ORCID Profile
0000-0001-9152-1319
Current Organisation
Peter MacCallum Cancer Centre
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Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 02-2021
Publisher: IEEE
Date: 09-2015
Publisher: IOP Publishing
Date: 10-03-2023
Abstract: Objective. To provide an open-source software for repeatable and efficient quantification of T 1 and T 2 relaxation times with the ISMRM/NIST system phantom. Quantitative magnetic resonance imaging (qMRI) biomarkers have the potential to improve disease detection, staging and monitoring of treatment response. Reference objects, such as the system phantom, play a major role in translating qMRI methods into the clinic. The currently available open-source software for ISMRM/NIST system phantom analysis, Phantom Viewer (PV), includes manual steps that are subject to variability. Approach. We developed the Magnetic Resonance BIomarker Assessment Software (MR-BIAS) to automatically extract system phantom relaxation times. The inter-observer variability (IOV) and time efficiency of MR-BIAS and PV was observed in six volunteers analysing three phantom datasets. The IOV was measured with the coefficient of variation (CV) of percent bias (%bias) in T 1 and T 2 with respect to NMR reference values. The accuracy of MR-BIAS was compared to a custom script from a published study of twelve phantom datasets. This included comparison of overall bias and %bias for variable inversion recovery ( T 1 VIR ), variable flip angle ( T 1 VFA ) and multiple spin-echo ( T 2 MSE ) relaxation models. Main results. MR-BIAS had a lower mean CV with T 1 VIR (0.03%) and T 2 MSE (0.05%) in comparison to PV with T 1 VIR (1.28%) and T 2 MSE (4.55%). The mean analysis duration was 9.7 times faster for MR-BIAS (0.8 min) than PV (7.6 min). There was no statistically significant difference in the overall bias, or the %bias for the majority of ROIs, as calculated by MR-BIAS or the custom script for all models. Significance. MR-BIAS has demonstrated repeatable and efficient analysis of the ISMRM/NIST system phantom, with comparable accuracy to previous studies. The software is freely available to the MRI community, providing a framework to automate required analysis tasks, with the flexibility to explore open questions and accelerate biomarker research.
Publisher: Springer Science and Business Media LLC
Date: 03-09-2021
DOI: 10.1038/S41598-021-96600-4
Abstract: Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
Publisher: Frontiers Media SA
Date: 03-03-2022
Abstract: Delivering radiotherapy to patients in an upright position can allow for increased patient comfort, reduction in normal tissue irradiation, or reduction of machine size and complexity. This paper gives an overview of the requirements for the delivery of contemporary arc and modulated radiation therapy to upright patients. We explore i) patient positioning and immobilization, ii) simulation imaging, iii) treatment planning and iv) online setup and image guidance. Treatment chairs have been designed to reproducibly position seated patients for treatment and can be augmented by several existing immobilisation systems or promising emerging technologies such as soft robotics. There are few solutions for acquiring CT images for upright patients, however, cone beam computed tomography (CBCT) scans of upright patients can be produced using the imaging capabilities of standard Linacs combined with an additional patient rotation device. While these images will require corrections to make them appropriate for treatment planning, several methods indicate the viability of this approach. Treatment planning is largely unchanged apart from translating gantry rotation to patient rotation, allowing for a fixed beam with a patient rotating relative to it. Rotation can be provided by a turntable during treatment delivery. Imaging the patient with the same machinery as used in treatment could be advantageous for online plan adaption. While the current focus is using clinical linacs in existing facilities, developments in this area could also extend to lower-cost and mobile linacs and heavy ion therapy.
