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  1. Article ; Online: A model of infection and immune response to low dose radiation.

    Kirkby, Charles

    International journal of radiation biology

    2022  Volume 98, Issue 7, Page(s) 1243–1256

    Abstract: Purpose: Low dose radiation therapy (LDRT) using doses in the range of 30-150 cGy has been proposed as a means of mitigating the pneumonia associated with COVID-19. However, preliminary results from ongoing clinical trials have been mixed. The aim of ... ...

    Abstract Purpose: Low dose radiation therapy (LDRT) using doses in the range of 30-150 cGy has been proposed as a means of mitigating the pneumonia associated with COVID-19. However, preliminary results from ongoing clinical trials have been mixed. The aim of this work is to develop a mathematical model of the viral infection and associated systemic inflammation in a patient based on the time evolution of the viral load. The model further proposes an immunomodulatory response to LDRT based on available data. Inflammation kinetics are then explored and compared to clinical results.
    Methods: The time evolution of a viral infection, inflammatory signaling factors, and inflammatory response are modeled by a set of coupled differential equations. Adjustable parameters are taken from the literature where available and otherwise iteratively adjusted to fit relevant data. Simple functions modeling both the suppression of pro-inflammatory signal factors and the enhancement of anti-inflammatory factors in response to low doses of radiation are developed. The inflammation response is benchmarked against C-reactive protein (CRP) levels measured for cohorts of patients with severe COVID-19.
    Results: The model fit the time-evolution of viral load data, cytokine data, and inflammation (CRP) data. When LDRT was applied early, the model predicted a reduction in peak inflammation consistent with the difference between the non-surviving and surviving cohorts. This reduction of peak inflammation diminished as the application of LDRT was delayed.
    Conclusion: The model tracks the available data on viral load, cytokine levels, and inflammatory biomarkers well. An LDRT effect is large enough in principle to provide a life-saving immunomodulatory effect, though patients treated with LDRT already near the peak of their inflammation trajectory are unlikely to see drastic reductions in that peak. This result potentially explains some discrepancies in the preliminary clinical trial data.
    MeSH term(s) COVID-19/radiotherapy ; Cytokines ; Humans ; Immunity ; Inflammation/radiotherapy ; Radiotherapy Dosage
    Chemical Substances Cytokines
    Language English
    Publishing date 2022-01-07
    Publishing country England
    Document type Journal Article
    ZDB-ID 3065-x
    ISSN 1362-3095 ; 0020-7616 ; 0955-3002
    ISSN (online) 1362-3095
    ISSN 0020-7616 ; 0955-3002
    DOI 10.1080/09553002.2022.2020361
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Stability of dosiomic features against variations in dose calculation: An analysis based on a cohort of prostate external beam radiotherapy patients.

    Sun, Lingyue / Smith, Wendy / Kirkby, Charles

    Journal of applied clinical medical physics

    2023  Volume 24, Issue 5, Page(s) e13904

    Abstract: Introduction: Interest in using higher order features of the planned 3D dose distributions (i.e., dosiomics) to predict radiotherapy outcomes is growing. This is driving many retrospective studies where historical data are mined to train machine ... ...

    Abstract Introduction: Interest in using higher order features of the planned 3D dose distributions (i.e., dosiomics) to predict radiotherapy outcomes is growing. This is driving many retrospective studies where historical data are mined to train machine learning models; however, recent decades have seen considerable advances in dose calculation that could have a direct impact on the dosiomic features such studies seek to extract. Is it necessary to recalculate planned dose distributions using a common algorithm if retrospective datasets from different institutions are included? Does a change in dose calculation grid size part way through a retrospective cohort, introduce bias in the extracted dosiomic features? The purpose of this study is to assess the stability of dosiomic features against variations in three factors: the dose calculation algorithm type, version, and dose grid size.
    Methods: Dose distributions for 27 prostate patients who received EBRT were recalculated in the Eclipse Treatment Planning System (Varian Medical Systems, Palo Alto, California, USA) using two algorithms (AAA and Acuros XB), two versions (version 13.6 and 15.6), and three dose grids (2, 2.5 s, and 3 mm) - 12 dose distributions for each patient. Ninety-three dosiomic features were extracted from each dose distribution and each of the following regions-of-interest: high dose PTV (PTV_High), 1 cm rind around PTV_High (PTV_Ring), low dose PTV (PTV_Low), rectum, and bladder using PyRadiomics. The coefficient of variation (CV) was calculated for each dosiomic feature. Hierarchical clustering was used to group features with high and low variability. Three-way repeated measures ANOVA was performed to investigate the effect of the three different factors on dosiomic features that were classified with high variation. Additionally, CVs were calculated for cumulative dose volume histograms (DVHs) to test their ability to detect the variations in dose distributions.
    Results: For PTV_Ring, PTV_Low, and rectum, all the dosiomic features had low CV (average CV ≤ 0.26) across the varying dose calculation conditions. For PTV_High, six dosiomic features showed CV > 0.26, and dose calculation algorithm type and grid size were the major sources of within-patient variation. For bladder, one dosiomic feature had average CV > 0.26, but none of the three dose calculation-related factors led to a statistically significant variation. The CVs for all the DVHs were very small (CV < 0.05).
    Conclusion: For all the regions-of-interest examined in this study, the majority of the dosiomic features were stable against variations in dose calculation; however, some of the dosiomic features for PTV_High and bladder had significant variations due to differences in dose calculation details. DVHs were detecting less variation than dosiomic features.
    MeSH term(s) Male ; Humans ; Prostate ; Retrospective Studies ; Radiotherapy Dosage ; Radiotherapy, Intensity-Modulated ; Radiotherapy Planning, Computer-Assisted ; Algorithms
    Language English
    Publishing date 2023-01-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2010347-5
    ISSN 1526-9914 ; 1526-9914
    ISSN (online) 1526-9914
    ISSN 1526-9914
    DOI 10.1002/acm2.13904
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Is low dose radiation therapy a potential treatment for COVID-19 pneumonia?

