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Zhou J, Wang X, Li Y, Yang Y, Shi J. Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism. BMC Med Inform Decis Mak 2024; 24:141. [PMID: 38802861 PMCID: PMC11131248 DOI: 10.1186/s12911-024-02543-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data. METHODS A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH). RESULTS The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788). CONCLUSIONS In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.
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Affiliation(s)
- Jun Zhou
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xin Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China
| | - Yiyao Li
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China
| | - Yuqing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Juhong Shi
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.
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Nickolls BJ, Relton C, Hemkens L, Zwarenstein M, Eldridge S, McCall SJ, Griffin XL, Sohanpal R, Verkooijen HM, Maguire JL, McCord KA. Randomised trials conducted using cohorts: a scoping review. BMJ Open 2024; 14:e075601. [PMID: 38458814 PMCID: PMC10928784 DOI: 10.1136/bmjopen-2023-075601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 11/24/2023] [Indexed: 03/10/2024] Open
Abstract
INTRODUCTION Cohort studies generate and collect longitudinal data for a variety of research purposes. Randomised controlled trials (RCTs) increasingly use cohort studies as data infrastructures to help identify and recruit trial participants and assess outcomes. OBJECTIVE To examine the extent, range and nature of research using cohorts for RCTs and describe the varied definitions and conceptual boundaries for RCTs using cohorts. DESIGN Scoping review. DATA SOURCES Searches were undertaken in January 2021 in MEDLINE (Ovid) and EBM Reviews-Cochrane Methodology Registry (Final issue, third Quarter 2012). ELIGIBILITY CRITERIA Reports published between January 2007 and December 2021 of (a) cohorts used or planned to be used, to conduct RCTs, or (b) RCTs which use cohorts to recruit participants and/or collect trial outcomes, or (c) methodological studies discussing the use of cohorts for RCTs. DATA EXTRACTION AND SYNTHESIS Data were extracted on the condition being studied, age group, setting, country/continent, intervention(s) and comparators planned or received, unit of randomisation, timing of randomisation, approach to informed consent, study design and terminology. RESULTS A total of 175 full-text articles were assessed for eligibility. We identified 61 protocols, 9 descriptions of stand-alone cohorts intended to be used for future RCTs, 39 RCTs using cohorts and 34 methodological papers.The use and scope of this approach is growing. The thematics of study are far-ranging, including population health, oncology, mental and behavioural disorders, and musculoskeletal conditions.Authors reported that this approach can lead to more efficient recruitment, more representative samples, and lessen disappointment bias and crossovers. CONCLUSION This review outlines the development of cohorts to conduct RCTs including the range of use and innovative changes and adaptations. Inconsistencies in the use of terminology and concepts are highlighted. Guidance now needs to be developed to support the design and reporting of RCTs conducted using cohorts.
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Affiliation(s)
- Beverley Jane Nickolls
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Clare Relton
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Lars Hemkens
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Meta-Research Innovation Center Berlin (METRICS-B), Berlin Institute of Health, Berlin, Germany
| | - Merrick Zwarenstein
- Department of Family Medicine, Western University, London, Ontario, Canada
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Sandra Eldridge
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Stephen J McCall
- National Perinatal Epidemiology Unit, Clinical Trials Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Ras Beirut, Lebanon
| | - Xavier Luke Griffin
- Bone and Joint Health, Blizard Institute, Queen Mary University of London, London, UK
- Barts Health NHS Trust, Royal London Hospital, London, UK
| | - Ratna Sohanpal
- Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Helena M Verkooijen
- University Medical Centre Utrecht, Utrecht, The Netherlands
- University of Utrecht, Utrecht, The Netherlands
| | - Jonathon L Maguire
- University of Toronto Institute of Health Policy Management and Evaluation, Toronto, Ontario, Canada
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Coates A, Chung AQH, Lessard L, Grudniewicz A, Espadero C, Gheidar Y, Bemgal S, Da Silva E, Sauré A, King J, Fung-Kee-Fung M. The use and role of digital technology in learning health systems: A scoping review. Int J Med Inform 2023; 178:105196. [PMID: 37619395 DOI: 10.1016/j.ijmedinf.2023.105196] [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: 04/18/2023] [Revised: 07/12/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVE The review aimed to identify which digital technologies are proposed or used within learning health systems (LHS) and to analyze the extent to which they support learning processes in LHS. MATERIALS AND METHODS Multiple databases and grey literature were searched with terms related to LHS. Manual searches and backward searches of reference lists were also undertaken. The review considered publications from 2007 to 2022. Records focusing on LHS, referring to one or more digital technologies, and describing how at least one digital technology could be used in LHS were included. RESULTS 2046 records were screened for inclusion and 154 records were included in the analysis. Twenty categories of digital technology were identified. The two most common ones across records were data recording and processing and electronic health records. Digital technology was primarily leveraged to support data access and aggregation and data analysis, two of the seven recognized learning processes within LHS learning cycles. DISCUSSION The results of the review show that a wide array of digital technologies is being leveraged to support learning cycles within LHS. Nevertheless, an over-reliance on a narrow set of technologies supporting knowledge discovery, a lack of direct evaluation of digital technologies and ambiguity in technology descriptions are hindering the realization of the LHS vision. CONCLUSION Future LHS research and initiatives should aim to integrate digital technology to support practice change and impact evaluation. The use of recognized evaluation methods for health information technology and more detailed descriptions of proposed technologies are also recommended.
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Affiliation(s)
- Alison Coates
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | | | - Lysanne Lessard
- Telfer School of Management, University of Ottawa, Ottawa, Canada, Institut du Savoir Montfort - Research, Ottawa, Canada, LIFE Research Institute, University of Ottawa, Ottawa, Canada.
| | - Agnes Grudniewicz
- Telfer School of Management, University of Ottawa, Ottawa, Canada, Institut du Savoir Monfort - Research, Ottawa, Canada
| | - Cathryn Espadero
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - Yasaman Gheidar
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - Sampath Bemgal
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | | | - Antoine Sauré
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - James King
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Michael Fung-Kee-Fung
- Departments of Obstetrics-Gynaecology and Surgery, Faculty of Medicine, University of Ottawa, Ottawa, Canada, The Ottawa Hospital - General Campus, University of Ottawa/Ottawa Regional Cancer Centre, Ottawa, Canada
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El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, Parodi K. Translation of AI into oncology clinical practice. Oncogene 2023; 42:3089-3097. [PMID: 37684407 DOI: 10.1038/s41388-023-02826-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Les Folio
- Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Ahmad A Tarhini
- Cutaneous Oncology and Immunology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
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Mahmoud HA, Oteify W, Elkhayat H, Zaher AM, Mohran TZ, Mekkawy N. Volumetric parameters of the primary tumor and whole-body tumor burden derived from baseline 18F-FDG PET/CT can predict overall survival in non-small cell lung cancer patients: initial results from a single institution. Eur J Hybrid Imaging 2022; 6:37. [PMID: 36575330 PMCID: PMC9794406 DOI: 10.1186/s41824-022-00158-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/29/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) are volumetric parameters derived from 18F-FDG PET/CT, suggested to have a prognostic value in cancer patients. Our study aimed to test whether these volumetric parameters of the primary tumor and whole-body tumor burden (WBTB) can predict overall survival (OS) in non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS Thirty biopsy-proven NSCLC patients who had not begun anti-tumor therapy were included in this prospective study. A baseline 18F-FDG PET/CT study was acquired. Scans were interpreted visually and semi-quantitatively by drawing a 3D volume of interest (VOI) over the primary tumor and all positive lesions to calculate metabolic, volumetric parameters, and WBTB. The PET parameters were used to stratify patients into high- and low-risk categories. The overall survival was estimated from the date of scanning until the date of death or last follow-up. RESULTS At a median follow-up of 22.73 months, the mean OS was shorter among patients with higher tu MTV and tu TLG and high WBTB. High WB TLG was independently associated with the risk of death (p < 0.025). Other parameters, e.g., SUVmax, SUVpeak, and SUVmean, were not predictive of outcomes in these patients. CONCLUSION In patients with NSCLC, tu MTV, tu TLG, and WBTB determined on initial staging 18F-FDG PET/CT seems to be a strong, independent imaging biomarker to predict OS, superior to the clinical assessment of the primary tumor. The WB TLG was found to be the best predictor of OS.
