TY - JOUR

T1 - Inverse radiotherapy planning based on bioeffect modelling for locally advanced left-sided breast cancer

AU - Stick, Line Bjerregaard

AU - Vogelius, Ivan Richter

AU - Modiri, Arezoo

AU - Rice, Stephanie Renee

AU - Maraldo, Maja Vestmø

AU - Sawant, Amit

AU - Bentzen, Søren M

N1 - Copyright © 2019 Elsevier B.V. All rights reserved.

PY - 2019

Y1 - 2019

N2 - BACKGROUND AND PURPOSE: Treatment planning of radiotherapy (RT) for left-sided breast cancer is a challenging case. Several competing concerns are incorporated at present through protocol-defined dose-volume constraints, e.g. cardiac exposure and target coverage. Such constraints are limited by neglecting patient-specific risk factors (RFs). We propose an alternative RT planning method based solely on bioeffect models to minimize the estimated risks of breast cancer recurrence (BCR) and radiation-induced mortality endpoints considering patient-specific factors.METHODS AND MATERIALS: Thirty-nine patients with left-sided breast cancer treated with comprehensive post-lumpectomy loco-regional conformal RT were included. An in-house particle swarm optimization (PSO) engine was used to choose fields from a large set of predefined fields and optimize monitor units to minimize the total risk of BCR and mortality caused by radiation-induced ischaemic heart disease (IHD), secondary lung cancer (SLC) and secondary breast cancer (SBC). Risk models included patient age, smoking status and cardiac risk and were developed using published multi-institutional data.RESULTS: For the clinical plans the normal tissue complication probability, i.e. summed risk of IHD, SLC and SBC, was <3.7% and the risk of BCR was <6.1% for all patients. Median total decrease in mortality or recurrence achieved with individualized PSO plans was 0.4% (range, 0.06-2.0%)/0.5% (range, 0.11-2.2%) without/with risk factors.CONCLUSIONS: Inverse RT plan optimization using bioeffect probability models allows individualization according to patient-specific risk factors. The modelled benefit when compared to clinical plans is, however, modest in most patients, demonstrating that current clinical plans are close to optimal. Larger gains may be achievable with morbidity endpoints rather than mortality.

AB - BACKGROUND AND PURPOSE: Treatment planning of radiotherapy (RT) for left-sided breast cancer is a challenging case. Several competing concerns are incorporated at present through protocol-defined dose-volume constraints, e.g. cardiac exposure and target coverage. Such constraints are limited by neglecting patient-specific risk factors (RFs). We propose an alternative RT planning method based solely on bioeffect models to minimize the estimated risks of breast cancer recurrence (BCR) and radiation-induced mortality endpoints considering patient-specific factors.METHODS AND MATERIALS: Thirty-nine patients with left-sided breast cancer treated with comprehensive post-lumpectomy loco-regional conformal RT were included. An in-house particle swarm optimization (PSO) engine was used to choose fields from a large set of predefined fields and optimize monitor units to minimize the total risk of BCR and mortality caused by radiation-induced ischaemic heart disease (IHD), secondary lung cancer (SLC) and secondary breast cancer (SBC). Risk models included patient age, smoking status and cardiac risk and were developed using published multi-institutional data.RESULTS: For the clinical plans the normal tissue complication probability, i.e. summed risk of IHD, SLC and SBC, was <3.7% and the risk of BCR was <6.1% for all patients. Median total decrease in mortality or recurrence achieved with individualized PSO plans was 0.4% (range, 0.06-2.0%)/0.5% (range, 0.11-2.2%) without/with risk factors.CONCLUSIONS: Inverse RT plan optimization using bioeffect probability models allows individualization according to patient-specific risk factors. The modelled benefit when compared to clinical plans is, however, modest in most patients, demonstrating that current clinical plans are close to optimal. Larger gains may be achievable with morbidity endpoints rather than mortality.

U2 - 10.1016/j.radonc.2019.03.018

DO - 10.1016/j.radonc.2019.03.018

M3 - Journal article

C2 - 31015135

VL - 136

SP - 9

EP - 14

JO - Radiotherapy & Oncology

JF - Radiotherapy & Oncology

SN - 0167-8140

ER -