    Kirkby, Charles / Mackenzie, Marc

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2020  Volume 147, Page(s) 221

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Humans ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-04-06
    Publishing country Ireland
    Document type Editorial ; Comment
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2020.04.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Response to: Low dose radiation therapy for COVID-19 pneumonia a double-edged sword.

    Kirkby, Charles / Mackenzie, Marc

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2020  Volume 147, Page(s) 226

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Humans ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-05-07
    Publishing country Ireland
    Document type Letter ; Comment
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2020.04.042
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Response to: RILI model and the Covid-19 pneumonia: The radiation oncologist point of view.

    Kirkby, Charles / Mackenzie, Marc

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2020  Volume 149, Page(s) 72

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Humans ; Pandemics ; Pneumonia, Viral ; Radiation Oncologists ; Radiation Pneumonitis ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-05-07
    Publishing country Ireland
    Document type Letter ; Comment
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2020.04.050
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  6. Article ; Online: Low dose lung radiation therapy for pneumonia: an examination of historical dose distributions.

    Kirkby, Charles / Mackenzie, Marc

    Physics in medicine and biology

    2020  Volume 65, Issue 15, Page(s) 155019

    Abstract: The novel coronavirus, SARS-CoV-2, that causes the COVID-19 disease currently has healthcare systems around the world dealing with unprecedented numbers of critically ill patients. One of the primary concerns associated with this illness is acute ... ...

    Abstract The novel coronavirus, SARS-CoV-2, that causes the COVID-19 disease currently has healthcare systems around the world dealing with unprecedented numbers of critically ill patients. One of the primary concerns associated with this illness is acute respiratory distress syndrome (ARDS) and the pneumonia that accompanies it. Historical literature dating back to the 1940s and earlier contains many reports of successful treatment of pneumonias with ionizing radiation. Although these were not randomized controlled trials, they do suggest a potential avenue for further investigation. Technical details in these reports however were limited. In this work we review the literature and identify details including nominal kilovoltage ranges, filtration, and focus-skin distances (FSDs). Using a freely available and benchmarked code, we generated spectra and used these as sources for Monte Carlo simulations using the EGSnrc software package. The approximate sources were projected through a radiologically anthropomorphic phantom to provide detailed dose distributions within a targeted lung volume (approximate right middle lobe). After accounting for the reported exposure levels, mean lung doses fell in a relatively narrow range: 30-80 cGy. Variation in patient dimensions and other details are expected to result in an uncertainty on the order of ± 20%. This result is consistent with the dose range expected to induce anti-inflammatory effects.
    MeSH term(s) COVID-19 ; Coronavirus Infections/complications ; Humans ; Lung/radiation effects ; Monte Carlo Method ; Pandemics ; Pneumonia/complications ; Pneumonia/radiotherapy ; Pneumonia, Viral/complications ; Radiation Dosage ; Radiotherapy Dosage
    Keywords covid19
    Language English
    Publishing date 2020-07-31
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ab9e55
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  7. Article: Do Dosiomic Features Extracted From Planned 3-Dimensional Dose Distribution Improve Biochemical Failure-Free Survival Prediction: an Analysis Based on a Large Multi-Institutional Data Set.

    Sun, Lingyue / Burke, Ben / Quon, Harvey / Swallow, Alec / Kirkby, Charles / Smith, Wendy

    Advances in radiation oncology

    2023  Volume 8, Issue 5, Page(s) 101227

    Abstract: Purpose: The objective of this work was to investigate whether including additional dosiomic features can improve biochemical failure-free survival prediction compared with models with clinical features only or with clinical features as well as ... ...

