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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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Ranjith CP, Puzhakkal N, Arunkrishnan MP, Vysakh R, Irfad MP, Vijayagopal KS, Jayashanker S. Mean parotid dose prediction model using machine learning regression method for intensity-modulated radiotherapy in head and neck cancer. Med Dosim 2021; 46:283-288. [PMID: 33744079 DOI: 10.1016/j.meddos.2021.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/21/2020] [Accepted: 02/11/2021] [Indexed: 10/21/2022]
Abstract
Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-learning linear regression was developed and validated in this study. The model was developed using independent variables, such as parotid to PTV overlapping volume, dose coverage of the overlapping PTV, the ratio of overlapping parotid volume to total parotid volume, and volume of parotid overlapping with isotopically expanded PTV contours. The Pearson correlation coefficients between these independent variables and the mean parotid dose were calculated. Multicollinearity of the independent variables was checked by calculating the Variance Inflation Factor (VIF). All variables are having VIF less than ten were taken for the model. Fifty IMRT patient plans were used to develop the model. The mean parotid dose predicted by the model was in good agreement with the obtained mean parotid dose. The model is having a Root Mean Square Error (RMSE) of 2.89 Gy and an R-square of 0.7695. The model was successfully validated using the fivefold cross-validation method, resulting R-square value of 0.6179 and an RMSE of 2.93 Gy. The normality of the model's residuals was tested using Quartile-Quartile (Q-Q) plot and Shapiro Wilk test (p = 0.996, for null hypothesis ``residuals were normally distributed''). The data points in the Q-Q plot are falling approximately along the reference line. This model can be used in clinics to help the planner in the preplanning phase for efficient plan optimization.
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Affiliation(s)
- C P Ranjith
- MVR Cancer Centre and Research Institute, Calicut, India.
| | | | | | - R Vysakh
- MVR Cancer Centre and Research Institute, Calicut, India
| | - M P Irfad
- MVR Cancer Centre and Research Institute, Calicut, India
| | | | - S Jayashanker
- MVR Cancer Centre and Research Institute, Calicut, India
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3
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Mathematical model for the thermal enhancement of radiation response: thermodynamic approach. Sci Rep 2021; 11:5503. [PMID: 33750833 PMCID: PMC7970926 DOI: 10.1038/s41598-021-84620-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/15/2021] [Indexed: 02/08/2023] Open
Abstract
Radiotherapy can effectively kill malignant cells, but the doses required to cure cancer patients may inflict severe collateral damage to adjacent healthy tissues. Recent technological advances in the clinical application has revitalized hyperthermia treatment (HT) as an option to improve radiotherapy (RT) outcomes. Understanding the synergistic effect of simultaneous thermoradiotherapy via mathematical modelling is essential for treatment planning. We here propose a theoretical model in which the thermal enhancement ratio (TER) relates to the cell fraction being radiosensitised by the infliction of sublethal damage through HT. Further damage finally kills the cell or abrogates its proliferative capacity in a non-reversible process. We suggest the TER to be proportional to the energy invested in the sensitisation, which is modelled as a simple rate process. Assuming protein denaturation as the main driver of HT-induced sublethal damage and considering the temperature dependence of the heat capacity of cellular proteins, the sensitisation rates were found to depend exponentially on temperature; in agreement with previous empirical observations. Our findings point towards an improved definition of thermal dose in concordance with the thermodynamics of protein denaturation. Our predictions well reproduce experimental in vitro and in vivo data, explaining the thermal modulation of cellular radioresponse for simultaneous thermoradiotherapy.