Publisher: American Society of Clinical Oncology (ASCO)
Date: 20-05-2020
DOI: 10.1200/JCO.2020.38.15_SUPPL.3557
Abstract: 3557 Background: This is a first in human in-vivo biodistribution of ex-vivo labelled CAR T cells assessed in a cohort of patients. Cells were labelled with novel Cu-64 labelled superparamagnetic iron oxide nanoparticles (SPION) and infused IV into patients with solid tumors & tracked using clinical dual PET-MR. The study validates the clinical translation of CAR T cell in-vivo tracking in real time. Methods: Cu-64 radioisotope was bound to silica coated SPION using electrolysis plating with tin & palladium seeding. Cellular uptake of Cu-64 SPION was facilitated with a transfecting agent. Functional assays including 51 Chromium release, cytometric bead array demonstrated that labelling process did not affect cytotoxicity & cytokine secretion (TNFα & IFN-g). T cells were transduced with retroviral vector constructs encoding for second-generation chimeric T-cell receptor specific for carbohydrate Lewis Y antigen. Modified T-cells were expanded ex-vivo & were labelled with Cu-64 (~300 MBq) prior to re-infusion (3 x10 8 labelled cells). Scanning is performed with Siemens 3T dual PET-MR scanner. Results: In this first in human in-vivo study (HREC/16/PMCC/30) a cohort of patients received ex-vivo labelled CAR T cells to determine how many labelled cells distribute to solid tumor sites within 3-5 days. Our results demonstrate that cells can be efficiently labelled (≤60%) with high cell viability (≥85%) at a sensitivity sufficient to detect labelled cells at tumor site for up to 5 days. An observed trend in SUV mean & SUV max provided insight into efficacy & in idual response to therapy. Early time points showed moderate uptake of labelled cells in lungs posterior basal segments without increased activity over next few days, suggesting a transient process. Mild, diffuse bone marrow & relatively intense uptake of labelled cells in liver & spleen suggests margination of cells to reticulo-endothelial system. Distinct PET signal at some of the tumor sites at 24 h suggests antigen specific localization & time taken to reach these sites. Excretion via hepatobiliary indicated reabsorption from GI tract & re-circulation of labelled cells. Minimal uptake in brain & heart supported safety profile of labeling agent. Conclusions: This is first in human in-vivo study to provide highly valuable visual and dynamic data in real time and provides insight into in idual responses to therapy. CAR T cell functionality largely remain unchanged due to labeling process. The findings indicate that labelled cells traffic to tumor sites at later time points & remain persistent for extended period of time.
Publisher: Springer Science and Business Media LLC
Date: 27-07-2022
DOI: 10.1038/S41598-022-16520-9
Abstract: Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.JMR.2019.106595
Abstract: A new framework for B
Publisher: IOP Publishing
Date: 10-2020
DOI: 10.1088/1742-6596/1662/1/012018
Abstract: Stereotactic ablative body radiotherapy (SABR) is demonstrating good local control for patients with inoperable primary renal cell carcinoma. In a previous pilot study we identified magnetic resonance imaging (MRI) early response biomarkers that correlate with later morphological changes in computed tomography (CT) images. These early functional changes in diffusion and perfusion following radiotherapy were observed on MRI and have the potential to identify non-responders who may benefit from adjuvant or salvage therapies. Here we detail the imaging protocol for an MRI sub-study of the Focal Ablative STereotactic Radiosurgery for Cancers of the Kidney (FASTRACK II) trial. A preliminary patient case demonstrates the high quality of the imaging data, with discussion of the improvements made from the pilot protocol for improved motion management and correction. We aim to validate the previously identified early response MRI biomarkers with this rich prospective multi-centre dataset.