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Affiliation(s)
- Hemat A. Mahmoud
- grid.252487.e0000 0000 8632 679XDepartment of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Assiut University, Asyût, Egypt
| | - Walaa Oteify
- grid.252487.e0000 0000 8632 679XDepartment of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Assiut University, Asyût, Egypt
| | - Hussein Elkhayat
- grid.252487.e0000 0000 8632 679XCardiothoracic Surgery Department, Faculty of Medicine, Assiut University, Asyût, Egypt
| | - Ahmed M. Zaher
- grid.7776.10000 0004 0639 9286Nuclear Medicine Department, National Cancer Institute, Cairo University, Cairo, Egypt
| | - Taha Zaki Mohran
- grid.252487.e0000 0000 8632 679XDepartment of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Assiut University, Asyût, Egypt
| | - Nesreen Mekkawy
- grid.252487.e0000 0000 8632 679XDepartment of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Assiut University, Asyût, Egypt
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Field M, I Thwaites D, Carolan M, Delaney GP, Lehmann J, Sykes J, Vinod S, Holloway L. Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer. J Biomed Inform 2022; 134:104181. [PMID: 36055639 DOI: 10.1016/j.jbi.2022.104181] [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: 02/13/2022] [Revised: 04/29/2022] [Accepted: 08/20/2022] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers. METHODS An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy. RESULTS The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data. CONCLUSION The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
| | - Martin Carolan
- Illawarra Cancer Care Centre, Wollongong, NSW, Australia
| | - Geoff P Delaney
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Joerg Lehmann
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Department of Radiation Oncology, Calvary Mater Newcastle, NSW, Australia
| | - Jonathan Sykes
- Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia; Blacktown Haematology and Oncology Cancer Care Centre, Blacktown Hospital, Blacktown, NSW, Australia; Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, NSW, Australia
| | - Shalini Vinod
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, NSW, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Institute of Medical Physics, School of Physics, University of Sydney, NSW, Australia
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7
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Linardos A, Kushibar K, Walsh S, Gkontra P, Lekadir K. Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Sci Rep 2022; 12:3551. [PMID: 35241683 PMCID: PMC8894335 DOI: 10.1038/s41598-022-07186-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 02/02/2022] [Indexed: 12/25/2022] Open
Abstract
Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients' privacy, federated learning promises to alleviate these issues, by enabling diligent multi-center studies. We present the first simulated federated learning study on the modality of cardiovascular magnetic resonance and use four centers derived from subsets of the M&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy. We adapt a 3D-CNN network pretrained on action recognition and explore two different ways of incorporating shape prior information to the model, and four different data augmentation set-ups, systematically analyzing their impact on the different collaborative learning choices. We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results that are competitive with traditional centralized learning. We further find that federatively trained models exhibit increased robustness and are more sensitive to domain shift effects.
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Affiliation(s)
- Akis Linardos
- Department of Mathematics and Computer Science, University of Barcelona, 08007, Barcelona, Spain.
| | - Kaisar Kushibar
- Department of Mathematics and Computer Science, University of Barcelona, 08007, Barcelona, Spain
| | | | - Polyxeni Gkontra
- Department of Mathematics and Computer Science, University of Barcelona, 08007, Barcelona, Spain
| | - Karim Lekadir
- Department of Mathematics and Computer Science, University of Barcelona, 08007, Barcelona, Spain
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Damiani A, Masciocchi C, Lenkowicz J, Capocchiano ND, Boldrini L, Tagliaferri L, Cesario A, Sergi P, Marchetti A, Luraschi A, Patarnello S, Valentini V. Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.768266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.
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Field M, Vinod S, Aherne N, Carolan M, Dekker A, Delaney G, Greenham S, Hau E, Lehmann J, Ludbrook J, Miller A, Rezo A, Selvaraj J, Sykes J, Holloway L, Thwaites D. Implementation of the Australian Computer-Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. J Med Imaging Radiat Oncol 2021; 65:627-636. [PMID: 34331748 DOI: 10.1111/1754-9485.13287] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/29/2021] [Indexed: 12/28/2022]
Abstract
INTRODUCTION There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. METHODS A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. RESULTS The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024). CONCLUSION Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Shalini Vinod
- South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Noel Aherne
- Mid North Coast Cancer Institute, Coffs Harbour, New South Wales, Australia.,Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Martin Carolan
- Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Geoff Delaney
- South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Stuart Greenham
- Mid North Coast Cancer Institute, Coffs Harbour, New South Wales, Australia
| | - Eric Hau
- Sydney West Radiation Oncology Network, Sydney, Australia.,Westmead Clinical School, University of Sydney, Sydney, New South Wales, Australia
| | - Joerg Lehmann
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, New South Wales, Australia.,Department of Radiation Oncology, Calvary Mater, Newcastle, New South Wales, Australia.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Joanna Ludbrook
- Department of Radiation Oncology, Calvary Mater, Newcastle, New South Wales, Australia
| | - Andrew Miller
- Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia
| | - Angela Rezo
- Canberra Health Services, Canberra, Australian Capital Territory, Australia
| | - Jothybasu Selvaraj
- South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia.,Canberra Health Services, Canberra, Australian Capital Territory, Australia
| | - Jonathan Sykes
- Sydney West Radiation Oncology Network, Sydney, Australia.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
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Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 19:13-24. [PMID: 34307915 PMCID: PMC8295850 DOI: 10.1016/j.phro.2021.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/23/2022]
Abstract
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Michael Jameson
- GenesisCare, Alexandria, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia
| | - Noel Aherne
- Mid North Coast Cancer Institute, NSW, Australia.,Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.,Cancer Therapy Centre, Liverpool Hospital, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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11
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van Wijk Y, Ramaekers B, Vanneste BGL, Halilaj I, Oberije C, Chatterjee A, Marcelissen T, Jochems A, Woodruff HC, Lambin P. Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study. Cancers (Basel) 2021; 13:cancers13112687. [PMID: 34072509 PMCID: PMC8198879 DOI: 10.3390/cancers13112687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. Abstract The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.
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Affiliation(s)
- Yvonka van Wijk
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
- Correspondence:
| | - Bram Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Ben G. L. Vanneste
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
| | - Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Cary Oberije
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Tom Marcelissen
- Department of Urology, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands;
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; (I.H.); (C.O.); (A.C.); (A.J.); (H.C.W.); (P.L.)