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Ciecior W, Ebert N, Borgeaud N, Thames HD, Baumann M, Krause M, Löck S. Sample-size calculation for preclinical dose-response experiments using heterogeneous tumour models. Radiother Oncol 2021; 158:262-267. [PMID: 33667590 DOI: 10.1016/j.radonc.2021.02.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/22/2021] [Accepted: 02/22/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND In preclinical radio-oncological research, local tumour control is considered the most relevant endpoint as it reflects the inactivation of cancer stem cells. Preclinical tumour-control assays may compare dose-response curves between different radiotherapy strategies, e.g., assessing additional targeted drugs and immunotherapeutic interventions, or between different radiation modalities. To mimic the biological heterogeneity of human tumour populations and to accommodate for approaches of personalized oncology, preclinical studies are increasingly performed combining larger panels of tumour models. For designing the study protocols and to obtain reliable results, prospective sample-size planning has to be developed that accounts for such heterogeneous cohorts. METHODS A Monte-Carlo-based method was developed to estimate the sample size of a comparative 1:1 two-arm prospective tumour-control assay. Based on repeated logistic regression analysis, pre-defined dose levels, assumptions on the dose-response curves of the included tumour models and on the dose-modifying factors (DMF), the power is calculated for a given number of animals per dose group. RESULTS Two applications are presented: (i) For a simple tumour-control assay with the head and neck squamous cell carcinoma (HNSCC) model FaDu, 10 animals would be required for each of 7 dose levels per arm to reveal a DMF of 1.25 with a power of 0.82 without drop out (total: 140 animals). (ii) In a more complex experiment combining six different lung tumour models to a heterogeneous population, 21 animals would be required for each of 11 dose levels per arm to reveal a DMF of 1.25 with a power of 0.81 without drop out (total: 462 animals). Analyzing the heterogeneous cohort reduces the required animal number by more than 50% compared to six individual tumour-control assays. CONCLUSION An approach for estimating the required animal number for comparative tumour-control assays in a heterogeneous population is presented, allowing also the inclusion of different treatments as a personalized approach in the experimental arm. The software is publicly available and can be applied to plan comparisons of sigmoidal dose-response curves based on logistic regression.
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Affiliation(s)
- Willy Ciecior
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Nadja Ebert
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nathalie Borgeaud
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany
| | - Howard D Thames
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, United States
| | - Michael Baumann
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Ruprecht-Karls-University, Heidelberg, Germany
| | - Mechthild Krause
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, Germany
| | - Steffen Löck
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), partner site Dresden, Germany.
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Kashef A, Khatibi T, Mehrvar A. Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital. Asian Pac J Cancer Prev 2020; 21:3211-3219. [PMID: 33247677 PMCID: PMC8033115 DOI: 10.31557/apjcp.2020.21.11.3211] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Indexed: 02/07/2023] Open
Abstract
Background: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of this disease has witnessed dramatic rise until this date which is about 90 percent. There are many investigations tending to indicate the efficiency of cranial radiotherapy found out that without that, outcome of the patients did not change and even it improved at some cases. Methods: the main aim of this study is predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients using machine learning. Scope of this paper is intertwined with predicting the necessity of one of the treatment modalities that has been used for many years for this group of patients named Cranial Radiotherapy (CRT). For this purpose, a case study is considered at Mahak charity hospital. In this paper, our focus is on ALL patients aged 0 to 17 treated at Mahak hospital, one of the best centers for treatment of childhood malignancies in Iran. Dataset analyzed in this study is gathered by the research team from patient’s paper-based files. Our dataset consists of 241 observations on patients with 31 attributes after the data cleaning process. Our designed machine learning model for predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients is a stacked ensemble classifier of independently strong models with a meta-learner to tune the weights and parameters of the base classifiers. Results: The stacked ensemble classifier show highly reasonable performance with AUC of 87.52%. Moreover, the attributes are ranked based on their predictive power and the most important variable for CRT necessity prediction is the disease relapse. Conclusion: In order to conclude, derived from previous studies regarding CRT it is not only cost-effective but also more healthy to eradicate the use of CRT for the treatment of childhood ALL. Furthermore, it is valuable to increase the clinical databases by creating more synthetic health databases not only for research purposes but also for physicians to keep track of their patient’s status.
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Affiliation(s)
- Amirarash Kashef
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran
| | - Toktam Khatibi
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran
| | - Azim Mehrvar
- Mahak Hematology Oncology Research Center (Mahak-HORC), Mahak Hospital, Tehran, Iran.,AJA Cancer Epidemiology Research and Treatment Center (AJA-CERTC), AJA University of Medical Sciences, Tehran, Iran
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Sachpazidis I, Mavroidis P, Zamboglou C, Klein CM, Grosu AL, Baltas D. Prostate cancer tumour control probability modelling for external beam radiotherapy based on multi-parametric MRI-GTV definition. Radiat Oncol 2020; 15:242. [PMID: 33081804 PMCID: PMC7574270 DOI: 10.1186/s13014-020-01683-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022] Open
Abstract
Purpose To evaluate the applicability and estimate the radiobiological parameters of linear-quadratic Poisson tumour control probability (TCP) model for primary prostate cancer patients for two relevant target structures (prostate gland and GTV). The TCP describes the dose–response of prostate after definitive radiotherapy (RT). Also, to analyse and identify possible significant correlations between clinical and treatment factors such as planned dose to prostate gland, dose to GTV, volume of prostate and mpMRI-GTV based on multivariate logistic regression model.
