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The use of breath-hold techniques in radiotherapy, such as deep-inspiration breath hold, is increasing although guidelines for clinical implementation are lacking. In these recommendations, we aim to provide an overview of available technical solutions and guidance for best practice in the implementation phase. We will discuss specific challenges in different tumour sites including factors such as staff training and patient coaching, accuracy, and reproducibility. In addition, we aim to highlight the need for further research in specific patient groups. This report also reviews considerations for equipment, staff training and patient coaching, as well as image guidance for breath-hold treatments. Dedicated sections for specific indications, namely breast cancer, thoracic and abdominal tumours are also included.

The use of breath-hold techniques in radiotherapy, such as deep-inspiration breath hold, is increasing although guidelines for clinical implementation are lacking. In these recommendations, we aim to provide an overview of available technical solutions and guidance for best practice in the implementation phase. We will discuss specific challenges in different tumour sites including factors such as staff training and patient coaching, accuracy, and reproducibility. In addition, we aim to highlight the need for further research in specific patient groups. This report also reviews considerations for equipment, staff training and patient coaching, as well as image guidance for breath-hold treatments. Dedicated sections for specific indications, namely breast cancer, thoracic and abdominal tumours are also included.

Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. Probe placement and ultrasound image inter-pretation are manual tasks contingent upon operator skill, leading to interoperator uncertainties that degrade radiotherapy precision. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is combined with an image clas-sifier using a recurrent neural network to generate two sets of predictions in real-time. The first set identifies relevant prostate anatomy visible in the field of view using the classes: outside prostate, prostate periphery, prostate centre. The second set recommends a probe angular adjustment to achieve alignment between the probe and prostate centre with the classes: move left, move right, stop. The algo-rithm was trained and tested on 9,743 clinical images from 61 treatment sessions across 32 patients. We evaluated classification accuracy against class labels de-rived from three experienced observers at 2/3 and 3/3 agreement thresholds. For images with unanimous consensus between observers, anatomical classification accuracy was 97.2% and probe adjustment accuracy was 94.9%. The algorithm identified optimal probe alignment within a mean (standard deviation) range of 3.7$^{\circ}$ (1.2$^{\circ}$) from angle labels with full observer consensus, comparable to the 2.8$^{\circ}$ (2.6$^{\circ}$) mean interobserver range. We propose such an algorithm could assist ra-diotherapy practitioners with limited experience of ultrasound image interpreta-tion by providing effective real-time feedback during patient set-up., Comment: Accepted to MICCAI 2020