To evaluate the difference in time taken to contour with and without the assistance of an auto-segmentation tool.

Acute clinician reported toxicity will be measured using CTCAE version 5.0 across individual items (see full protocol appendix). For this study, the outcome will be the difference in the proportion of patients with grade≥3 toxicity at any point in time from the start of radiotherapy to 90 days following radiotherapy.

Late clinician reported toxicity will be measured using CTCAE version 5.0 across individual items (see full protocol appendix). For this study, the outcome will be the difference in the proportion of patients with grade≥3 toxicity at any point in time between 90 days following radiotherapy and 5 years following radiotherapy.

General acute patient quality of life outcomes will be measured using the EORTC QLQ-C30 instrument. For this study, the outcome will be the difference in total scores and by domain at any point in time from the start of radiotherapy to 90 days following radiotherapy.

Acute patient reported toxicity will be measured using the EORTC QLQ-C30 and QLQ-BR45. For this study, the outcome will be the difference in total scores and by domain at any point in time between 90 days following radiotherapy and 5 years following radiotherapy.

Breast specific acute patient quality of life outcomes will be measured using the EORTC QLQ-BR45 instrument. For this study, the outcome will be the difference in total scores and by domain at any point in time from the start of radiotherapy to 90 days following radiotherapy.

Breast specific late patient quality of life outcomes will be measured using the EORTC QLQ-BR45 instrument. For this study, the outcome will be the difference in total scores and by domain at any point in time between 90 days following radiotherapy and 5 years following radiotherapy.

To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in surface dice similarity coefficient (sDSC).

To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in dice similarity coefficient (DSC).

To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in APL.

To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in MSHD.

To compare the dose volume histogram metrics per contoured structure between patient planned with AI contours and those planned with manual contours.

We will assess dosimetric differences to the clinical tumour volume (CTV), planning target volume (PTV) and organs at risk (OARs) between AI assisted contours before and after manual edits. The measure will be in the proportion of patients who pass all planning constraints as per the FAST FORWARD protocol.

To assess improvements, if any, in accuracy (geometrically) on contours generated on an initial AI model versus models re-trained on clinical trial data every 20-50 patients. Comparisons will be made using the change in surface dice similarity coefficient (sDSC) when the initially generated AI contour is compared with the final edited contour.

To assess improvements, if any, in accuracy (geometrically) on contours generated on an initial AI model versus models re-trained on clinical trial data every 50-100 patients. Comparisons will be made using the change in dice similarity coefficient (DSC) when the initially generated AI contour is compared with the final edited contour.

To compare the accuracy of an in-house AI segmentation tool (LOCATOR) against commercially available tools on geometric accuracy. Comparisons will be made using the difference in surface dice similarity coefficient (sDSC) with the initially generated AI contours when compared with the final manual contour.

To compare the accuracy of an in-house AI segmentation tool (LOCATOR) against commercially available tools on geometric accuracy. Comparisons will be made using the difference in dice similarity coefficient (DSC) with the initially generated AI contours when compared with the final manual contour.

To compare the quality of contours of an in-house AI segmentation tool (LOCATOR) against commercially available tools. This assessment will be done using the MD Anderson Cancer Centre five-point likert scale used to validate autosegmentation models ranging from (Strongly disagree to Strongly Agree). The measure will be the proportion of unacceptable contours (as defined by MD Anderson autocontouring score <= 2) between manual contouring and AI-assisted contouring.

We will perform a brief assessment of patient perception on AI use in their care with a six question survey following their treatment on a five-point likert scale (strongly agree to strongly disagree).

To perform an economic cost-benefit analysis of using an in-house auto-segmentation (LOCATOR) tool compared to manual segmentation and commercial auto-segmentation systems. This will be done using direct dollar (US and Australian) cost comparisons. Direct costs will be calculated for the LOCATOR system including labor, hardware and maintenance costs for 1 and 3 years. The direct dollar cost for a commercial system will be compared against the overall direct cost of the LOCATOR system. The direct cost of retaining a manual system will be calculated based on the direct cost of extra hours of labor required.