Methods The study included 129 intermediate and high-risk prostate cancer patients (cN0 and cM0), who were treated with image-guided intensity modulated radiotherapy (IMRT) ± androgen deprivation therapy with a median follow-up period of 81.4 months (range 42.0–149.0) months. Tumour control was defined as biochemical relapse free survival according to the Phoenix definition (BRFS). MpMRI-GTV was delineated retrospectively based on a pre-treatment multi-parametric MR imaging (mpMRI), which was co-registered to the planning CT. The clinical treatment planning procedure was based on prostate gland, delineated on CT imaging modality. Furthermore, we also fitted the clinical data to TCP model for the two considered targets for the 5-year follow-up after radiation treatment, where our cohort was composed of a total number of 108 patients, of which 19 were biochemical relapse (BR) patients. Results For the median follow-up period of 81.4 months (range 42.0–149.0) months, our results indicated an appropriate α/β = 1.3 Gy for prostate gland and α/β = 2.9 Gy for mpMRI-GTV. Only for prostate gland, EQD2 and gEUD2Gy were significantly lower in the biochemical relapse (BR) group compared to the biochemical control (BC) group. Fitting results to the linear-quadratic Poisson TCP model for prostate gland and α/β = 1.3 Gy were D50 = 66.8 Gy with 95% CI [64.6 Gy, 69.0 Gy], and γ = 3.8 with 95% CI [2.6, 5.2]. For mpMRI-GTV and α/β = 2.9 Gy, D50 was 68.1 Gy with 95% CI [66.1 Gy, 70.0 Gy], and γ = 4.5 with 95% CI [3.0, 6.1]. Finally, for the 5-year follow-up after the radiation treatment, our results for the prostate gland were: D50 = 64.6 Gy [61.6 Gy, 67.4 Gy], γ = 3.1 [2.0, 4.4], α/β = 2.2 Gy (95% CI was undefined). For the mpMRI-GTV, the optimizer was unable to deliver any reasonable results for the expected clinical D50 and α/β. The results for the mpMRI-GTV were D50 = 50.1 Gy [44.6 Gy, 56.0 Gy], γ = 0.8 [0.5, 1.2], α/β = 0.0 Gy (95% CI was undefined). For a follow-up time of 5 years and a fixed α/β = 1.6 Gy, the TCP fitting results for prostate gland were D50 = 63.9 Gy [60.8 Gy, 67.0 Gy], γ = 2.9 [1.9, 4.1], and for mpMRI-GTV D50 = 56.3 Gy [51.6 Gy, 61.1 Gy], γ = 1.3 [0.8, 1.9]. Conclusion The linear-quadratic Poisson TCP model was better fit when the prostate gland was considered as responsible target than with mpMRI-GTV. This is compatible with the results of the comparison of the dose distributions among BR and BC groups and with the results achieved with the multivariate logistic model regarding gEUD2Gy. Probably limitations of mpMRI in defining the GTV explain these results. Another explanation could be the relatively homogeneous dose prescription and the relatively low number of recurrences. The failure to identify any benefit for considering mpMRI-GTV as the target responsible for the clinical response is confirmed when considering a fixed α/β = 1.6 Gy, a fixed follow-up time for biochemical response at 5 years or Gleason score differentiation.
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Affiliation(s)
- Ilias Sachpazidis
- Department of Radiation Oncology, Division of Medical Physics, Medical Centre, Faculty of Medicine, University of Freiburg, Robert-Koch-Str. 3, 79106, Freiburg, Germany. .,German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Centre (DKFZ), Heidelberg, Germany.
