Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Sunday
May 08
14:15 - 15:15
Mini-Oral Theatre 1
13: Implementation of new technology
Livia Marrazzo, Italy;
Stefanie Ehrbar, Switzerland
2430
Mini-Oral
Physics
No need for manual adjustments of deep learning segmentation in oropharyngeal cancer?
Hanne van de Glind, The Netherlands
MO-0549

Abstract

No need for manual adjustments of deep learning segmentation in oropharyngeal cancer?
Authors:

Hanne van de Glind1, Ilse G. van Bruggen2, Johannes A. Langendijk2, Stefan Both2, Charlotte L. Brouwer2

1Universitair Medisch Centrum Groningen , Department of Radiation Oncology, Groningen, The Netherlands; 2Universitair Medisch Centrum Groningen, Department of Radiation Oncology, Groningen, The Netherlands

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Purpose or Objective

Delineation of organs at risk (OAR’s) plays a critical role in radiotherapy treatment planning. However, the segmentation of OAR’s can be very time-consuming, especially in the head and neck region. The accuracy of our deep learning based automated segmentation is currently within interobserver variability, however the influence of its use in treatment planning – without performing manual adjustments – is still unclear. We compared dose and normal tissue complication probability (NTCP) of fully automated vs. adjusted deep learning contouring (DLC) for volumetric modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT).

Material and Methods

A test set of 10 patients, who were treated for oropharyngeal cancer between February 2021 and July 2021 in the UMCG, was selected. The patients were treated with a prescribed dose of 70 Gy in 35 fractions to the primary tumour and 54.25 Gy to the elective lymph node areas. DLC (Mirada Medical, Oxford, United Kingdom) was used for automated segmentation of OAR’s. Treatment plans were created in RayStation Development 10B using internally validated machine learning based automated planning models (RaySearch Laboratories, AB, Stockholm, Sweden). For every patient four plans were created: DLC VMAT, DLC adjusted VMAT, DLC IMPT and DLC adjusted IMPT. The DLC plan is optimized using automatically segmented DLC. The DLC adjusted plan is optimized using  manual adjusted DLC by a radiation therapy technician and approved by a radiation oncologist. NTCP values for the development of late xerostomia and dysphagia were calculated and compared between the DLC and DLC adjusted plans. The input variables to the NTCP model for xerostomia were the mean planned dose to the parotid and submandibular glands, and for dysphagia the mean dose to the oral cavity and pharyngeal constrictor muscles based on the DLC and DLC adjusted plans. All dose and NTCP variables were derived using the manually adjusted contours. 

Results

The mean dose differences between the DLC and DLC adjusted plans for the OAR’s relevant for the NTCP’s were within 0.07 Gy (Table 1a). The mean difference in NTCP (NTCP DLC – NTCP DLC adjusted) was small: -0.08 percentage point (pp) for VMAT and 0.11 pp for IMPT for xerostomia grade II, and 0.16 pp for both VMAT and IMPT for dysphagia grade II (Table 1b). Statistically significant difference was found between the NTCP of the DLC and DLC adjusted IMPT plans for dysphagia (p = 0.01), as shown in Figure 1.

Conclusion

Mean OAR dose and NTCP values resulting from VMAT and IMPT plans based on automated DLC are in agreement with VMAT and IMPT plans based on manually adjusted DLC (mean dose deviations within 0.07 Gy, NTCP within 1 pp). This suggests a limited effect of the manual adjustments made to automatically segmented DLC for  VMAT and IMPT treatment of oropharyngeal cancer patients.