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  1. Article ; Online: BPEX - story of an amazing past and very bright future ahead!

    Jeraj, Robert

    Biomedical physics & engineering express

    2023  Volume 9, Issue 2

    Language English
    Publishing date 2023-01-16
    Publishing country England
    Document type Editorial
    ISSN 2057-1976
    ISSN (online) 2057-1976
    DOI 10.1088/2057-1976/acafd9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Computational modelling of modern cancer immunotherapy.

    Valentinuzzi, Damijan / Jeraj, Robert

    Physics in medicine and biology

    2020  Volume 65, Issue 24, Page(s) 24TR01

    Abstract: Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the ... ...

    Abstract Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.
    MeSH term(s) Combined Modality Therapy ; Computer Simulation ; Humans ; Immunotherapy ; Models, Biological ; Neoplasms/immunology ; Neoplasms/therapy
    Language English
    Publishing date 2020-12-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/abc3fc
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    Zs.A 199: Show issues Location:
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  3. Article ; Online: Improved PET quantification and harmonization by adaptive denoising.

    Namías, Mauro / Jeraj, Robert

    Biomedical physics & engineering express

    2020  Volume 6, Issue 1, Page(s) 15023

    Abstract: Purpose: Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal of quantitative harmonization is to achieve comparable measurements between different scanners, thus enabling ... ...

    Abstract Purpose: Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal of quantitative harmonization is to achieve comparable measurements between different scanners, thus enabling multicenter clinical trials. Clinical guidelines, such as those from the European Association of Nuclear Medicine (EANM), recommend harmonizing PET reconstructions to bring contrast recovery coefficients (CRCs) within specifications. However, these harmonized reconstructions can show quantitative biases. In this work we improve harmonization by using a novel adaptive filtering scheme. Our goal was to obtain low quantification bias and high peak signal to noise ratio (PSNR) values at the same time.
    Methods: a novel three-stage adaptive denoising filter was implemented. Filter parameters were optimized to achieve both high PSNR in a digital brain phantom and low quantitative bias of maximum CRC values (CRCmax) obtained from a National Electrical Manufacturers Association (NEMA) PET image quality phantom. The NEMA phantom was scanned on several PET/CT scanners and reconstructed without postfilters. The optimal filter settings found for a training dataset were then applied to testing reconstructions from other scanners. Harmonization limits were defined using the 95% confidence intervals across reconstructions.
    Results: Average CRCmax values close to unity (± 5%) were achieved for spheres with diameter equal or greater than 13 mm for the training dataset. PSNR values were comparable to other state-of-the-art filter results. Using the same optimal filter settings for the testing datasets, similar quantitative results were found. Lesion conspicuity was improved on clinical scans when compared with EANM reconstructions, with no visible artifacts.
    Conclusions: Our three-stage adaptive filter achieved state-of-the-art quantitative performance for PET imaging. Harmonization tolerances with lower bias and variance than EANM guidelines were achieved for a variety of scanner models. CRCmax values were close to unity and the quantification variability was reduced when compared with standard reconstructions.
    MeSH term(s) Humans ; Image Processing, Computer-Assisted/methods ; Phantoms, Imaging ; Positron Emission Tomography Computed Tomography/methods ; Signal-To-Noise Ratio ; Tomography Scanners, X-Ray Computed/statistics & numerical data
    Language English
    Publishing date 2020-01-20
    Publishing country England
    Document type Journal Article
    ISSN 2057-1976
    ISSN (online) 2057-1976
    DOI 10.1088/2057-1976/ab6996
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  4. Article ; Online: An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

    Santoro-Fernandes, Victor / Huff, Daniel T / Rivetti, Luciano / Deatsch, Alison / Schott, Brayden / Perlman, Scott B / Jeraj, Robert

    Physics in medicine and biology

    2024  Volume 69, Issue 8

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Humans ; Positron Emission Tomography Computed Tomography ; Tomography, X-Ray Computed/methods ; Multimodal Imaging/methods ; Positron-Emission Tomography/methods ; Neuroendocrine Tumors/diagnostic imaging ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2024-04-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ad31c6
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  5. Article ; Online: Comparison of automated full-body bone metastases delineation methods and their corresponding prognostic power.

    Schott, Brayden / Weisman, Amy J / Perk, Timothy G / Roth, Alison R / Liu, Glenn / Jeraj, Robert

    Physics in medicine and biology

    2023  Volume 68, Issue 3

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Male ; Humans ; Positron Emission Tomography Computed Tomography/methods ; Prostatic Neoplasms, Castration-Resistant/pathology ; Prognosis ; Bone Neoplasms/diagnostic imaging ; Bone Neoplasms/secondary ; Radionuclide Imaging
    Language English
    Publishing date 2023-01-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/acaf22
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  6. Article ; Online: Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

    Deatsch, Alison / Perovnik, Matej / Namías, Mauro / Trošt, Maja / Jeraj, Robert

    Physics in medicine and biology

    2022  Volume 67, Issue 19

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Alzheimer Disease/diagnostic imaging ; Deep Learning ; Fluorodeoxyglucose F18 ; Humans ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods ; Positron-Emission Tomography/methods
    Chemical Substances Fluorodeoxyglucose F18 (0Z5B2CJX4D)
    Language English
    Publishing date 2022-09-30
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ac8f10
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  7. Article ; Online: Apolipoprotein genetic variants and hereditary amyloidosis.

    Jeraj, Natasha / Hegele, Robert A / Berberich, Amanda J

    Current opinion in lipidology

    2021  Volume 32, Issue 2, Page(s) 132–140

    Abstract: Purpose of review: Amyloidosis is caused by the deposition of misfolded aggregated proteins called amyloid fibrils that in turn cause organ damage and dysfunction. In this review, we aim to summarize the genetic, clinical, and histological findings in ... ...