| | - Panayiotis Mavroidis
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Centre, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Christina Marie Klein
- Department of Radiation Oncology, Medical Centre, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Anca-Ligia Grosu
- Department of Radiation Oncology, Medical Centre, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, Medical Centre, Faculty of Medicine, University of Freiburg, Robert-Koch-Str. 3, 79106, Freiburg, Germany.,German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research Centre (DKFZ), Heidelberg, Germany
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Belciug S. Radiotherapist at work. Artif Intell Cancer 2020. [DOI: 10.1016/b978-0-12-820201-2.00006-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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9
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Cui S, Luo Y, Hsin Tseng H, Ten Haken RK, El Naqa I. Artificial Neural Network with Composite Architectures for Prediction of Local Control in Radiotherapy. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 3:242-249. [PMID: 30854501 DOI: 10.1109/trpms.2018.2884134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this study, we investigated the application of artificial neural networks (ANNs) with composite architectures into the prediction of local control (LC) of lung cancer patients after radiotherapy. The motivation of this study was to take advantage of the temporal associations among longitudinal (sequential) data to improve the predictive performance of outcome models under the circumstance of limited sample sizes. Two composite architectures: (1) a one dimension (1D) convolutional + fully connected and (2) a locally-connected+ fully connected architectures were implemented for this purpose. Compared with the fully-connected architecture (multi-layer perceptron [MLP]), our composite architectures yielded better predictive performance of LC in lung cancer patients who received radiotherapy. Specifically, in a cohort of 98 patients (29 patients failed locally), the composite architecture of 1D convolutional layers and fully-connected layers achieved an AUC (area under receiver operating characteristic curve) of 0.83 (95% confidence interval (CI): 0.807~0.841) with 18 features (14 features are longitudinal data). Whereas, the composite architecture of locally- connected layers and fully-connected layers achieved an AUC of 0.80 (95%CI: 0.775~0.811). Both outperformed an MLP in the prediction performance with the same set of features, which achieved an AUC of 0.78 (95%CI: 0.751~0.790); (P-values for differences in AUC using the DeLong tests were 1.609 × 10-14and 1.407 × 10-4, respectively).
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Affiliation(s)
- Sunan Cui
- Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA,
| | - Yi Luo
- Department of Radiation Oncology, University of Michigan, Medical School, MI 48103, USA,
| | - Huan Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Medical School, MI 48103, USA,
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Medical School, MI 48103, USA,
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Medical School, MI 48103, USA,
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10
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El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu QJ, Oh JH, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran JM, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Med Phys 2018; 45:e834-e840. [PMID: 30144098 DOI: 10.1002/mp.12811] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/28/2017] [Accepted: 01/22/2018] [Indexed: 11/06/2022] Open
Abstract
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California Los San Francisco, San Francisco, CA, USA
| | - Andre Dekker
- GROW-School for Oncology and Developmental Biology, Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Todd McNutt
- Department of Radiation Oncology, John Hopkins University, Baltimore, MD, USA
| | - Yaorong Ge
- Department of Software and Information Systems, University of North Carolina, Charlotte, NC, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wade Smith
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Arvind Rao
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA.,Department of Bioinformatics and Computational Biology, MD Anderson, Houston, TX, USA
| | - Clifton Fuller
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Frank Manion
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Charles Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Chao HH, Valdes G, Luna JM, Heskel M, Berman AT, Solberg TD, Simone CB. Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy. J Appl Clin Med Phys 2018; 19:539-546. [PMID: 29992732 PMCID: PMC6123157 DOI: 10.1002/acm2.12415] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 05/24/2018] [Accepted: 06/13/2018] [Indexed: 12/25/2022] Open
Abstract
Background and purpose Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose–volume constraints. Materials and methods Twenty‐five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out‐of‐bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees. Results Univariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning‐curve experiments, the dataset proved to be self‐consistent and provides a realistic model for CWS analysis. Conclusions Using machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis.
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Affiliation(s)
- Hann-Hsiang Chao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiation Oncology, University of California - San Francisco, San Francisco, CA, USA
| | - Jose M Luna
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marina Heskel
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Abigail T Berman
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy D Solberg
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiation Oncology, University of California - San Francisco, San Francisco, CA, USA
| | - Charles B Simone
- Department of Radiation Oncology, University of Maryland, School of Medicine, Baltimore, MD, USA
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12
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Valdes G, Chang AJ, Interian Y, Owen K, Jensen ST, Ungar LH, Cunha A, Solberg TD, Hsu IC. Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis. Int J Radiat Oncol Biol Phys 2018; 101:694-703. [DOI: 10.1016/j.ijrobp.2018.03.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 01/07/2018] [Accepted: 03/06/2018] [Indexed: 11/25/2022]
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13
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Anacleto A, Dias J. Data Analysis in Radiotherapy Treatments. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018070103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.
