Introduction
Variation in target volume delineation from clinical trial protocols has been shown to contribute to poorer patient outcomes. A clinical trial quality assurance framework can support compliance with trial protocol. Results of the TROG 08.03 RAVES benchmarking exercise considering variation from protocol, inter‐observer variability and impact on dosimetry are reported in this paper.
Methods
Clinicians were required to contour and plan a benchmarking case according to trial protocol. Geometric pjmirometers including volume, Hausdorff Distance, Mean Distance to Agreement and DICE similarity coefficient were analysed for targets and organs at risk. Submitted volumes were compared to a STAPLE and consensus ‘reference’ volume for each structure. Dosimetric analysis was performed using dose volume histogram data.
Results
Benchmarking exercise submissions were received from 96 clinicians. In total 205 protocol variations were identified. The most common variation was inadequate contouring of the CTV in 84/205 (41%). The CTV volume ranged from 65.3 to 193.1 cm3 with a median of 113.2 cm3. The most common dosimetric protocol variation related to rectal dosimetry. The mean submitted rectal volume receiving 40 Gy and 60 Gy, respectively, was 56.14% ± 5.55% and 30.25% ± 6.15%. When corrected to the protocol defined length the mean rectal volume receiving 40 Gy was 60.8% ± 7.92%, while the volume receiving 60 Gy was 33.86% ± 8.21%.
Conclusion
Variations from protocol were found in the RAVES benchmarking exercise, most notably in CTV and rectum delineation. Inter‐observer variability was evident. Incorrect delineation of the rectum impacted on dosimetric compliance with protocol.
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.
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