Multicriteria optimization enables less experienced planners to efficiently produce high quality treatment plans in head and neck cancer radiotherapy

Roel G J Kierkels; Ruurd Visser; Hendrik P Bijl; Johannes A Langendijk; Aart A van 't Veld; Roel J H M Steenbakkers; Erik W Korevaar

doi:10.1186/s13014-015-0385-9

Multicriteria optimization enables less experienced planners to efficiently produce high quality treatment plans in head and neck cancer radiotherapy

Radiat Oncol. 2015 Apr 12:10:87. doi: 10.1186/s13014-015-0385-9.

Authors

Roel G J Kierkels¹, Ruurd Visser^{2

3}, Hendrik P Bijl⁴, Johannes A Langendijk⁵, Aart A van 't Veld⁶, Roel J H M Steenbakkers⁷, Erik W Korevaar⁸

Affiliations

¹ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
² Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
³ Department of Medical Imaging and Radiation Therapy, Hanze University of Applied Sciences, Groningen, the Netherlands. [email protected].
⁴ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
⁵ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
⁶ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
⁷ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].
⁸ Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen, 9700 RB, the Netherlands. [email protected].

Abstract

Objectives: To demonstrate that novice dosimetry planners efficiently create clinically acceptable IMRT plans for head and neck cancer (HNC) patients using a commercially available multicriteria optimization (MCO) system.

Methods: Twenty HNC patients were enrolled in this in-silico comparative planning study. Per patient, novice planners with less experience in dosimetry planning created an IMRT plan using an MCO system (RayStation). Furthermore, a conventionally planned clinical IMRT plan was available (Pinnacle(3)). All conventional IMRT and MCO-plans were blind-rated by two expert radiation-oncologists in HNC, using a 5-point scale (1-5 with 5 the highest score) assessment form comprising 10 questions. Additionally, plan quality was reported in terms of planning time, dosimetric and normal tissue complication probability (NTCP) comparisons. Inter-rater reliability was derived using the intra-class correlation coefficient (ICC).

Results: In total, the radiation-oncologists rated 800 items on plan quality. The overall plan score indicated no differences between both planning techniques (conventional IMRT: 3.8 ± 1.2 vs. MCO: 3.6 ± 1.1, p = 0.29). The inter-rater reliability of all ratings was 0.65 (95% CI: 0.57-0.71), indicating substantial agreement between the radiation-oncologists. In 93% of cases, the scoring difference of the conventional IMRT and MCO-plans was one point or less. Furthermore, MCO-plans led to slightly higher dose uniformity in the therapeutic planning target volume, to a lower integral body dose (13.9 ± 4.5 Gy vs. 12.9 ± 4.0 Gy, p < 0.001), and to reduced dose to the contra-lateral parotid gland (28.1 ± 11.8 Gy vs. 23.0 ± 11.2 Gy, p < 0.002). Consequently, NTCP estimates for xerostomia reduced by 8.4 ± 7.4% (p < 0.003). The hands-on time of the conventional IMRT planning was approximately 205 min. The time to create an MCO-plan was on average 43 ± 12 min.

Conclusions: MCO planning enables novice treatment planners to create high quality IMRT plans for HNC patients. Plans were created with vastly reduced planning times, requiring less resources and a short learning curve.

MeSH terms

Aged
Algorithms*
Carcinoma, Squamous Cell / radiotherapy*
Computer Simulation
Female
Head and Neck Neoplasms / radiotherapy*
Humans
Male
Middle Aged
Prospective Studies
Quality Assurance, Health Care*
Radiometry
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted / methods*
Radiotherapy, Intensity-Modulated / methods*
Radiotherapy, Intensity-Modulated / standards*