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Affiliation(s)
- Ana Anacleto
- Faculty of Economics, University of Coimbra, Coimbra, Portugal
| | - Joana Dias
- Inesc-Coimbra, CeBER, Faculty of Economics, University of Coimbra, Coimbra, Portugal
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14
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Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol 2018; 129:421-426. [PMID: 29907338 DOI: 10.1016/j.radonc.2018.05.030] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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Affiliation(s)
- Reid F Thompson
- Oregon Health & Science University, Portland, USA; VA Portland Health Care System, Portland, USA.
| | - Gilmer Valdes
- University of California San Francisco, San Francisco, USA
| | | | | | - Olivier Morin
- University of California San Francisco, San Francisco, USA
| | | | | | - Hugo J W L Aerts
- Brigham and Women's Hospital, Boston, USA; Dana Farber Cancer Institute, Boston, USA
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15
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Feng M, Valdes G, Dixit N, Solberg TD. Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs. Front Oncol 2018; 8:110. [PMID: 29719815 PMCID: PMC5913324 DOI: 10.3389/fonc.2018.00110] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 03/29/2018] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
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Affiliation(s)
- Mary Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Nayha Dixit
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Timothy D Solberg
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
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16
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[Rectal toxicity prediction based on accurate rectal surface dose summation for cervical cancer radiotherapy]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017. [PMID: 29292256 PMCID: PMC6744008 DOI: 10.3969/j.issn.1673-4254.2017.12.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To propose arectal toxicity prediction method based on deformable surface dose accumulation. METHODS The clinical data were collected retrospectively from 42patients receiving radiotherapy for cervical cancer. With the first fraction as the reference, the other fractions of rectum surface were registered to the reference fraction to obtain the deformation vector fields (DVFs), which were used to deform and sum the fractional rectal doses to yield the cumulative rectal dose. The cumulative rectal dose was flattened via 3D-2D mapping to generate a 2D rectum surface dose map. Two dosimetric features, namely DVPs and DGPs were extracted. Logistic regression embedded with sequential forward feature selection was used as the prediction model. The predictive performance was evaluated in terms of the accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS Significant improvements for rectum surface DIR were achieved. The best predictive results were achieved by using both DVPs and DGPs as the features with a sensitivity of 79.5%, a specificity of 81.3% and an AUC of 0.88. CONCLUSION The proposed method is feasible for predicting clinical rectal toxicity in patients undergoing radiotherapy for cervical cancer.
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17
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Valdes G, Simone CB, Chen J, Lin A, Yom SS, Pattison AJ, Carpenter CM, Solberg TD. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol 2017; 125:392-397. [PMID: 29162279 DOI: 10.1016/j.radonc.2017.10.014] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 10/10/2017] [Accepted: 10/10/2017] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND PURPOSE Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. MATERIAL AND METHODS Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated. RESULTS Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided. CONCLUSIONS We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients.
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Affiliation(s)
- Gilmer Valdes
- Department of Radiation Oncology, University of California, San Francisco, United States.
| | | | - Josephine Chen
- Department of Radiation Oncology, University of California, San Francisco, United States
| | - Alexander Lin
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, United States
| | - Sue S Yom
- Department of Radiation Oncology, University of California, San Francisco, United States; Department of Otolaryngology-Head and Neck Surgery, San Francisco, United States
| | | | | | - Timothy D Solberg
- Department of Radiation Oncology, University of California, San Francisco, United States
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18
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Joo YB, Kim Y, Park Y, Kim K, Ryu JA, Lee S, Bang SY, Lee HS, Yi GS, Bae SC. Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study. Arthritis Res Ther 2017; 19:244. [PMID: 29065906 PMCID: PMC5655942 DOI: 10.1186/s13075-017-1414-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 08/31/2017] [Indexed: 12/05/2022] Open
Abstract
Background Radiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis. Methods We obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA. Results A total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10-5) or SPOT (AUC 0.7449, p value 0.0423). Our model strategy also showed superior prediction accuracy in Caucasian patients with RA compared with GWAS (p value 0.0049) and SPOT (p value 0.0151). Conclusions Using various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools. Electronic supplementary material The online version of this article (doi:10.1186/s13075-017-1414-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Young Bin Joo
- Department of Rheumatology, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Republic of Korea
| | - Yul Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Youngho Park
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea
| | - Jeong Ah Ryu
- Department of Radiology, Hanyang University Hospital, Seoul, Republic of Korea
| | - Seunghun Lee
- Department of Radiology, Hanyang University Hospital, Seoul, Republic of Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea.
| | - Gwan-Su Yi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea.