Publisher: Springer Science and Business Media LLC
Date: 10-09-2021
DOI: 10.1007/S13246-021-01056-5
Abstract: Volumetric medical imaging lacks a standardised coordinate geometry which links image frame-of-reference to specific anatomical regions. This results in an inability to locate anatomy in medical images without visual assessment and precludes a variety of image analysis tasks which could benefit from a standardised, machine-readable coordinate system. In this work, a proposed geometric system that scales based on patient size is described and applied to a variety of cases in computed tomography imaging. Subsequently, a convolutional neural network is trained to associate axial slice CT image appearance with the standardised coordinate value along the patient superior-inferior axis. The trained neural network showed an accuracy of ± 12 mm in the ability to predict per-slice reference location and was relatively stable across all annotated regions ranging from brain to thighs. A version of the trained model along with scripts to perform network training in other applications are made available. Finally, a selection of potential use applications are illustrated including organ localisation, image registration initialisation, and scan length determination for auditing diagnostic reference levels.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Frontiers Media SA
Date: 25-02-2020
Publisher: Elsevier BV
Date: 03-2017
Publisher: Wiley
Date: 11-04-2020
DOI: 10.1002/ACM2.12873
Publisher: American Association for Cancer Research (AACR)
Date: 15-08-2020
DOI: 10.1158/1538-7445.AM2020-LB-023
Abstract: Objective: The aim is to demonstrate dynamic in-vivo tracking of CAR T cell therapy for treatment of solid tumors using Cu-64 labeled superparamagnetic iron oxide nanoparticles (SPION) as novel dual PET-MR imaging agent. Methodology: Cu-64 SPION: Cu-64 radioisotope is bound to silica coated SPION using enhanced electrolysis plating techniques with tin and palladium seeding. Preclinical Model: Mouse splenic T cells were activated with anti-CD3, anti-CD28 & cultured with IL-2 and IL-7, prior to being transduced with second generation anti-Her-2 CAR (scFv-CD28-CD3ζ). 5 x 105 E0771-hHER2 breast tumor cells were implanted subcutaneously into male C57Bl/6-human HER2 transgenic mice. 107 labeled CAR T or control T cells (Cu-64 5-8 MBq) were injected into tail vein. Clinical Model: Activated T cells using antibody CD3 (OKT3) & IL-2 are transduced with retroviral vector constructs encoding for chimeric T-cell receptor specific for Lewis Y antigen. Modified T-cells are further expanded ex-vivo and reinfused. 3 x 108 CAR T cells were labeled with Cu-64 (200 - 300 MBq). Labeling of CAR T cells with Cu-64 SPION: Transfecting agent protamine sulphate facilitated cellular uptake of Cu-64 SPION within cells. Functional assays: 51Chromium release, cytometric bead array and cell viability showed that labeling process did not affect CAR T cell cytotoxicity, cytokine secretion (TNFα and IFN-γ) and viability. CAR T Cell Tracking: Scanning was performed using clinical grade dual PET-MR scanner. Preliminary Data: In this clinical trial (HREC/16/PMCC/30) patients are being enrolled for first in human in vivo study to determine how many cells distribute to solid tumor sites within first few days of CAR T cell therapy. This is first data that demonstrates that CAR-T cells can be consistently and efficiently labeled (≤60%) with cell viability (≥85%) and at sensitivity comparable to detecting at least z cells at tumor site using clinical grade dual PET-MR scanner. SUVmean values provides insight into in idual response to therapy. The observed increase in SUVmax over time specifies localization of CAR T cells at tumor sites. Clinical data at early time point showed moderate uptake in lungs posterior basal segments without increased activity over next few days, thus suggesting transient process. Mild, diffuse bone marrow and relatively intense uptake in the liver and spleen suggests margination of cells to the reticulo-endothelial system. Distinct PET signal suggests localization of labeled cells in the secondary tumor sites. Little background uptake in important organs such as brain and heart indicate the safety profile of imaging agent. Absence of signal in bladder indicates hepatobiliary excretion, which may allow re-absorption from GI tract and re-circulation. Distinct PET signal within tumor in preclinical studies confirms trafficking of CAR T cells to tumor site as compared to controls. A negative contrast in the liver on T2 weighted MRI in both the preclinical and clinical studies. Preliminary Conclusion:This is first in human in vivo study to show CAR T cell distribution in real time and provides insight into in idual responses of tumors to therapy. CAR T cell functionality largely remain unchanged due to labeling process. The preliminary findings indicate that labeled cells traffic to tumor sites in first few hours of infusion and remain persistent for extended period. Citation Format: Ritu Singla, Dominic Wall, Samuel Anderson, Nicholas Zia, James C. Korte, Lucy Kravets, Gerard McKiernan, Jeanne Butler, Amanda Gammilonghi, Jyoti Arora, Ben Solomon, Rodney Hicks, Timothy Cain, Phillip Darcy, Carleen Cullinane, Paul Neeson, Rajesh Ramanathan, Ravi Shukla, Vipul Bansal, Simon Harrison. Dynamic real time in vivo CAR T cell tracking: Clinical and preclinical studies using a novel dual PET-MR imaging agent [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR Cancer Res 2020 (16 Suppl):Abstract nr LB-023.