    Abstract Purpose of review: Amyloidosis is caused by the deposition of misfolded aggregated proteins called amyloid fibrils that in turn cause organ damage and dysfunction. In this review, we aim to summarize the genetic, clinical, and histological findings in apolipoprotein-associated hereditary amyloidosis and the growing list of mutations and apolipoproteins associated with this disorder. We also endeavor to summarize the features of apolipoproteins that have led them to be overrepresented among amyloidogenic proteins. Additionally, we aim to distinguish mutations leading to amyloidosis from those that lead to inherited dyslipidemias.
    Recent findings: Apolipoproteins are becoming increasingly recognized in hereditary forms of amyloidosis. Although mutations in APOA1 and APOA2 have been well established in hereditary amyloidosis, new mutations are still being detected, providing further insight into the pathogenesis of apolipoprotein-related amyloidosis. Furthermore, amyloidogenic mutations in APOC2 and APOC3 have more recently been described. Although no hereditary mutations in APOE or APOA4 have been described to date, both protein products are amyloidogenic and frequently found within amyloid deposits.
    Summary: Understanding the underlying apolipoprotein mutations that contribute to hereditary amyloidosis may help improve understanding of this rare but serious disorder and could open the door for targeted therapies and the potential development of new treatment options.
    MeSH term(s) Amyloidosis/genetics ; Amyloidosis, Familial/genetics ; Apolipoproteins/genetics ; Humans
    Chemical Substances Apolipoproteins
    Language English
    Publishing date 2021-01-03
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1045394-5
    ISSN 1473-6535 ; 0957-9672
    ISSN (online) 1473-6535
    ISSN 0957-9672
    DOI 10.1097/MOL.0000000000000736
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Interpretation and visualization techniques for deep learning models in medical imaging.

    Huff, Daniel T / Weisman, Amy J / Jeraj, Robert

    Physics in medicine and biology

    2021  Volume 66, Issue 4, Page(s) 04TR01

    Abstract: Deep learning (DL) approaches to medical image analysis tasks have recently become popular; however, they suffer from a lack of human interpretability critical for both increasing understanding of the methods' operation and enabling clinical translation. ...

    Abstract Deep learning (DL) approaches to medical image analysis tasks have recently become popular; however, they suffer from a lack of human interpretability critical for both increasing understanding of the methods' operation and enabling clinical translation. This review summarizes currently available methods for performing image model interpretation and critically evaluates published uses of these methods for medical imaging applications. We divide model interpretation in two categories: (1) understanding model structure and function and (2) understanding model output. Understanding model structure and function summarizes ways to inspect the learned features of the model and how those features act on an image. We discuss techniques for reducing the dimensionality of high-dimensional data and cover autoencoders, both of which can also be leveraged for model interpretation. Understanding model output covers attribution-based methods, such as saliency maps and class activation maps, which produce heatmaps describing the importance of different parts of an image to the model prediction. We describe the mathematics behind these methods, give examples of their use in medical imaging, and compare them against one another. We summarize several published toolkits for model interpretation specific to medical imaging applications, cover limitations of current model interpretation methods, provide recommendations for DL practitioners looking to incorporate model interpretation into their task, and offer general discussion on the importance of model interpretation in medical imaging contexts.
    MeSH term(s) Deep Learning ; Diagnostic Imaging ; Humans ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2021-02-02
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/abcd17
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  9. Article ; Online: Treatment of Homozygous Familial Hypercholesterolemia With Evinacumab.

    Jeraj, Natasha / Huang, Shih-Han S / Kennedy, Brooke A / Hegele, Robert A

    CJC open

    2021  Volume 4, Issue 3, Page(s) 347–349

    Abstract: Patients with homozygous familial hypercholesterolemia (HoFH) have extremely elevated levels of low-density lipoprotein cholesterol (LDL-C), with premature atherosclerosis and aortic valve disease. Available drug treatments are inadequate, and even with ... ...

    Abstract Patients with homozygous familial hypercholesterolemia (HoFH) have extremely elevated levels of low-density lipoprotein cholesterol (LDL-C), with premature atherosclerosis and aortic valve disease. Available drug treatments are inadequate, and even with serial apheresis, HoFH patients rarely achieve acceptable LDL-C levels. Evinacumab is a monoclonal antibody against angiopoietin-like protein 3 that lowers LDL-C via a novel receptor-independent mechanism. We describe an Ontario patient with HoFH who for 17 months has been treated with monthly infusions of evinacumab added to pre-existing statin, ezetimibe, and evolocumab therapy. Evinacumab in this HoFH patient was associated with markedly improved LDL-C levels and decreased frequency of apheresis.
    Language English
    Publishing date 2021-11-29
    Publishing country United States
    Document type Case Reports
    ISSN 2589-790X
    ISSN (online) 2589-790X
    DOI 10.1016/j.cjco.2021.11.009
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  10. Article ; Online: Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability.

    Huff, Daniel T / Santoro-Fernandes, Victor / Chen, Song / Chen, Meijie / Kashuk, Carl / Weisman, Amy J / Jeraj, Robert / Perk, Timothy G

    Physics in medicine and biology

    2023  Volume 68, Issue 17

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Positron Emission Tomography Computed Tomography ; Tomography, X-Ray Computed/methods ; Lung Neoplasms ; Lymphoma ; Algorithms
    Language English
    Publishing date 2023-08-28
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/acef8f
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