practice of stereotactic body radiation therapy.

An overview on personalisation of radiotherapy prescriptions in locally advanced non-small cell lung cancer: Are we there yet?

“…As a result, the findings were evaluated in a qualitative manner. 19 relevant publications were identified (Figure1) [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

“…Nine studies [24][25][26][27][28][29][30][31][32] were identified that outlined novel concepts which may prove to be a future basis for personalised RT prescriptions, findings are summarised in Table 3. These studies can be broadly categorised depending on the patient specific feature they are utilising as a basis for personalisation.…”

Mathematical Modeling in Radiation Oncology

“…These algorithms can also include objectives to be maximized, such as tumor size or cell kill . Groups have already demonstrated the feasibility of implementing spatial optimization of dose using multi-objective evolutionary algorithm methods into a clinical workflow (Kim et al 2015;Smith et al 2016). The incorporation of patient-specific tumor growth and response models into this paradigm is a reasonable goal.…”

Within the next five years, most radiotherapy treatment schedules will be designed using spatiotemporal optimization

“…By incorporating tumor characteristics and calculated relative dose between tumor and OAR for each patient into FSO, it is possible to optimize fractionation schedules that lead to the maximum tumor BED for each patient. 7 Radiation oncology, in line with the rest of medicine, is moving toward evidence-based, personalized therapy. 8 The ultimate goal of radiotherapy is to allow patients to have longer and healthier lives.…”