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12
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Rocha-Filho DR, Peixoto RD, Weschenfelder RF, Rego JFM, Riechelmann R, Coutinho AK, Fernandes GS, Jacome AA, Andrade AC, Murad AM, Mello CAL, Miguel DSCG, Gomes DBD, Racy DJ, Moraes ED, Akaishi EH, Carvalho ES, Mello ES, Filho FM, Coimbra FJF, Capareli FC, Arruda FF, Vieira FMAC, Takeda FR, Cotti GCC, Pereira GLS, Paulo GA, Ribeiro HSC, Lourenco LG, Crosara M, Toneto MG, Oliveira MB, de Lourdes Oliveira M, Begnami MD, Forones NM, Yagi O, Ashton-Prolla P, Aguillar PB, Amaral PCG, Hoff PM, Araujo RLC, Di Paula Filho RP, Gansl RC, Gil RA, Pfiffer TEF, Souza T, Ribeiro U, Jesus VHF, Costa WL, Prolla G. Brazilian Group of Gastrointestinal Tumours' consensus guidelines for the management of oesophageal cancer. Ecancermedicalscience 2021; 15:1195. [PMID: 33889204 PMCID: PMC8043684 DOI: 10.3332/ecancer.2021.1195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Indexed: 11/28/2022] Open
Abstract
Oesophageal cancer is among the ten most common types of cancer worldwide. More than 80% of the cases and deaths related to the disease occur in developing countries. Local socio-economic, epidemiologic and healthcare particularities led us to create a Brazilian guideline for the management of oesophageal and oesophagogastric junction (OGJ) carcinomas. The Brazilian Group of Gastrointestinal Tumours invited 50 physicians with different backgrounds, including radiology, pathology, endoscopy, nuclear medicine, genetics, oncological surgery, radiotherapy and clinical oncology, to collaborate. This document was prepared based on an extensive review of topics related to heredity, diagnosis, staging, pathology, endoscopy, surgery, radiation, systemic therapy (including checkpoint inhibitors) and follow-up, which was followed by presentation, discussion and voting by the panel members. It provides updated evidence-based recommendations to guide clinical management of oesophageal and OGJ carcinomas in several scenarios and clinical settings.
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Affiliation(s)
- Duilio R Rocha-Filho
- Hospital Universitário Walter Cantídio, 60430-372 Fortaleza, Brazil
- Grupo Oncologia D’Or, 04535-110 São Paulo, Brazil
| | | | | | | | | | | | | | | | | | | | | | | | - Diogo B D Gomes
- Hospital Israelita Albert Einstein, 05652-900, São Paulo, Brazil
| | - Douglas J Racy
- Hospital Beneficência Portuguesa de São Paulo, 01323-001 São Paulo, Brazil
| | | | - Eduardo H Akaishi
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
| | | | - Evandro S Mello
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
| | - Fauze Maluf Filho
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
| | | | | | | | | | - Flavio R Takeda
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
| | | | | | - Gustavo A Paulo
- Universidade Federal de São Paulo, 04040-003 São Paulo, Brazil
| | | | | | | | | | - Marcos B Oliveira
- Faculdade de Ciências Médicas da Santa Casa de São Paulo, 01238-010 São Paulo, Brazil
| | | | | | - Nora M Forones
- Universidade Federal de São Paulo, 04040-003 São Paulo, Brazil
| | - Osmar Yagi
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
| | | | | | | | - Paulo M Hoff
- Grupo Oncologia D’Or, 04535-110 São Paulo, Brazil
| | | | | | | | | | | | - Tulio Souza
- Hospital Aliança de Salvador, 41920-900 Salvador, Brazil
| | - Ulysses Ribeiro
- Faculdade de Medicina da Universidade de São Paulo, 01246903 São Paulo, Brazil
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13
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The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer-ATTM.BLADE. J Pers Med 2021; 11:jpm11020143. [PMID: 33669549 PMCID: PMC7926376 DOI: 10.3390/jpm11020143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 01/09/2023] Open
Abstract
Background: During the 2016 Assisi Think Tank Meeting (ATTM) on breast cancer, the panel of experts proposed developing a validated system, based on rapid learning health care (RLHC) principles, to standardize inter-center data collection and promote personalized treatments for breast cancer. Material and Methods: The seven-step Breast LArge DatabasE (BLADE) project included data collection, analysis, application, and evaluation on a data-sharing platform. The multidisciplinary team developed a consensus-based ontology of validated variables with over 80% agreement. This English-language ontology constituted a breast cancer library with seven knowledge domains: baseline, primary systemic therapy, surgery, adjuvant systemic therapies, radiation therapy, follow-up, and toxicity. The library was uploaded to the BLADE domain. The safety of data encryption and preservation was tested according to General Data Protection Regulation (GDPR) guidelines on data from 15 clinical charts. The system was validated on 64 patients who had undergone post-mastectomy radiation therapy. In October 2018, the BLADE system was approved by the Ethical Committee of Fondazione Policlinico Gemelli IRCCS, Rome, Italy (Protocol No. 0043996/18). Results: From June 2016 to July 2019, the multidisciplinary team completed the work plan. An ontology of 218 validated variables was uploaded to the BLADE domain. The GDPR safety test confirmed encryption and data preservation (on 5000 random cases). All validation benchmarks were met. Conclusion:BLADE is a support system for follow-up and assessment of breast cancer care. To successfully develop and validate it as the first standardized data collection system, multidisciplinary collaboration was crucial in selecting its ontology and knowledge domains. BLADE is suitable for multi-center uploading of retrospective and prospective clinical data, as it ensures anonymity and data privacy.
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14
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Russnes HG. Clinical trials in the era of precision cancer medicine - for the few or for the many? Acta Oncol 2020; 59:731-732. [PMID: 32579040 DOI: 10.1080/0284186x.2020.1777582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Hege G. Russnes
- Head of Experimental Pathology and Trial Support, Department of Pathology, Clinic for Laboratory Medicine, Oslo University Hospital, Oslo, Norway
- Group leader, Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Head of the National Precision Medicine Competence Network, Norway
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15
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Mallappallil M, Sabu J, Gruessner A, Salifu M. A review of big data and medical research. SAGE Open Med 2020; 8:2050312120934839. [PMID: 32637104 PMCID: PMC7323266 DOI: 10.1177/2050312120934839] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 05/21/2020] [Indexed: 12/11/2022] Open
Abstract
Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. "Big data" is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data) and the relationships between industry, government regulators, and academics. Cultural changes along with easy access to information via the Internet facilitate ease of participation by more people. Current needs demand quick answers which may be supplied by big data, biobanks, and changes in flexibility in study design. Big data can reveal health patterns, and promises to provide solutions that have previously been out of society's grasp; however, the murkiness of international laws, questions of data ownership, public ignorance, and privacy and security concerns are slowing down the progress that could otherwise be achieved by the use of big data. The goal of this descriptive review is to create awareness of the ramifications for big data and to encourage readers that this trend is positive and will likely lead to better clinical solutions, but, caution must be exercised to reduce harm.