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19
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El Naqa I, Kerns SL, Coates J, Luo Y, Speers C, West CML, Rosenstein BS, Ten Haken RK. Radiogenomics and radiotherapy response modeling. Phys Med Biol 2017; 62:R179-R206. [PMID: 28657906 PMCID: PMC5557376 DOI: 10.1088/1361-6560/aa7c55] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
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20
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Perspectives on making big data analytics work for oncology. Methods 2016; 111:32-44. [PMID: 27586524 DOI: 10.1016/j.ymeth.2016.08.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 08/19/2016] [Accepted: 08/25/2016] [Indexed: 12/31/2022] Open
Abstract
Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule-Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from incorporating prior knowledge, using information-theoretic techniques to modern ensemble machine learning approaches or combination of these. We will particularly discuss the pros and cons of different approaches to improve mining of big data in oncology.
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21
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Li G, Wei J, Huang H, Gaebler CP, Yuan A, Deasy JO. Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning. Biomed Phys Eng Express 2015; 1. [PMID: 27110388 DOI: 10.1088/2057-1976/1/4/045015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients (fv) to account for most of the spectrum energy (Σfv2). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves (R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.
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Affiliation(s)
- Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Jie Wei
- Department of Computer Science, City College of New York, New York, USA
| | - Hailiang Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Carl Philipp Gaebler
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Amy Yuan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
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22
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Meldolesi E, van Soest J, Damiani A, Dekker A, Alitto AR, Campitelli M, Dinapoli N, Gatta R, Gambacorta MA, Lanzotti V, Lambin P, Valentini V. Standardized data collection to build prediction models in oncology: a prototype for rectal cancer. Future Oncol 2015; 12:119-36. [PMID: 26674745 DOI: 10.2217/fon.15.295] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.
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Affiliation(s)
- Elisa Meldolesi
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andrea Damiani
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | - Nicola Dinapoli
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Roberto Gatta
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | | | - Vito Lanzotti
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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McNutt TR, Moore KL, Quon H. Needs and Challenges for Big Data in Radiation Oncology. Int J Radiat Oncol Biol Phys 2015; 95:909-915. [PMID: 27302506 DOI: 10.1016/j.ijrobp.2015.11.032] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 11/13/2015] [Accepted: 11/20/2015] [Indexed: 01/15/2023]
Affiliation(s)
- Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
| | - Kevin L Moore
- Department of Radiation Oncology, University of California - San Diego, La Jolla, California
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
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24
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Robertson SP, Quon H, Kiess AP, Moore JA, Yang W, Cheng Z, Afonso S, Allen M, Richardson M, Choflet A, Sharabi A, McNutt TR. A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients. Med Phys 2015; 42:4329-37. [DOI: 10.1118/1.4922686] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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25
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Coates J. Motivation for the inclusion of genetic risk factors of radiosensitivity alongside dosimetric and clinical parameters in predicting normal tissue effects. Acta Oncol 2015; 54:1230-1. [PMID: 25608825 DOI: 10.3109/0284186x.2014.999163] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- James Coates
- a CRUK/MRC Oxford Institute for Radiation Oncology, Department of Oncology , Oxford , UK
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26
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Wang H, Liao Z, Zhuang Y, Liu Y, Levy LB, Xu T, Yusuf SW, Gomez DR. Incidental receipt of cardiac medications and survival outcomes among patients with stage III non-small-cell lung cancer after definitive radiotherapy. Clin Lung Cancer 2014; 16:128-36. [PMID: 25450873 DOI: 10.1016/j.cllc.2014.09.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 09/22/2014] [Accepted: 09/24/2014] [Indexed: 12/25/2022]
Abstract
BACKGROUND Preclinical and epidemiologic studies suggest that receipt of some cardiac medications such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, or aspirin may have antiproliferative effects in several types of cancer. The aim of this study was to estimate survival outcomes in patients receiving incidental cardiac medications during treatment for lung cancer, and to compare outcomes with those patients not receiving these medications. PATIENTS AND METHODS We retrospectively reviewed 673 patients who had received definitive radiotherapy for stage III non-small-cell lung cancer (NSCLC). Cox proportional hazard models were used to assess associations between receipt of ACEIs, ARBs, β-blockers, or aspirin and locoregional progression-free survival (LRPFS), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS). RESULTS Multivariate analyses showed that ACEI receipt was associated with poorer LRPFS but had no effect on DMFS, DFS, or OS. Aspirin receipt was associated only with improved DMFS, and β-blocker receipt was associated with improved DMFS, DFS, and OS. CONCLUSION Incidental receipt of ACEIs was associated with a higher prevalence of local failure, whereas receipt of either β-blockers or aspirin had protective effects on survival outcomes in this large group of patients with lung cancer. This finding warrants further clinical and preclinical exploration, as it may have important implications for treating patients with lung cancer who are also receiving cardiac medications.
