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Horne A, Harada K, Brown KD, Chua KLM, McDonald F, Price G, Putora PM, Rothwell DG, Faivre-Finn C. Treatment Response Biomarkers: Working Toward Personalized Radiotherapy for Lung Cancer. J Thorac Oncol 2024; 19:1164-1185. [PMID: 38615939 DOI: 10.1016/j.jtho.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/05/2024] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
Owing to major advances in the field of radiation oncology, patients with lung cancer can now receive technically individualized radiotherapy treatments. Nevertheless, in the era of precision oncology, radiotherapy-based treatment selection needs to be improved as many patients do not benefit or are not offered optimum therapies. Cost-effective robust biomarkers can address this knowledge gap and lead to individuals being offered more bespoke treatments leading to improved outcome. This narrative review discusses some of the current achievements and challenges in the realization of personalized radiotherapy delivery in patients with lung cancer.
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Affiliation(s)
- Ashley Horne
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom; Department of Radiation Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom.
| | - Ken Harada
- Department of Radiation Oncology, Showa University Northern Yokohama Hospital, Tsuzuki-ku, Yokohama, Kanagawa, Japan
| | - Katherine D Brown
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom; Department of Research and Innovation, The Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Kevin Lee Min Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | | | - Gareth Price
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Paul Martin Putora
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Radiation Oncology, Inselspital, University of Bern, Bern, Switzerland
| | - Dominic G Rothwell
- CR-UK National Biomarker Centre, University of Manchester, Manchester, United Kingdom
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom; Department of Radiation Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
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2
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Bekker HL, Winterbottom AE, Gavaruzzi T, Finderup J, Mooney A. Decision aids to assist patients and professionals in choosing the right treatment for kidney failure. Clin Kidney J 2023; 16:i20-i38. [PMID: 37711634 PMCID: PMC10497379 DOI: 10.1093/ckj/sfad172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Indexed: 09/16/2023] Open
Abstract
Background Kidney services vary in the way they involve people with kidney failure (PwKF) in treatment decisions as management needs change. We discuss how decision-science applications support proactively PwKF to make informed decisions between treatment options with kidney professionals. Methods A conceptual review of findings about decision making and use of decision aids in kidney services, synthesized with reference to: the Making Informed Decisions-Individually and Together (MIND-IT) multiple stakeholder decision makers framework; and the Medical Research Council-Complex Intervention Development and Evaluation research framework. Results This schema represents the different types of decision aids that support PwKF and professional reasoning as they manage kidney disease individually and together; adjustments at micro, meso and macro levels supports integration in practice. Conclusion Innovating services to meet clinical guidelines on enhancing shared decision making processes means enabling all stakeholders to use decision aids to meet their goals within kidney pathways at individual, service and organizational levels.
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Affiliation(s)
- Hilary L Bekker
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Department of Public Health, Aarhus University, Denmark
- ResCenPI – Research Centre for Patient Involvement, Aarhus University, Aarhus and the Central Denmark Region, Denmark
| | - Anna E Winterbottom
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Renal Unit, St James's University Hospital, Leeds Teaching Hospital Trust, Leeds, UK
| | - Teresa Gavaruzzi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Jeanette Finderup
- ResCenPI – Research Centre for Patient Involvement, Aarhus University, Aarhus and the Central Denmark Region, Denmark
- Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andrew Mooney
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Renal Unit, St James's University Hospital, Leeds Teaching Hospital Trust, Leeds, UK
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3
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Spencer KL, Absolom KL, Allsop MJ, Relton SD, Pearce J, Liao K, Naseer S, Salako O, Howdon D, Hewison J, Velikova G, Faivre-Finn C, Bekker HL, van der Veer SN. Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice. JCO Clin Cancer Inform 2023; 7:e2300070. [PMID: 37976441 PMCID: PMC10681558 DOI: 10.1200/cci.23.00070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions. RESULTS Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population. CONCLUSION Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.