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Affiliation(s)
| | - Jacob Sabu
- State University of New York at Downstate, Brooklyn, NY, USA
| | | | - Moro Salifu
- State University of New York at Downstate, Brooklyn, NY, USA
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16
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Walsh S, de Jong EEC, van Timmeren JE, Ibrahim A, Compter I, Peerlings J, Sanduleanu S, Refaee T, Keek S, Larue RTHM, van Wijk Y, Even AJG, Jochems A, Barakat MS, Leijenaar RTH, Lambin P. Decision Support Systems in Oncology. JCO Clin Cancer Inform 2020; 3:1-9. [PMID: 30730766 PMCID: PMC6873918 DOI: 10.1200/cci.18.00001] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Affiliation(s)
- Seán Walsh
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Inge Compter
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Simon Keek
- Maastricht University, Maastricht, the Netherlands
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17
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Grau C, Durante M, Georg D, Langendijk JA, Weber DC. Particle therapy in Europe. Mol Oncol 2020; 14:1492-1499. [PMID: 32223048 PMCID: PMC7332216 DOI: 10.1002/1878-0261.12677] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/17/2019] [Accepted: 03/22/2020] [Indexed: 12/16/2022] Open
Abstract
Particle therapy using protons or heavier ions is currently the most advanced form of radiotherapy and offers new opportunities for improving cancer care and research. Ions deposit the dose with a sharp maximum – the Bragg peak – and normal tissue receives a much lower dose than what is delivered by X‐ray therapy. Particle therapy has also biological advantages due to the high linear energy transfer of the charged particles around the Bragg peak. The introduction of particle therapy has been slow in Europe, but within the last decade, more than 20 clinical facilities have opened and facilitated access to this frontline therapy. In this review article, the basic concepts of particle therapy are reviewed along with a presentation of the current clinical indications, the European clinical research, and the established networks.
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Affiliation(s)
- Cai Grau
- Department of Oncology and Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Marco Durante
- Biophysics Department, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany.,Institut für Festkörperphysik, Technische Universität Darmstadt, Germany
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna/AKH Wien, Vienna, Austria
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Centrum Groningen, Groningen, The Netherlands
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18
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Bogowicz M, Jochems A, Deist TM, Tanadini-Lang S, Huang SH, Chan B, Waldron JN, Bratman S, O'Sullivan B, Riesterer O, Studer G, Unkelbach J, Barakat S, Brakenhoff RH, Nauta I, Gazzani SE, Calareso G, Scheckenbach K, Hoebers F, Wesseling FWR, Keek S, Sanduleanu S, Leijenaar RTH, Vergeer MR, Leemans CR, Terhaard CHJ, van den Brekel MWM, Hamming-Vrieze O, van der Heijden MA, Elhalawani HM, Fuller CD, Guckenberger M, Lambin P. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Sci Rep 2020; 10:4542. [PMID: 32161279 PMCID: PMC7066122 DOI: 10.1038/s41598-020-61297-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 01/28/2020] [Indexed: 12/23/2022] Open
Abstract
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10-7). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.
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Grants
- P30 CA016672 NCI NIH HHS
- P50 CA097007 NCI NIH HHS
- R01 DE025248 NIDCR NIH HHS
- R01 CA214825 NCI NIH HHS
- R25 EB025787 NIBIB NIH HHS
- R56 DE025248 NIDCR NIH HHS
- R01 CA218148 NCI NIH HHS
- Swiss National Science Foundation Sinergia grant (310030_173303) and Scientific Exchange grant (IZSEZ0_180524).
- This work was also supported by the Interreg grant EURADIOMICS and the Dutch technology Foundation STW (grant n° 10696 DuCAT and n° P14-19 Radiomics STRaTegy), which is the applied science division of NWO, the Technology Program of the Ministry of Economic Affairs and the Manchester Cancer Research UK major centre grant. The authors also acknowledge financial support from the EU 7th framework program (ARTFORCE - n° 257144, REQUITE - n° 601826), CTMM-TraIT, EUROSTARS (E-DECIDE, DEEPMAM), Kankeronderzoekfonds Limburg from the Health Foundation Limburg, Alpe d’HuZes-KWF (DESIGN), The Dutch Cancer Society, the European Program H2020-2015-17 (ImmunoSABR - n° 733008 and BD2Decide - PHC30-689715), the ERC advanced grant (ERC-ADG-2015, n° 694812 - Hypoximmuno), SME Phase 2 (EU proposal 673780 – RAIL).
- The clinical study used as one of the cohorts was supported by a research grant from Merck (Schweiz) AG.
- Dr. Fuller is a Sabin Family Foundation Fellow. Dr. Fuller receive funding and project-relevant salary support from the National Institutes of Health (NIH), including: National Institute for Dental and Craniofacial Research Award (1R01DE025248-01/R56DE025248-01); National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program(1R01CA218148-01); National Science Foundation (NSF), Division of Mathematical Sciences; NIH Big Data to Knowledge (BD2K) Program of the National Cancer Institute Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825-01); NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672) and National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Program (R25EB025787). Dr. Fuller has received direct industry grant support and travel funding from Elekta AB.and Fuller receive funding and project-relevant salary support from NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007-10).
- This project was supported by the Swiss National Science Foundation Sinergia grant (310030_173303)
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Affiliation(s)
- Marta Bogowicz
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
| | - Arthur Jochems
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Timo M Deist
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Stephanie Tanadini-Lang
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland
| | - Shao Hui Huang
- Princess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Biu Chan
- Princess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - John N Waldron
- Princess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Scott Bratman
- Princess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Brian O'Sullivan
- Princess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Oliver Riesterer
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland
- Kantonsspital Aarau, Center for Radiation Oncology- KSA-KSB-, Aarau, Switzerland
| | - Gabriela Studer
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland
- Cantonal Hospital Lucerne, Radiation Oncology, Lucerne, Switzerland
| | - Jan Unkelbach
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland
| | - Samir Barakat
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Ruud H Brakenhoff
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands
| | - Irene Nauta
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands
| | | | - Giuseppina Calareso
- IRCCS Fondazione Istituto Nazionale dei Tumori, Radiology Department, Milan, Italy
| | - Kathrin Scheckenbach
- University Hospital Duesseldorf, Heinrich-Heine-University, Department of Otorhinolaryngology & Head/Neck, Surgery, Duesseldorf, Germany
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre, Department of Radiation Oncology, Maastricht, The Netherlands
| | - Frederik W R Wesseling
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre, Department of Radiation Oncology, Maastricht, The Netherlands
| | - Simon Keek
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Sebastian Sanduleanu
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
| | - Marije R Vergeer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - C René Leemans
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands
| | - Chris H J Terhaard
- University Medical Center Utrecht, Department of Radiotherapy, Utrecht, The Netherlands
| | - Michiel W M van den Brekel
- The Netherlands Cancer Institute, Department of Head and Neck Oncology and Surgery, Amsterdam, The Netherlands
| | - Olga Hamming-Vrieze
- The Netherlands Cancer Institute, Department of Radiation Oncology, Amsterdam, The Netherlands
| | - Martijn A van der Heijden
- The Netherlands Cancer Institute, Department of Head and Neck Oncology and Surgery, Amsterdam, The Netherlands
| | - Hesham M Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthias Guckenberger
- University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland
| | - Philippe Lambin
- GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands
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Chin S, Eccles CL, McWilliam A, Chuter R, Walker E, Whitehurst P, Berresford J, Van Herk M, Hoskin PJ, Choudhury A. Magnetic resonance-guided radiation therapy: A review. J Med Imaging Radiat Oncol 2020; 64:163-177. [PMID: 31646742 DOI: 10.1111/1754-9485.12968] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022]
Abstract
Magnetic resonance-guided radiation therapy (MRgRT) is a promising approach to improving clinical outcomes for patients treated with radiation therapy. The roles of image guidance, adaptive planning and magnetic resonance imaging in radiation therapy have been increasing over the last two decades. Technical advances have led to the feasible combination of magnetic resonance imaging and radiation therapy technologies, leading to improved soft-tissue visualisation, assessment of inter- and intrafraction motion, motion management, online adaptive radiation therapy and the incorporation of functional information into treatment. MRgRT can potentially transform radiation oncology by improving tumour control and quality of life after radiation therapy and increasing convenience of treatment by shortening treatment courses for patients. Multiple groups have developed clinical implementations of MRgRT predominantly in the abdomen and pelvis, with patients having been treated since 2014. While studies of MRgRT have primarily been dosimetric so far, an increasing number of trials are underway examining the potential clinical benefits of MRgRT, with coordinated efforts to rigorously evaluate the benefits of the promising technology. This review discusses the current implementations, studies, potential benefits and challenges of MRgRT.
