Online

ESTRO 2020

Session Item

Sunday
November 29
13:15 - 14:30
Clinical Stream 2
Second malignancy after modern radiotherapy: more or less than historical precedents
2400
Symposium
Clinical
15:45 - 15:50
A Prospective Clinical Evaluation of Mirada DLCExpert Auto-Contouring for Head and Neck OARs.
PD-0434

Abstract

A Prospective Clinical Evaluation of Mirada DLCExpert Auto-Contouring for Head and Neck OARs.
Authors: South|, Chris(1)[csouth@nhs.net];Navarro|, Clara(2);Rickard|, Donna J(2);Lynch|, Joanna(3);Wood|, Katie(3);Nisbet|, Andrew(4);Adams|, Elizabeth J(2)*;
(1)St. Luke's Cancer Centre Royal Surrey County Hosp, Radiotherapy Physics, Guildford, United Kingdom;(2)St. Luke's Cancer Centre Royal Surrey County Hospital, Radiotherapy Physics, Guildford, United Kingdom;(3)St. Luke's Cancer Centre Royal Surrey County Hospital, Oncology, Guildford, United Kingdom;(4)University College London, Medical Physics and Clinical Engineering, London, United Kingdom;
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Purpose or Objective

It is well established that patients with cancers of the head and neck (H&N) are adversely affected by treatment delays. Availability of qualified clinicians is often the rate-limiting step in the treatment planning pathway, and manual contouring of targets and organs-at-risk (OARs) is the most time-intensive stage for clinicians. Novel AI-based auto-contouring algorithms are becoming commercially available. We aimed to test whether using Mirada DLCExpert (Mirada-Medical, Oxford, UK) with a H&N model trained at another institution could improve clinical contouring efficiency for OARs.

Material and Methods

DLCExpert was used to generate spinal cord, brainstem and parotid volumes for 10 H&N patients previously contoured by experienced clinical oncologists at our institution. The quality of the automatically-generated contours was assessed by: quantitative measurement of congruence with existing clinical contours (centre-of-mass shift; dice similarity co-efficient (DSC)); blinded review of both existing clinical contours and DLCExpert contours, scored 1-5 based on level of modification required (1=complete; 2=major; 3=minor; 4=insignificant; 5=none). Time taken to generate clinically acceptable contours for all OARs routinely outlined in our institution (those listed above, plus orbits, lenses, optic nerves and chiasm) was prospectively recorded for 9 H&N patients without use of Mirada, and for 7 patients for whom auto-contouring was used (DLCExpert for OARs listed above, and Mirada Embrace atlas-based software for the optical structures, for which the DLCExpert model had not been trained). All contouring, reviewing, editing and scoring of contours was carried out by a consultant clinical oncologist with extensive H&N experience.

Results

Mean centre-of-mass shifts were close to zero, indicating no systematic spatial shift of DLCExpert contours with respect to clinician contours. DSC averaged 0.80 across all structures (see table 1).

Table 1: Mean centre of mass shift and DSC for the 10 test patients.



In the blind evaluation, the scores for clinical versus DLCExpert contours were: 3.9 vs 2.6 for Brain stem; 4.7 vs 3.6 for Spinal cord; 4.4 vs 3.6 for left parotid; 4.8 vs 3.5 for right parotid. Overall, 33% of structures were clinically acceptable with no amendments, with 50% requiring minor and 18% major adjustments.
Figure 1 shows the proportion of contours requiring major amendment for each OAR, versus those requiring minor or no adjustment.
Figure 1: Percentage of DLCExpert contours requiring major amendments versus minor or no amendments.


Without the use of Mirada software, OAR contouring took an average of 10.4 minutes (range 8-18 minutes); with auto-contouring, review and amendment of OAR contours took 8.4 minutes (range 8-9 minutes).

Conclusion

Whilst the automatically generated contours were scored inferior to the clinicians own contours, over 80% of contours required no more than minor amendments, with 33% requiring no adjustment. This led to a reduction of approximately 20% in the time taken for a clinician to contour OARs for a H&N patient.