Publisher: Wiley
Date: 11-2021
DOI: 10.1002/MP.15290
Abstract: To investigate multiple deep learning methods for automated segmentation (auto‐segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time‐consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto‐segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk. Three convolutional neural network (CNN)‐based auto‐segmentation architectures were developed using manual segmentations and T2‐weighted MRI images provided from the American Association of Physicists in Medicine (AAPM) radiotherapy MRI auto‐contouring (RT‐MAC) challenge dataset ( n = 31). Auto‐segmentation performance was evaluated with segmentation similarity and surface distance metrics on the RT‐MAC dataset with institutional manual segmentations ( n = 10). The generalizability of the auto‐segmentation methods was assessed on an institutional MRI dataset ( n = 10). Auto‐segmentation performance on the RT‐MAC images with institutional segmentations was higher than previously reported MRI methods for the parotid glands (Dice: 0.860 ± 0.067, mean surface distance [MSD]: 1.33 ± 0.40 mm) and the first report of MRI performance for submandibular glands (Dice: 0.830 ± 0.032, MSD: 1.16 ± 0.47 mm). We demonstrate that high‐resolution auto‐segmentations with improved geometric accuracy can be generated for the parotid and submandibular glands by cascading a localizer CNN and a cropped high‐resolution CNN. Improved MSDs were observed between automatic and manual segmentations of the submandibular glands when a low‐resolution auto‐segmentation was used as prior knowledge in the second‐stage CNN. Reduced auto‐segmentation performance was observed on our institutional MRI dataset when trained on external RT‐MAC images only the parotid gland auto‐segmentations were considered clinically feasible for manual correction (Dice: 0.775 ± 0.105, MSD: 1.20 ± 0.60 mm). This work demonstrates that CNNs are a suitable method to auto‐segment the parotid and submandibular glands on MRI images of patients with HNC, and that cascaded CNNs can generate high‐resolution segmentations with improved geometric accuracy. Deep learning methods may be suitable for auto‐segmentation of the parotid glands on T2‐weighted MRI images from different scanners, but further work is required to improve the performance and generalizability of these methods for auto‐segmentation of the submandibular glands and lymph nodes.
Publisher: IOP Publishing
Date: 27-01-2023
Abstract: Objective. Functional lung avoidance (FLA) radiotherapy treatment aims to spare lung regions identified as functional from imaging. Perfusion contributes to lung function and can be measured from the determination of pulmonary blood volume (PBV). An advantageous alternative to the current determination of PBV from positron emission tomography (PET) may be from dual energy CT (DECT), due to shorter examination time and widespread availability. This study aims to determine the correlation between PBV determined from DECT and PET in the context of FLA radiotherapy. Approach. DECT and PET acquisitions at baseline of patients enrolled in the HI-FIVE clinical trial (ID: NCT03569072) were reviewed. Determination of PBV from PET imaging ( PBV PET ), from DECT imaging generated from a commercial software (Syngo.via, Siemens Healthineers, Forchheim, Germany) with its lowest ( PBV syngo R = 1 ) and highest ( PBV syngo R = 10 ) smoothing level parameter value ( R ), and from a two-material decomposition (TMD) method ( PBV TMD L ) with variable median filter kernel size ( L ) were compared. Deformable image registration between DECT images and the CT component of the PET/CT was applied to PBV maps before res ling to the PET resolution. The Spearman correlation coefficient ( r s ) between PBV determinations was calculated voxel-wise in lung subvolumes. Main results. Of this cohort of 19 patients, 17 had a DECT acquisition at baseline. PBV maps determined from the commercial software and the TMD method were very strongly correlated [ r s ( PBV syngo R = 1 , PBV TMD L = 1 ) = 0.94 ± 0.01 and r s ( PBV syngo R = 10 , PBV TMD L = 9 ) = 0.94 ± 0.02]. PBV PET was strongly correlated with PBV TMD L [ r s ( PBV PET , PBV TMD L = 28 ) = 0.67 ± 0.11]. Perfusion patterns differed along the posterior-anterior direction [ r s ( PBV PET , PBV TMD L = 28 ) = 0.77 ± 0.13/0.57 ± 0.16 in the anterior osterior region]. Significance . A strong correlation between DECT and PET determination of PBV was observed. Streak and smoothing effects in DECT and gravitational artefacts and misregistration in PET reduced the correlation posteriorly.
No related grants have been discovered for James Korte.