Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets.

Background And Objectives: Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset.

Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy.

Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.The study retrospectively analysed CT scans of 10 patients from the TROG 08.

Assessment of intrafraction motion and its dosimetric impact on prostate radiotherapy using an in-house developed position monitoring system.

Purpose: To implement an in-house developed position monitoring software, SeedTracker, for conventional fractionation prostate radiotherapy, and study the effect on dosimetric impact and intrafraction motion.

Methods: Thirty definitive prostate radiotherapy patients with implanted fiducial markers were included in the study. All patients were treated with VMAT technique and plans were generated using the Pinnacle planning system using the 6MV beam model for Elekta linear accelerator.

Changes in serial multiparametric MRI and FDG-PET/CT functional imaging during radiation therapy can predict treatment response in patients with head and neck cancer.

Objectives: To test if tumour changes measured using combination of diffusion-weighted imaging (DWI) MRI and FDG-PET/CT performed serially during radiotherapy (RT) in mucosal head and neck carcinoma can predict treatment response.

Methods: Fifty-five patients from two prospective imaging biomarker studies were analysed. FDG-PET/CT was performed at baseline, during RT (week 3), and post RT (3 months).

Mid-treatment 18F-FDG PET imaging changes in parotid gland correlates to radiation-induced xerostomia.

Background: The aim of this study was to measure functional changes in parotid glands using mid-treatment FDG-PET/CT and correlate early imaging changes to subsequent xerostomia in mucosal head and neck squamous cell carcinoma patients undergoing radiotherapy.

Materials And Methods: 56 patients from two prospective imaging biomarker studies underwent FDG-PET/CT at baseline and during radiotherapy (week 3). Both parotid glands were volumetrically delineated at each time point.

Impact of tumour region of interest delineation method for mid-treatment FDG-PET response prediction in head and neck squamous cell carcinoma undergoing radiotherapy.

Background: The aim of this study was to evaluate the impact of tumour region of interest (ROI) delineation method on mid-treatment F-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) response prediction in mucosal head and neck squamous cell carcinoma during radiotherapy.

Methods: A total of 52 patients undergoing definitive radiotherapy with or without systemic therapy from two prospective imaging biomarker studies were analysed. FDG-PET was performed at baseline and during radiotherapy (week 3).

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

Background: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects.

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures.

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

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.

Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer.

Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer.

Development of a vendor neutral MRI distortion quality assurance workflow.

With the utilization of magnetic resonance (MR) imaging in radiotherapy increasing, routine quality assurance (QA) of these systems is necessary. The assessment of geometric distortion in images used for radiotherapy treatment planning needs to be quantified and monitored over time. This work presents an adaptable methodology for performing routine QA for systematic MRI geometric distortion.

Optimal and actual rates of Stereotactic Ablative Body Radiotherapy (SABR) utilisation for primary lung cancer in Australia.

Background And Purpose: Radiotherapy utilisation rates considerably vary across different countries and service providers, highlighting the need to establish reliable benchmarks against which utilisation rates can be assessed. Here, optimal utilisation rates of Stereotactic Ablative Body Radiotherapy (SABR) for lung cancer are estimated and compared against actual utilisation rates to identify potential shortfalls in service provision.

Materials And Methods: An evidence-based optimal utilisation model was constructed after reviewing practice guidelines and identifying indications for lung SABR based on the best available evidence.

Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy.

Purpose: To compare gross tumour volume (GTV) delineation of lung cancer on magnetic resonance imaging (MRI) and positron emission tomography (PET) versus computed tomography (CT) and PET.

Methods: Three experienced thoracic radiation oncologists delineated GTVs on twenty-six patients with lung cancer, based on CT registered to PET, T2-weighted MRI registered to PET and T1-weighted MRI registered with PET. All observers underwent education on reviewing T1 and T2 images along with guidance on window and level setup.

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal post-operative brain scans (T1 MRI, post-Gadolinium T1 MRI, and T2-FLAIR images) of 15 patients who were treated with post-operative radiation therapy, along with manual annotations of their tumour subregions.

Assessing tumor centrality in lung stereotactic ablative body radiotherapy (SABR): the effects of variations in bronchial tree delineation and potential for automated methods.

Accurate delineation of the proximal bronchial tree (PBT) is crucial for appropriate assessment of lung tumor centrality and choice of Stereotactic Ablative Body Radiotherapy (SABR) dose prescription. Here, we investigate variabilities in manual PBT delineation and their potential to influence assessing lesion centrality. A fully automatic, intensity-based tool for PBT contouring and measuring distance to the target is also described.

Data-Driven Modeling of Intracellular Auxin Fluxes Indicates a Dominant Role of the ER in Controlling Nuclear Auxin Uptake.

In plants, the phytohormone auxin acts as a master regulator of developmental processes and environmental responses. The best characterized process in the auxin regulatory network occurs at the subcellular scale, wherein auxin mediates signal transduction into transcriptional programs by triggering the degradation of Aux/IAA transcriptional repressor proteins in the nucleus. However, whether and how auxin movement between the nucleus and the surrounding compartments is regulated remain elusive.