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Affiliation(s)
- Stephen Chin
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
- Westmead Clinical School, University of Sydney, Sydney, New South Wales, Australia
| | - Cynthia L Eccles
- Department of Radiotherapy, The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Robert Chuter
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Emma Walker
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Philip Whitehurst
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Joseph Berresford
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Marcel Van Herk
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Peter J Hoskin
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Ananya Choudhury
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
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20
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Hansen CR, Friborg J, Jensen K, Samsøe E, Johnsen L, Zukauskaite R, Grau C, Maare C, Johansen J, Primdahl H, Bratland Å, Kristensen CA, Andersen M, Eriksen JG, Overgaard J. NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy. Acta Oncol 2019; 58:1410-1415. [PMID: 31432744 DOI: 10.1080/0284186x.2019.1654129] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Introduction: Prediction models using logistic regression may perform poorly in external patient cohorts. However, there is a need to standardize and validate models for clinical use. The purpose of this project was to describe a method for validation of external NTCP models used for patient selection in the randomized trial of protons versus photons in head and neck cancer radiotherapy, DAHANCA 35. Material and methods: Organs at risk of 588 patients treated primarily with IMRT in the randomized controlled DAHANCA19 trial were retrospectively contoured according to recent international recommendations. Dose metrics were extracted using MatLab and all clinical parameters were retrieved from the DAHANCA database. The model proposed by Christianen et al. to predict physician-rated dysphagia was validated through the closed testing, where change of the model intercept, slope and individual beta's were tested for significant prediction improvements. Results: Six months prevalence of dysphagia in the validation cohort was 33%. The closed testing procedure for physician-rated dysphagia showed that the Christianen et al. model needed an intercept refitting for the best match for the Danish patients. The intercept update increased the risk of dysphagia for the validation cohort by 7.9 ± 2.5% point. For the raw model performance, the Brier score (mean squared residual) was 0.467, which improved significantly with a new intercept to 0.415. Conclusions: The previously published Dutch dysphagia model needed an intercept update to match the Danish patient cohort. The implementation of a closed testing procedure on the current validation cohort allows quick and efficient validation of external NTCP models for patient selection in the future.
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Affiliation(s)
- C. R. Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, Australia
| | - J. Friborg
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Rigshospitalet, Copenhagen, Denmark
| | - K. Jensen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - E. Samsøe
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Copenhagen University Hospital Herlev, Copenhagen, Denmark
| | - L. Johnsen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
| | - R. Zukauskaite
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - C. Grau
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - C. Maare
- Department of Oncology, Copenhagen University Hospital Herlev, Copenhagen, Denmark
| | - J. Johansen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - H. Primdahl
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Å. Bratland
- The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | | | - M Andersen
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - J. G. Eriksen
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - J. Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
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21
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Valentini V, Marijnen C, Beets G, Bujko K, De Bari B, Cervantes A, Chiloiro G, Coco C, Gambacorta MA, Glynne-Jones R, Haustermans K, Meldolesi E, Peters F, Rödel C, Rutten H, van de Velde C, Aristei C. The 2017 Assisi Think Tank Meeting on rectal cancer: A positioning paper. Radiother Oncol 2019; 142:6-16. [PMID: 31431374 DOI: 10.1016/j.radonc.2019.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 06/06/2019] [Accepted: 07/01/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND PURPOSES To describe current practice in the management of rectal cancer, to identify uncertainties that usually arise in the multidisciplinary team (MDT)'s discussions ('grey zones') and propose next generation studies which may provide answers to them. MATERIALS AND METHODS A questionnaire on the areas of controversy in managing T2, T3 and T4 rectal cancer was drawn up and distributed to the Rectal-Assisi Think Tank Meeting (ATTM) Expert European Board. Less than 70% agreement on a treatment option was indicated as uncertainty and selected as a 'grey zone'. Topics with large disagreement were selected by the task force group for discussion at the Rectal-ATTM. RESULTS The controversial clinical issues that had been identified within cT2-cT3-cT4 needed further investigation. The discussions focused on the role of (1) neoadjuvant therapy and organ preservation on cT2-3a low-middle rectal cancer; (2) neoadjuvant therapy in cT3 low rectal cancer without high risk features; (3) total neoadjuvant therapy, radiotherapy boost and the best chemo-radiotherapy schedule in T4 tumors. A description of each area of investigation and trial proposals are reported. CONCLUSION The meeting successfully identified 'grey zones' and, in the light of new evidence, proposed clinical trials for treatment of early, intermediate and advanced stage rectal cancer.
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Affiliation(s)
- Vincenzo Valentini
- Department of Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Corrie Marijnen
- Department of Radiotherapy, Leiden University Medical Centre, the Netherlands
| | - Geerard Beets
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School of Oncology and Developmental Biology, University of Maastricht, the Netherlands
| | - Krzysztof Bujko
- Department of Radiotherapy, Maria Skłodowska-Curie Memorial Cancer Centre, Warsaw, Poland
| | - Berardino De Bari
- Service de Radio-oncologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Andres Cervantes
- Department of Medical Oncology, Biomedical Research Institute INCLIVA, University of Valencia, Spain
| | - Giuditta Chiloiro
- Department of Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Claudio Coco
- Department of Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Italy
| | | | | | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals, Leuven, Belgium
| | - Elisa Meldolesi
- Department of Radiation Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Femke Peters
- Department of Radiotherapy, Leiden University Medical Centre, the Netherlands
| | - Claus Rödel
- Department of Radiotherapy and Oncology, University Hospital Frankfurt, Goethe University, Germany
| | - Harm Rutten
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands; GROW School of Oncology and Developmental Biology, University of Maastricht, the Netherlands
| | | | - Cynthia Aristei
- Radiation Oncology Section, Department of Surgical and Biomedical Science, University of Perugia and Perugia General Hospital, Italy
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Decision Support Systems in Prostate Cancer Treatment: An Overview. BIOMED RESEARCH INTERNATIONAL 2019; 2019:4961768. [PMID: 31281840 PMCID: PMC6590598 DOI: 10.1155/2019/4961768] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 04/02/2019] [Accepted: 05/06/2019] [Indexed: 12/11/2022]
Abstract
Background A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. Methods We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals. Results Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS. Conclusions Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.
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23
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Tseng HH, Wei L, Cui S, Luo Y, Ten Haken RK, El Naqa I. Machine Learning and Imaging Informatics in Oncology. Oncology 2018; 98:344-362. [PMID: 30472716 DOI: 10.1159/000493575] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 09/10/2018] [Indexed: 01/01/2023]
Abstract
In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology. We will review example applications of ML in oncology from the literature, identify current challenges and highlight future potentials.
