Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.

Background And Purpose: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.

Materials And Methods: Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method.

Fully automated radiotherapy treatment planning: A scan to plan challenge.

Background And Purpose: Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible.

Materials And Methods: A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up.

Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology.

Purpose: Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas.

An approach to generate synthetic 4DCT datasets to benchmark Mid-Position implementations.

Purpose: The Mid-Position image is constructed from 4DCT data using Deformable Image Registration and can be used as planning CT with reduced PTV volumes. 4DCT datasets currently-available for testing do not provide the corresponding Mid-P images of the datasets. This work describes an approach to generate human-like synthetic 4DCT datasets with the associated Mid-P images that can be used as reference in the validation of Mid-P implementations.

Clinical evaluation of atlas-based auto-segmentation in breast and nodal radiotherapy.

Objectives: Accurate contouring of anatomical structures allows for high-precision radiotherapy planning, targeting the dose at treatment volumes and avoiding organs at risk. Manual contouring is time-consuming with significant user variability, whereas auto-segmentation (AS) has proven efficiency benefits but requires editing before treatment planning. This study investigated whether atlas-based AS (ABAS) accuracy improves with template atlas group size and character-specific atlas and test case selection.

An investigation into the risk of population bias in deep learning autocontouring.

Background And Purpose: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by determining whether the performance of an autocontouring system is impacted by geographic population.

Materials And Methods: 80 Head Neck CT deidentified scans were collected from four clinics in Europe (n = 2) and Asia (n = 2).

Analytic calculations and synthetic shapes for validation of quantitative contour comparison software.

A high level of variability in reported values was observed in a recent survey of contour similarity measures (CSMs) calculation tools. Such variations in the output measurements prevent meaningful comparison between studies. The purpose of this study was to develop a dataset with analytically calculated gold standard values to facilitate standardization and ensure accuracy of CSM implementations.

Exploration of the contribution of physiotherapy students to the delivery of health services: a qualitative study.

Objective: To understand the perspectives of physiotherapists on the contribution of students to the delivery of health services during clinical placements.

Methods: Focus groups with a semi-structured interview guide were completed separately with new graduate physiotherapists reflecting on their student experience and experienced physiotherapists from five Queensland public health-sector hospitals. Interviews were transcribed verbatim in preparation for thematic analysis.

Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy.

Background And Purpose: A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different implementations of the measures to the same data in order to investigate how consistently such measures are defined and implemented in radiation oncology.

Materials And Methods: A survey was conducted to assess if there were any differences in definitions of contouring measures or their implementations that would lead to variation in reported results between institutions.

Real-world analysis of manual editing of deep learning contouring in the thorax region.

Background And Purpose: User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region.

Materials And Methods: A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed.

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm.

The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned.

Beyond automatic medical image segmentation-the spectrum between fully manual and fully automatic delineation.

Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully manual segmentation to reduce time spent contouring and to increase contour quality and consistency. Particularly, fully automatic segmentation has seen exceptional improvements through the use of deep learning in recent years. These fully automatic methods may not require user interactions, but the resulting contours are often not suitable to be used in clinical practice without a review by the clinician.

Segmentation-guided multi-modal registration of liver images for dose estimation in SIRT.

Purpose: Selective internal radiation therapy (SIRT) requires a good liver registration of multi-modality images to obtain precise dose prediction and measurement. This study investigated the feasibility of liver registration of CT and MR images, guided by segmentation of the liver and its landmarks. The influence of the resulting lesion registration on dose estimation was evaluated.

A collaborative clinical placement model for physiotherapy students results in equivalent (or greater) direct patient care activity than that delivered by physiotherapists alone: an observational study.

Background: The demand for physiotherapy clinical placements is rising which requires innovative approaches and an understanding of clinical placement models.

Objective: To determine physiotherapy student contribution to direct patient care activity during a collaborative clinical placement model. Secondary aims determined the impact of clinical area and clinical educator to student (CE:student) ratio and if a group of students could reach equivalent direct patient care activity of a junior or senior physiotherapist.

Interactive contouring through contextual deep learning.

Purpose: To investigate a deep learning approach that enables three-dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two-dimensional (2D) contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist.

Methods: A series of deep learning segmentation models using a Recurrent Residual U-Net with attention gates was trained with a successively expanding training set.

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy.

Background And Purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring.

Materials And Methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019-April 2020).

External validation of deep learning-based contouring of head and neck organs at risk.

Background And Purpose: Head and neck (HN) radiotherapy can benefit from automatic delineation of tumor and surrounding organs because of the complex anatomy and the regular need for adaptation. The aim of this study was to assess the performance of a commercially available deep learning contouring (DLC) model on an external validation set.

Materials And Methods: The CT-based DLC model, trained at the University Medical Center Groningen (UMCG), was applied to an independent set of 58 patients from the Radboud University Medical Center (RUMC).

Calcimimetic Use in Dialysis-Dependent Medicare Fee-for-Service Beneficiaries and Implications for Bundled Payment.

Background: Patients who are dialysis dependent and have secondary hyperparathyroidism (SHPT) may require calcimimetics to reduce parathyroid hormone levels to treatment goals. Medicare currently uses the Transitional Drug Add-on Payment Adjustment (TDAPA) designation under the ESKD Prospective Payment System ("bundled payment") to pay for calcimimetics (the first products eligible for the adjustment); this payment designation for calcimimetics is expected to conclude after 2020. This study explores variability in calcimimetic use across key patient characteristics and its potential effect on policy options for incorporating calcimimetics permanently into the bundle.

Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations.

Purpose: The use of magnetic resonance imaging (MRI) in radiotherapy treatment planning has rapidly increased due to its ability to evaluate patient's anatomy without the use of ionizing radiation and due to its high soft tissue contrast. For these reasons, MRI has become the modality of choice for longitudinal and adaptive treatment studies. Automatic segmentation could offer many benefits for these studies.

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

Purpose: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.

Methods: A multi-scale CNN was modified for liver segmentation for SIRT patients.

The transition from physiotherapy student to new graduate: are they prepared?

: The transition from physiotherapy student to new graduate poses many challenges. In other health disciplines concerns have been raised about new graduate preparedness for practice.: To explore the perspectives of new graduate and experienced physiotherapists on the transition from student to new graduate.

CT images with expert manual contours of thoracic cancer for benchmarking auto-segmentation accuracy.

Purpose: Automatic segmentation offers many benefits for radiotherapy treatment planning; however, the lack of publicly available benchmark datasets limits the clinical use of automatic segmentation. In this work, we present a well-curated computed tomography (CT) dataset of high-quality manually drawn contours from patients with thoracic cancer that can be used to evaluate the accuracy of thoracic normal tissue auto-segmentation systems.

Acquisition And Validation Methods: Computed tomography scans of 60 patients undergoing treatment simulation for thoracic radiotherapy were acquired from three institutions: MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, and the MAASTRO clinic.

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy.

Background And Purpose: In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring.

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Introduction: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS.