[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program.

Background And Purpose: The [18]F-fluoroethyl-l-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TV) and hybrid target volumes (TV) incorporating pre-defined FET PET biological tumour volumes (BTVs).

Materials And Methods: Central review and analysis of TV and TV was undertaken across three benchmarking cases.

Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy.

. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit (HU) accuracy, and image quality. To achieve these goals, a vision-transformer and anatomically sensitive loss functions are described.

Patient Independent Interictal Epileptiform Discharge Detection.

Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discharges (IEDs) become effective markers for the diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable across individuals, yet trained experts are still able to identify them through EEG recordings - meaning that commonalities exist across IEDs that an algorithm can be trained on to detect and generalise to the larger population.

Quantitative [Ga]Ga-PSMA-11 PET biomarkers for the analysis of lesion-level progression in biochemically recurrent prostate cancer: a multicentre study.

[Ga]Ga-PSMA-11 PET has become the standard imaging modality for biochemically recurrent (BCR) prostate cancer (PCa). However, its prognostic value in assessing response at this stage remains uncertain. The study aimed to assess the prognostic significance of radiographic patient-level patterns of progression derived from lesion-level biomarker quantitation in metastatic disease sites.

Prospective inter- and intra-tracer repeatability analysis of radiomics features in [Ga]Ga-PSMA-11 and [F]F-PSMA-1007 PET scans in metastatic prostate cancer.

Objective: This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [F]F-PSMA-1007 positron emission tomography (PET) scans.

Methods: Eighteen patients with metastatic prostate cancer (mPCa) were prospectively recruited for the study and randomised to one of three test-retest groups: (i) intratracer [Ga]Ga-PSMA-11 PET, (ii) intratracer [F]F-PSMA-1007 PET or (iii) intertracer between [Ga]Ga-PSMA-11 and [F]F-PSMA-1007 PET. Four conventional PET metrics (standardised uptake value (SUV), SUV, SUV and volume) and 107 radiomics features were extracted from 75 lesions and assessed using the repeatability coefficient (RC) and the ICC.

Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study-TROG 18.06.

Purpose: The O-(2-[F]-fluoroethyl)-L-tyrosine (FET) PET in Glioblastoma (FIG) trial is an Australian prospective, multi-centre study evaluating FET PET for glioblastoma patient management. FET PET imaging timepoints are pre-chemoradiotherapy (FET1), 1-month post-chemoradiotherapy (FET2), and at suspected progression (FET3). Before participant recruitment, site nuclear medicine physicians (NMPs) underwent credentialing of FET PET delineation and image interpretation.

Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [Ga]Ga-PSMA-11 PET images.

Purpose: This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model.

Methods: One hundred ninety-nine patients were imaged with [Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.

Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM.

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders.

Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [Ga]Ga-PSMA-11 PET/CT images.

Purpose: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers.

Methods: Three hundred thirty-seven [Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation.

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies.

A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections.

Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to demonstrate how deep learning based on phantom data can be used effectively for CBCT intensity correction in patient images. Four anthropomorphic phantoms were scanned on a CBCT and conventional fan-beam CT system.

Comprehensive investigation into the stability of Varian and Elekta kV imaging systems during arc delivery.

In radiotherapy treatments utilizing accelerator gantry rotation, gantry-mounted kilovoltage (kV) imaging systems have become integral to treatment verification. The accuracy of such verification depends on the stability of the imaging components during gantry rotation. In this study, a simple measurement method and accurate algorithm are introduced for investigation of the kV panel and source movement during gantry rotation.