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Affiliation(s)
- Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sunan Cui
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Yi Luo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA,
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24
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Abstract
The favorable beam properties of protons can be translated into clinical benefits by target dose escalation to improve local control without enhancing unacceptable radiation toxicity or to spare normal tissues to prevent radiation-induced side effects without jeopardizing local tumor control. For the clinical validation of the added value of protons to improve local control, randomized controlled trials are required. For the clinical validation of the added value of protons to prevent side effects, both model-based validation or randomized controlled trials can be used. Model-based patient selection for proton therapy is crucial, independent of the validation approach. Combining these approaches in rapid learning health care systems is expected to yield the most efficient and scientifically sound way to continuously improve patient selection and the therapeutic window, eventually leading to more cancer survivors with better quality of life.
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25
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El Naqa I, Kosorok MR, Jin J, Mierzwa M, Ten Haken RK. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform 2018; 2:CCI.18.00002. [PMID: 30613823 PMCID: PMC6317743 DOI: 10.1200/cci.18.00002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.
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Affiliation(s)
- Issam El Naqa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michael R. Kosorok
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Judy Jin
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michelle Mierzwa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Randall K. Ten Haken
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
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26
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Larue RTHM, Klaassen R, Jochems A, Leijenaar RTH, Hulshof MCCM, van Berge Henegouwen MI, Schreurs WMJ, Sosef MN, van Elmpt W, van Laarhoven HWM, Lambin P. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol 2018; 57:1475-1481. [PMID: 30067421 DOI: 10.1080/0284186x.2018.1486039] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy. MATERIAL AND METHODS Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed. RESULTS In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61-0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47-0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54-0.71) and 0.62 (95%CI 0.49-0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor. CONCLUSIONS A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.
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Affiliation(s)
- Ruben T. H. M. Larue
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Remy Klaassen
- Department of Medical Oncology, Academic Medical Centre, Amsterdam, The Netherlands
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | - Wendy M. J. Schreurs
- Department of Nuclear Medicine, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Meindert N. Sosef
- Department of Surgery, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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27
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van Wijk Y, Vanneste BGL, Jochems A, Walsh S, Oberije CJ, Pinkawa M, Ramaekers BLT, Vega A, Lambin P. Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer. Acta Oncol 2018; 57:1499-1505. [PMID: 29952681 DOI: 10.1080/0284186x.2018.1484156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Previous studies revealed that dose escalated radiotherapy for prostate cancer patients leads to higher tumor control probabilities (TCP) but also to higher rectal toxicities. An isotoxic model was developed to maximize the given dose while controlling the toxicity level. This was applied to analyze the effect of an implantable rectum spacer (IRS) and extended with a genetic test of normal tissue radio-sensitivity. A virtual IRS (V-IRS) was tested using this method. We hypothesized that the patients with increased risk of toxicity would benefit more from an IRS. MATERIAL AND METHODS Sixteen localized prostate cancer patients implanted with an IRS were included in the study. Treatment planning was performed on computed tomography (CT) images before and after the placement of the IRS and with a V-IRS. The normal tissue complication probability (NTCP) was calculated using a QUANTEC reviewed model for Grade > =2 late rectal bleeding and the number of fractions of the plans were adjusted until the NTCP value was under 5%. The resulting treatment plans were used to calculate the TCP before and after placement of an IRS. This was extended by adding the effect of two published genetic single nucleotide polymorphisms (SNP's) for late rectal bleeding. RESULTS The median TCP resulting from the optimized plans in patients before the IRS was 75.1% [32.6-90.5%]. With IRS, the median TCP is significantly higher: 98.9% [80.8-99.9%] (p < .01). The difference in TCP between the V-IRS and the real IRS was 1.8% [0.0-18.0%]. Placing an IRS in the patients with SNP's improved the TCP from 49.0% [16.1-80.8%] and 48.9% [16.0-72.8%] to 96.3% [67.0-99.5%] and 90.1% [49.0-99.5%] (p < .01) respectively for either SNP. CONCLUSION This study was a proof-of-concept for an isotoxic model with genetic biomarkers with a V-IRS as a multifactorial decision support system for the decision of a placement of an IRS.
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Affiliation(s)
- Yvonka van Wijk
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Ben G. L. Vanneste
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Arthur Jochems
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Sean Walsh
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Cary J. Oberije
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Michael Pinkawa
- Department of Radiation Oncology, RWTH Aachen University, Aachen, Germany
- Department of Radiation Oncology, MediClin Robert Janker Klinik, Bonn, Germany
| | - Bram L. T. Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela, Spain
| | - Philippe Lambin
- The D‐Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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El Naqa I, Napel S, Zaidi H. Radiogenomics is the future of treatment response assessment in clinical oncology. Med Phys 2018; 45:4325-4328. [PMID: 29863785 DOI: 10.1002/mp.13035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 05/29/2018] [Accepted: 05/31/2018] [Indexed: 01/12/2023] Open
Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI, 48103-4943, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
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29
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Leijenaar RTH, Bogowicz M, Jochems A, Hoebers FJP, Wesseling FWR, Huang SH, Chan B, Waldron JN, O'Sullivan B, Rietveld D, Leemans CR, Brakenhoff RH, Riesterer O, Tanadini-Lang S, Guckenberger M, Ikenberg K, Lambin P. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol 2018; 91:20170498. [PMID: 29451412 PMCID: PMC6223271 DOI: 10.1259/bjr.20170498] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 01/17/2018] [Accepted: 02/12/2018] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative OPSCC. Here, we evaluate the use of a radiomic approach to identify the HPV status of OPSCC. METHODS Four independent cohorts, totaling 778 OPSCC patients with HPV determined by p16 were collected. We randomly assigned 80% of all data for model training (N = 628) and 20% for validation (N = 150). On the pre-treatment CT images, 902 radiomic features were calculated from the gross tumor volume. Multivariable modeling was performed using least absolute shrinkage and selection operator. To assess the impact of CT artifacts in predicting HPV (p16), a model was developed on all training data (Mall) and on the artifact-free subset of training data (Mno art). Models were validated on all validation data (Vall), and the subgroups with (Vart) and without (Vno art) artifacts. Kaplan-Meier survival analysis was performed to compare HPV status based on p16 and radiomic model predictions. RESULTS The area under the receiver operator curve for Mall and Mno art ranged between 0.70 and 0.80 and was not significantly different for all validation data sets. There was a consistent and significant split between survival curves with HPV status determined by p16 [p = 0.007; hazard ratio (HR): 0.46], Mall (p = 0.036; HR: 0.55) and Mno art (p = 0.027; HR: 0.49). CONCLUSION This study provides proof of concept that molecular information can be derived from standard medical images and shows potential for radiomics as imaging biomarker of HPV status. Advances in knowledge: Radiomics has the potential to identify clinically relevant molecular phenotypes.