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Affiliation(s)
- Katie L. Spencer
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Kate L. Absolom
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Matthew J. Allsop
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Samuel D. Relton
- Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Jessica Pearce
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Kuan Liao
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Centre for Health Informatics, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sairah Naseer
- School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Omolola Salako
- College of Medicine, University of Lagos, Lagos, Nigeria
| | - Daniel Howdon
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Jenny Hewison
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Galina Velikova
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Corinne Faivre-Finn
- Institute of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Hilary L. Bekker
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Sabine N. van der Veer
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Centre for Health Informatics, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
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Nair NS, Kothari B, Gupta S, Kanann S, Vanmali V, Hawaldar R, Tondare A, Siddique S, Parmar V, Joshi S, Badwe RA. Validation of PREDICT Version 2.2 in a Retrospective Cohort of Indian Women With Operable Breast Cancer. JCO Glob Oncol 2023; 9:e2300114. [PMID: 38085062 PMCID: PMC10846767 DOI: 10.1200/go.23.00114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE Online prediction models that use known prognostic factors in breast cancer (BC) are routinely used to assist in decisions for adjuvant therapy. PREDICT Version 2.2 (P2.2) is one such online tool, which uses tumor size, lymph node involvement, grade, age, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, and Ki67. We performed an external validation in a retrospective cohort of patients treated at a tertiary center in India. METHODS Women with operable BC between 2008 and 2016 with nonmetastatic, T1-T2 invasive, and HER2 receptor-negative BC and with available 5-year overall survival (OS) data were selected. Median predicted 5-year OS rates were used to calculate predicted events for the whole cohort and subgroups. The chi-square test was used to evaluate the goodness of fit of the tool. RESULTS Of 11,760 cases registered between 2008 and 2016, 2,783 (23.66%) eligible patients with a median age of 50 (26-70) years and a median pT size of 2.5 (0.1-5) cm, 2,037 (73.19%) with grade 3 tumors, 1,172 (42.11%) with node-positive disease, 817 (29.35%) with triple-negative breast cancer, and 1,966 (70.64%) with HR-positive BC were included in the analysis. The observed 5-year OS and predicted 5-year OS in the whole cohort were 94.8% and 90.00%, respectively, with an absolute difference of 4.8% (95% CI, 3.417 to 6.198, P < .001). The observed 5-year OS and predicted 5-year OS were also different in various subgroups. CONCLUSION PREDICT version 2.2 overestimated the number of deaths, with lower predicted 5-year OS compared with the observed value, in this retrospective Indian cohort. The reasons for this discrepancy could be differing biologic characteristics and possible selection bias in our cohort. We recommend a prospective validation of PREDICT in Indian patients and advocate caution in its use until such validation is achieved.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - RA Badwe
- Tata Memorial Centre, Mumbai, India
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Aly F, Hansen CR, Al Mouiee D, Sundaresan P, Haidar A, Vinod S, Holloway L. Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias. Radiother Oncol 2023; 183:109629. [PMID: 36934895 DOI: 10.1016/j.radonc.2023.109629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/20/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.
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Affiliation(s)
- Farhannah Aly
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
| | - Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
| | - Daniel Al Mouiee
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Purnima Sundaresan
- Sydney West Radiation Oncology Network, Western Sydney Local Health District, Sydney, Australia; Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Ali Haidar
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia
| | - Shalini Vinod
- Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia
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Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology. Biomedicines 2022; 10:biomedicines10112679. [DOI: 10.3390/biomedicines10112679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models.
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Liao K, Wang T, Coomber-Moore J, Wong DC, Gomes F, Faivre-Finn C, Sperrin M, Yorke J, van der Veer SN. Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review. BMC Cancer 2022; 22:1076. [PMID: 36261794 PMCID: PMC9580146 DOI: 10.1186/s12885-022-10151-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/08/2022] [Indexed: 11/24/2022] Open
Abstract
Background There is growing interest in the collection and use of patient-reported outcome measures (PROMs) to support clinical decision making in patients with non-small cell lung cancer (NSCLC). However, an overview of research into the prognostic value of PROMs is currently lacking. Aim To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC. Methods We systematically searched Medline, Embase, CINAHL Plus and Scopus for English-language articles published from 2011 to 2021 that report prognostic factor study, prognostic model development or validation study. Example data charting forms from the Cochrane Prognosis Methods Group guided our data charting on study characteristics, PROMs as predictors, predicted outcomes, and statistical methods. Two reviewers independently charted the data and critically appraised studies using the QUality In Prognosis Studies (QUIPS) tool for prognostic factor studies, and the risk of bias assessment section of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for prognostic model studies. Results Our search yielded 2,769 unique titles of which we included 31 studies, reporting the results of 33 unique analyses and models. Out of the 17 PROMs used for prediction, the EORTC QLQ-C30 was most frequently used (16/33); 12/33 analyses used PROM subdomain scores instead of the overall scores. PROMs data was mostly collected at baseline (24/33) and predominantly used to predict survival (32/33) but seldom other clinical outcomes (1/33). Almost all prognostic factor studies (26/27) had moderate to high risk of bias and all four prognostic model development studies had high risk of bias. Conclusion There is an emerging body of research into the value of PROMs as a prognostic factor for survival in people with NSCLC but the methodological quality of this research is poor with significant bias. This warrants more robust studies into the prognostic value of PROMs, in particular for predicting outcomes other than survival. This will enable further development of PROM-based prediction models to support clinical decision making in NSCLC. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10151-z.
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Affiliation(s)
- Kuan Liao
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
| | - Tianxiao Wang
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Jake Coomber-Moore
- Patient-Centred Research Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - David C Wong
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.,Department of Computer Science, University of Manchester, Manchester, UK
| | - Fabio Gomes
- Medical Oncology Department, The Christie NHS Foundation Trust, Manchester, UK
| | - Corinne Faivre-Finn
- The Christie NHS foundation Trust, Manchester, UK.,Division of Cancer Science, The University of Manchester, Manchester, UK
| | - Matthew Sperrin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Janelle Yorke
- Patient-Centred Research Centre, The Christie NHS Foundation Trust, Manchester, UK.,Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK
| | - Sabine N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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Jena R, Dekker A, Kang J. A Glimmer of Hope Within the Mountain of Hype - Reviewing the Role of Artificial Intelligence in Radiotherapy. Clin Oncol (R Coll Radiol) 2021; 34:71-73. [PMID: 34924257 DOI: 10.1016/j.clon.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/03/2021] [Indexed: 11/03/2022]
Affiliation(s)
- R Jena
- Department of Oncology, University of Cambridge, Cambridge, UK.
| | - A Dekker
- MAASTRO Clinic, Maastricht, the Netherlands
| | - J Kang
- University of Washington Medical Center Montlake - Radiation Oncology Center, Seattle, Washington, USA
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