    Abstract Purpose: The objective of this work was to investigate whether including additional dosiomic features can improve biochemical failure-free survival prediction compared with models with clinical features only or with clinical features as well as equivalent uniform dose and tumor control probability.
    Methods and materials: This retrospective study included 1852 patients who received diagnoses of localized prostate cancer between 2010 and 2016 and were treated with curative external beam radiation therapy in Albert, Canada. A total of 1562 patients from 2 centers were used for developing 3 random survival forest models: Model A included only 5 clinical features; Model B included 5 clinical features, equivalent uniform dose, and tumor control probability; and Model C considered 5 clinical features and 2074 dosiomic features derived from the planned dose distribution of the clinical target volume and planning target volume with further feature selection to determine prognostic features. No feature selection was performed for models A and B. Two hundred ninety patients from another 2 centers were used for independent validation. Individual model-based risk stratification was examined, and the log-rank tests were performed to test statistically significant differences between the risk groups. The 3 models' performances were evaluated using Harrell's concordance index (C-index) and compared using one-way repeated-measures analysis of variance with post hoc paired
    Results: Model C selected 6 dosiomic features and 4 clinical features to be prognostic. There were statistically significant differences between the 4 risk groups for both training and validation data sets. The C-index for the out-of-bag samples of the training data set was 0.650, 0.648, and 0.669 for models A, B, and C, respectively. The C-index for the validation data set for models A, B, and C was 0.653, 0.648, and 0.662, respectively. Although gains were modest, Model C was statistically significantly better than models A and B.
    Conclusions: Dosiomics contain information beyond common dose-volume histogram metrics from planned dose distributions. Incorporation of prognostic dosiomic features in biochemical failure-free survival outcome models can lead to statistically significant although modest improvement in performance.
    Language English
    Publishing date 2023-03-27
    Publishing country United States
    Document type Journal Article
    ISSN 2452-1094
    ISSN 2452-1094
    DOI 10.1016/j.adro.2023.101227
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  8. Article ; Online: External beam radiation therapy treatment factors prognostic of biochemical failure free survival: A multi-institutional retrospective study for prostate cancer.

    Sun, Lingyue / Quon, Harvey / Tran, Vicki / Kirkby, Charles / Smith, Wendy

    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

    2022  Volume 173, Page(s) 109–118

    Abstract: Background and purpose: The goal of this work is to identify specific treatment planning and delivery features that are prognostic of biochemical failure-free survival (BFFS) for prostate cancer patients treated with external beam radiotherapy (EBRT).!## ...

    Abstract Background and purpose: The goal of this work is to identify specific treatment planning and delivery features that are prognostic of biochemical failure-free survival (BFFS) for prostate cancer patients treated with external beam radiotherapy (EBRT).
    Materials and methods: This study reviewed patients diagnosed with localized prostate adenocarcinoma between 2005 and 2016, and treated with EBRT on a Varian linear accelerator at one of the four cancer centers in Alberta, Canada. BFFS was calculated using the Kaplan-Meier estimator. Patient demographics, tumor characteristics, and EBRT treatment planning and delivery factors, were collected for each patient. The patient cohort was split into a training dataset with patients from two centers and a validation dataset with patients from another two centers. A random survival forest was used to identify features associated with BFFS.
    Results: This study included 2827 patients with a median follow-up of 6.4 years. The BFFS for this cohort collectively was 84.9% at 5 years and 69.3% at 10 years. 2519 patients from two centers were used for model training and 308 patients from two different centers were used for model validation. The prognostic features were Gleason score, prostate-specific antigen (PSA) at diagnosis, clinical T stage, CTV D99, pelvic irradiation, IGRT frequency, and PTV V98. Variables including bladder volume, dose calculation algorithm, PTV D99, age at diagnosis, hip prosthesis, number of malignancies, fiducial marker usage, PTV volume, RT modality, PTV HI, rectal volume, hormone treatment, PTV D1cc, equivalent PTV margin, IGRT type, and EQD2_1.5 were unlikely to be prognostic. A random survival forest using only the seven prognostic variables was built. The Harrell's concordance index for the model was 0.65 for the whole training dataset, 0.62 for out-of-bag samples of the training dataset, and 0.62 for the validation dataset.
    Conclusion: EBRT features prognostic of BFFS were identified and a random survival forest was developed for predicting prostate cancer patients' BFFS after EBRT.
    MeSH term(s) Adenocarcinoma/mortality ; Adenocarcinoma/pathology ; Adenocarcinoma/radiotherapy ; Alberta/epidemiology ; Humans ; Male ; Prognosis ; Prostate-Specific Antigen ; Prostatic Neoplasms/mortality ; Prostatic Neoplasms/pathology ; Prostatic Neoplasms/radiotherapy ; Retrospective Studies
    Chemical Substances Prostate-Specific Antigen (EC 3.4.21.77)
    Language English
    Publishing date 2022-06-02
    Publishing country Ireland
    Document type Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 605646-5
    ISSN 1879-0887 ; 0167-8140
    ISSN (online) 1879-0887
    ISSN 0167-8140
    DOI 10.1016/j.radonc.2022.05.030
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