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Affiliation(s)
- Ralph TH Leijenaar
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
| | - Frank JP Hoebers
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Frederik WR Wesseling
- Department of Radiation Oncology, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Sophie H Huang
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Biu Chan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - John N Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Ontario, Canada
| | - Derek Rietveld
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, Netherlands
| | - C Rene Leemans
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Ruud H Brakenhoff
- Department of Otolaryngology/Head and Neck Surgery, VU University Medical Center, Amsterdam, Netherlands
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Kristian Ikenberg
- Institute of Pathology and Molecular Pathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine GROW - School for Oncology and Developmental Biology & MCCC Maastricht University Medical Centre+ Maastricht, Maastricht, Netherlands
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Vanneste BGL, van De Beek K, Lutgens L, Lambin P. Implantation of a biodegradable rectum balloon implant: tips, Tricks and Pitfalls. Int Braz J Urol 2018; 43:1033-1042. [PMID: 28338306 PMCID: PMC5734065 DOI: 10.1590/s1677-5538.ibju.2016.0494] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 12/20/2016] [Indexed: 12/18/2022] Open
Abstract
Introduction: A rectum balloon implant (RBI) is a new device to spare rectal structures during prostate cancer radiotherapy. The theoretical advantages of a RBI are to reduce the high radiation dose to the anterior rectum wall, the possibility of a post-implant correction, and their predetermined shape with consequent predictable position. Objective: To describe, step-by-step, our mini-invasive technique for hands-free transperineal implantation of a RBI before start of radiotherapy treatment. Materials and Methods: We provide step-by-step instructions for optimization of the transperineal implantation procedure performed by urologists and/or radiation oncologists experienced with prostate brachytherapy and the use of the real-time bi-plane transrectal ultrasonography (TRUS) probe. A RBI was performed in 15 patients with localised prostate cancer. Perioperative side-effects were reported. Results: We provide ‘tips and tricks’ for optimizing the procedure and proper positioning of the RBI. Please watch the animation, see video in https://vimeo.com/205852376/789df4fae4. The side-effects included mild discomfort to slight pain at the perineal region in 8 out of 15 patients. Seven patients (47%) had no complaints at all. Two patients developed redness of the skin, where prompt antibiotic regimen was started with no further sequelae. One patient revealed a temporary urine retention, which resolved in a few hours following conservative treatment. Further no perioperative complications occurred. Conclusion: This paper describes in detail the implantation procedure for an RBI. It is a feasible, safe and very well-tolerated procedure.
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Affiliation(s)
- Ben G L Vanneste
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Kees van De Beek
- Department of Urology, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Ludy Lutgens
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, Netherlands
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Carvalho S, Leijenaar RTH, Troost EGC, van Timmeren JE, Oberije C, van Elmpt W, de Geus-Oei LF, Bussink J, Lambin P. 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study. PLoS One 2018; 13:e0192859. [PMID: 29494598 PMCID: PMC5832210 DOI: 10.1371/journal.pone.0192859] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 01/31/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor). METHODS Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b). RESULTS Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH. CONCLUSION Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.
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Affiliation(s)
- Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Ralph T. H. Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Esther G. C. Troost
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
- Institute of Radiooncology—OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Dresden, Germany
- OncoRay, National Centre for Radiation Research in Oncology, Medical Faculty and University Hospital Carl Gustav Carus of Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Janna E. van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
| | - Lioe-Fee de Geus-Oei
- Department of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
- Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, the Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW–School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC +), Maastricht, the Netherlands
- * E-mail:
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Les big data , généralités et intégration en radiothérapie. Cancer Radiother 2018; 22:73-84. [DOI: 10.1016/j.canrad.2017.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 04/11/2017] [Accepted: 04/19/2017] [Indexed: 12/25/2022]
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Jochems A, El-Naqa I, Kessler M, Mayo CS, Jolly S, Matuszak M, Faivre-Finn C, Price G, Holloway L, Vinod S, Field M, Barakat MS, Thwaites D, de Ruysscher D, Dekker A, Lambin P. A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 2018; 57:226-230. [PMID: 29034756 PMCID: PMC6108087 DOI: 10.1080/0284186x.2017.1385842] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 09/21/2017] [Indexed: 01/02/2023]
Abstract
BACKGROUND Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. MATERIAL AND METHODS Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. RESULTS Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. CONCLUSIONS Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.
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Affiliation(s)
- Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Issam El-Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Marc Kessler
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Charles S. Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Lois Holloway
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Shalini Vinod
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | - Matthew Field
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
| | | | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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Foley KG, Hills RK, Berthon B, Marshall C, Parkinson C, Lewis WG, Crosby TDL, Spezi E, Roberts SA. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. Eur Radiol 2018; 28:428-436. [PMID: 28770406 PMCID: PMC5717119 DOI: 10.1007/s00330-017-4973-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 06/26/2017] [Accepted: 06/28/2017] [Indexed: 10/26/2022]
Abstract
OBJECTIVES This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. METHODS Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). RESULTS Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24-0.47), p < 0.001], log(TLG) [5.74 (1.44-22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10-0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04-1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X2 143.14, df 3, p < 0.001) and validation cohorts (X2 20.621, df 3, p < 0.001). CONCLUSIONS This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging. KEY POINTS • PET texture analysis adds prognostic value to oesophageal cancer staging. • Texture metrics are independently and significantly associated with overall survival. • A prognostic model including texture analysis can help risk stratify patients.
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Affiliation(s)
- Kieran G Foley
- Division of Cancer & Genetics, Cardiff University, Cardiff, UK.
| | - Robert K Hills
- Haematology Clinical Trials Unit, Cardiff University, Cardiff, UK
| | | | | | | | - Wyn G Lewis
- Department of Upper GI Surgery, University Hospital of Wales, Cardiff, UK
| | - Tom D L Crosby
- Department of Oncology, Velindre Cancer Centre, Cardiff, UK
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Grau C, Høyer M, Poulsen PR, Muren LP, Korreman SS, Tanderup K, Lindegaard JC, Alsner J, Overgaard J. Rethink radiotherapy - BIGART 2017. Acta Oncol 2017; 56:1341-1352. [PMID: 29148908 DOI: 10.1080/0284186x.2017.1371326] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Cai Grau
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Morten Høyer
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Ludvig Paul Muren
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | | | - Kari Tanderup
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | | | - Jan Alsner
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
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van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Lambin P. Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 2017; 56:1537-1543. [PMID: 28826307 DOI: 10.1080/0284186x.2017.1350285] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment adaptation. The aims of this study were to (1) describe and investigate a methodology for feature selection of a longitudinal radiomics approach (2) investigate which time-point during treatment is potentially useful for early treatment response assessment. MATERIAL AND METHODS For 90 NSCLC patients CBCT scans of the first two fractions of treatment (considered as 'test-retest' scans) were analyzed, as well as weekly CBCT images. One hundred and sixteen radiomic features were extracted from the GTV of all scans and subsequently absolute and relative differences were calculated between weekly CBCT images and the CBCT of the first fraction. Test-retest scans were used to determine the smallest detectable change (C = 1.96 * SD) allowing for feature selection by choosing a minimum number of patients for which a feature should change more than 'C' to be considered as relevant. Analysis of which features change at which moment during treatment was used to investigate which time-point is potentially relevant to extract longitudinal radiomics information for early treatment response assessment. RESULTS A total of six absolute delta features changed for at least ten patients at week 2 of treatment and increased to 61 at week 3, 79 at week 4 and 85 at week 5. There was 93% overlap between features selected at week 3 and the other weeks. CONCLUSIONS This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.
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Affiliation(s)
- Janna E. van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
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Nabhan C, Feinberg BA. Clinical Pathways in Oncology: Software Solutions. JCO Clin Cancer Inform 2017; 1:1-5. [PMID: 30657387 DOI: 10.1200/cci.16.00061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Chadi Nabhan
- All authors: Cardinal Health Specialty Solutions, Dublin, OH
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Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Radiother Oncol 2017; 125:147-153. [DOI: 10.1016/j.radonc.2017.07.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 06/12/2017] [Accepted: 07/15/2017] [Indexed: 12/29/2022]
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Jochems A, Deist TM, El Naqa I, Kessler M, Mayo C, Reeves J, Jolly S, Matuszak M, Ten Haken R, van Soest J, Oberije C, Faivre-Finn C, Price G, de Ruysscher D, Lambin P, Dekker A. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys 2017; 99:344-352. [PMID: 28871984 PMCID: PMC5575360 DOI: 10.1016/j.ijrobp.2017.04.021] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 04/13/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. METHODS AND MATERIALS Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. RESULTS Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). CONCLUSIONS Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care.
