The quantitative impact of joint peer review with a specialist radiologist in head and neck cancer radiotherapy planning.

OBJECTIVES: Radiologist input in peer review of head and neck radiotherapy has been introduced as a routine departmental approach. The aim was to evaluate this practice and to quantitatively analyse the changes made. METHODS: Patients treated with radical-dose radiotherapy between August and November 2020 were reviewed. The incidence of major and minor changes, as defined by The Royal College of Radiologists guidance, was prospectively recorded. The amended radiotherapy volumes were compared with the original volumes using Jaccard Index (JI) to assess conformity; Geographical Miss Index (GMI) for undercontouring; and Hausdorff Distance (HD) between the volumes. RESULTS: In total, 73 out of 87 (84%) patients were discussed. Changes were recommended in 38 (52%) patients: 30 had ≥1 major change, eight had minor changes only. There were 99 amended volumes: The overall median JI, GMI and HD was 0.91 (interquartile range [IQR]=0.80-0.97), 0.06 (IQR = 0.02-0.18) and 0.42 cm (IQR = 0.20-1.17 cm), respectively. The nodal gross-tumour-volume (GTVn) and therapeutic high-dose nodal clinical-target-volume (CTVn) had the biggest magnitude of changes: The median JI, GMI and HD of GTVn was 0.89 (IQR = 0.44-0.95), 0.11 (IQR = 0.05-0.51), 3.71 cm (IQR = 0.31-6.93 cm); high-dose CTVn was 0.78 (IQR = 0.59-0.90), 0.20 (IQR = 0.07-0.31) and 3.28 cm (IQR = 1.22-6.18 cm), respectively. There was no observed difference in the quantitative indices of the 85 'major' and 14 'minor' volumes (p = 0.5). CONCLUSIONS: Routine head and neck radiologist input in radiotherapy peer review is feasible and can help avoid gross error in contouring. ADVANCES IN KNOWLEDGE: The major and minor classifications may benefit from differentiation with quantitative indices but requires correlation from clinical outcomes.

Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning.

BACKGROUND AND PURPOSE: Peer-review of Target Volume (TV) and Organ at Risk (OAR) contours in radiotherapy planning are typically conducted visually; this can be time consuming and subject to interobserver variation. This study investigated automatic evaluation of contouring using conformity indices and supervised machine learning. METHODS: A total of 393 contours from 253 Stereotactic Ablative Body Radiotherapy (SABR) benchmark cases (adrenal gland, liver, pelvic lymph node and spine), delineated by 132 clinicians from 25 centres, were visually evaluated for conformity against gold standard contours. Contours were scored as "pass" or "fail" on visual peer review and six Conformity Indices (CIs) were applied. CI values were mapped to pass/fail scores for each contour and used to train supervised machine learning models. A 5-fold cross validation method was employed to determine the predictive accuracies of each model. RESULTS: The stomach structure produced models with the highest predictive accuracy overall (96% using Support Vector Machine and Ensemble models), whilst the liver GTV produced models with the lowest predictive accuracy (76% using Logistic Regression). Predictive accuracies across all models ranged from 68-96% (68-87% for TV and 71-96% for OARs). CONCLUSIONS: Although a final visual review by an experienced clinician is still required, the automatic contour evaluation method could reduce the time for benchmark case reviews by identifying gross contouring errors. This method could be successfully implemented to support departmental training and the continuous assessment of outlining for clinical staff in the peer-review process, to reduce interobserver variability in contouring and improve interpretation of radiological anatomy.