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Affiliation(s)
- Hongmei Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yan Zhuang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ying Liu
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lawrence B Levy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ting Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Syed Wamique Yusuf
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Daniel R Gomez
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Klement RJ, Allgäuer M, Appold S, Dieckmann K, Ernst I, Ganswindt U, Holy R, Nestle U, Nevinny-Stickel M, Semrau S, Sterzing F, Wittig A, Andratschke N, Guckenberger M. Support Vector Machine-Based Prediction of Local Tumor Control After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2014; 88:732-8. [DOI: 10.1016/j.ijrobp.2013.11.216] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Revised: 11/08/2013] [Accepted: 11/13/2013] [Indexed: 12/21/2022]
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Valentini V, Dinapoli N, Damiani A. The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results. Future Oncol 2013; 9:311-3. [PMID: 23469966 DOI: 10.2217/fon.12.197] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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El Naqa I, Pater P, Seuntjens J. Monte Carlo role in radiobiological modelling of radiotherapy outcomes. Phys Med Biol 2012; 57:R75-97. [PMID: 22571871 DOI: 10.1088/0031-9155/57/11/r75] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.
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Affiliation(s)
- Issam El Naqa
- Department of Oncology, Medical Physics Unit, Montreal, QC, Canada.
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Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol 2011; 102:239-45. [PMID: 22098794 DOI: 10.1016/j.radonc.2011.10.014] [Citation(s) in RCA: 152] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2011] [Revised: 10/13/2011] [Accepted: 10/20/2011] [Indexed: 11/12/2022]
Abstract
BACKGROUND AND PURPOSE Prediction of local failure in radiotherapy patients with non-small cell lung cancer (NSCLC) remains a challenging task. Recent evidence suggests that FDG-PET images can be used to predict outcomes. We investigate an alternative multimodality image-feature approach for predicting post-radiotherapy tumor progression in NSCLC. MATERIAL AND METHODS We analyzed pre-treatment FDG-PET/CT studies of twenty-seven NSCLC patients for local and loco-regional failures. Thirty-two tumor region features based on SUV or HU, intensity-volume-histogram (IVH) and texture characteristics were extracted. Statistical analysis was performed using Spearman's correlation (rs) and multivariable logistic regression. RESULTS For loco-regional recurrence, IVH variables had the highest univariate association. In PET, IVH-slope reached rs=0.3426 (p=0.0403). Motion correction slightly improved correlation of texture features. In CT, coefficient of variation had the highest association rs=-0.2665 (p=0.0871). Similarly for local failure, a CT-IVH parameter reached rs=0.4530 (p=0.0105). For loco-regional and local failures, a 2-parameter model of PET-V(80) and CT-V(70) yielded rs=0.4854 (p=0.0067) and rs=0.5908 (p=0.0013), respectively. Addition of dosimetric variables provided improvement in cases of loco-regional but not local failures. CONCLUSIONS We proposed a feature-based approach to evaluate radiation tumor response. Our study demonstrates that multimodality image-feature modeling provides better performance compared to existing metrics and holds promise for individualizing radiotherapy planning.
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Oh JH, Craft J, Al-Lozi R, Vaidya M, Meng Y, Deasy JO, Bradley JD, Naqa IE. A Bayesian network approach for modeling local failure in lung cancer. Phys Med Biol 2011; 56:1635-51. [PMID: 21335651 PMCID: PMC4646092 DOI: 10.1088/0031-9155/56/6/008] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.
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Affiliation(s)
- Jung Hun Oh
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Jeffrey Craft
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Rawan Al-Lozi
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Manushka Vaidya
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Yifan Meng
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Joseph O Deasy
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Jeffrey D Bradley
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
| | - Issam El Naqa
- Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110, USA
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