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Affiliation(s)
- Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Marc Kessler
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Chuck Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jackson Reeves
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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Alitto AR, Gatta R, Vanneste B, Vallati M, Meldolesi E, Damiani A, Lanzotti V, Mattiucci GC, Frascino V, Masciocchi C, Catucci F, Dekker A, Lambin P, Valentini V, Mantini G. PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncol 2017; 13:2171-2181. [PMID: 28758431 DOI: 10.2217/fon-2017-0142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
AIM Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org . RESULTS A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. CONCLUSION The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.
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Affiliation(s)
- A R Alitto
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - R Gatta
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - Bgl Vanneste
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - M Vallati
- School of Computing & Engineering, University of Huddersfield, Huddersfield, UK
| | - E Meldolesi
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - A Damiani
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - V Lanzotti
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - G C Mattiucci
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - V Frascino
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - C Masciocchi
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - F Catucci
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - A Dekker
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - P Lambin
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - V Valentini
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - G Mantini
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
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Lessard L, Michalowski W, Fung-Kee-Fung M, Jones L, Grudniewicz A. Architectural frameworks: defining the structures for implementing learning health systems. Implement Sci 2017. [PMID: 28645319 PMCID: PMC5481948 DOI: 10.1186/s13012-017-0607-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background The vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment. Discussion We present an architectural framework for LHSs that incorporates five dimensions—goals, scientific, social, technical, and ethical—commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making. Conclusion In this paper, we outline a high-level architectural framework grounded in conceptual and empirical LHS literature. Applying this architectural framework can guide the development and implementation of new LHSs and the evolution of existing ones, as it allows for clear and critical understanding of the types of decisions that underlie LHS operations. Further research is required to assess and refine its generalizability and methods.
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Affiliation(s)
- Lysanne Lessard
- Telfer School of Management, University of Ottawa, 55 Ave. Laurier E, Ottawa, ON, K1N 6N5, Canada. .,Institut du Savoir Montfort (ISM), 202-745A Montreal Road, Ottawa, ON, K1K 0T1, Canada.
| | - Wojtek Michalowski
- Telfer School of Management, University of Ottawa, 55 Ave. Laurier E, Ottawa, ON, K1N 6N5, Canada.,Institut du Savoir Montfort (ISM), 202-745A Montreal Road, Ottawa, ON, K1K 0T1, Canada
| | - Michael Fung-Kee-Fung
- Departments of Obstetrics-Gynecology and Surgery, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, ON, K1H 8M5, Canada.,The Ottawa Hospital-General Campus, University of Ottawa/Ottawa Regional Cancer Centre, 501 Smyth Rd, Ottawa, ON, K1H 8L6, Canada
| | - Lori Jones
- University of Ottawa, 55 Ave. Laurier E, Ottawa, ON, K1N 6N5, Canada
| | - Agnes Grudniewicz
- Telfer School of Management, University of Ottawa, 55 Ave. Laurier E, Ottawa, ON, K1N 6N5, Canada
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Zindler JD, Jochems A, Lagerwaard FJ, Beumer R, Troost EGC, Eekers DBP, Compter I, van der Toorn PP, Essers M, Oei B, Hurkmans CW, Bruynzeel AME, Bosmans G, Swinnen A, Leijenaar RTH, Lambin P. Individualized early death and long-term survival prediction after stereotactic radiosurgery for brain metastases of non-small cell lung cancer: Two externally validated nomograms. Radiother Oncol 2017; 123:189-194. [PMID: 28237400 DOI: 10.1016/j.radonc.2017.02.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 12/24/2016] [Accepted: 02/05/2017] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Commonly used clinical models for survival prediction after stereotactic radiosurgery (SRS) for brain metastases (BMs) are limited by the lack of individual risk scores and disproportionate prognostic groups. In this study, two nomograms were developed to overcome these limitations. METHODS 495 patients with BMs of NSCLC treated with SRS for a limited number of BMs in four Dutch radiation oncology centers were identified and divided in a training cohort (n=214, patients treated in one hospital) and an external validation cohort n=281, patients treated in three other hospitals). Using the training cohort, nomograms were developed for prediction of early death (<3months) and long-term survival (>12months) with prognostic factors for survival. Accuracy of prediction was defined as the area under the curve (AUC) by receiver operating characteristics analysis for prediction of early death and long term survival. The accuracy of the nomograms was also tested in the external validation cohort. RESULTS Prognostic factors for survival were: WHO performance status, presence of extracranial metastases, age, GTV largest BM, and gender. Number of brain metastases and primary tumor control were not prognostic factors for survival. In the external validation cohort, the nomogram predicted early death statistically significantly better (p<0.05) than the unfavorable groups of the RPA, DS-GPA, GGS, SIR, and Rades 2015 (AUC=0.70 versus range AUCs=0.51-0.60 respectively). With an AUC of 0.67, the other nomogram predicted 1year survival statistically significantly better (p<0.05) than the favorable groups of four models (range AUCs=0.57-0.61), except for the SIR (AUC=0.64, p=0.34). The models are available on www.predictcancer.org. CONCLUSION The nomograms predicted early death and long-term survival more accurately than commonly used prognostic scores after SRS for a limited number of BMs of NSCLC. Moreover these nomograms enable individualized probability assessment and are easy into use in routine clinical practice.
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Affiliation(s)
- Jaap D Zindler
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Frank J Lagerwaard
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Rosemarijne Beumer
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Esther G C Troost
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands; Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany
| | - Daniëlle B P Eekers
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | | | - Marion Essers
- Department of Radiation Oncology, Verbeeten Institute, Tilburg, The Netherlands
| | - Bing Oei
- Department of Radiation Oncology, Verbeeten Institute, Tilburg, The Netherlands
| | - Coen W Hurkmans
- Department of Radiation Oncology, Catharina Hospital Eindhoven, The Netherlands
| | - Anna M E Bruynzeel
- Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands
| | - Geert Bosmans
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Ans Swinnen
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
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Lambin P, Zindler J, Vanneste BGL, De Voorde LV, Eekers D, Compter I, Panth KM, Peerlings J, Larue RTHM, Deist TM, Jochems A, Lustberg T, van Soest J, de Jong EEC, Even AJG, Reymen B, Rekers N, van Gisbergen M, Roelofs E, Carvalho S, Leijenaar RTH, Zegers CML, Jacobs M, van Timmeren J, Brouwers P, Lal JA, Dubois L, Yaromina A, Van Limbergen EJ, Berbee M, van Elmpt W, Oberije C, Ramaekers B, Dekker A, Boersma LJ, Hoebers F, Smits KM, Berlanga AJ, Walsh S. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 2017; 109:131-153. [PMID: 26774327 DOI: 10.1016/j.addr.2016.01.006] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/08/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022]
Abstract
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
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Affiliation(s)
- Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Jaap Zindler
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ben G L Vanneste
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lien Van De Voorde
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kranthi Marella Panth
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jurgen Peerlings
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ruben T H M Larue
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evelyn E C de Jong
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolle Rekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marike van Gisbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Catharina M L Zegers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maria Jacobs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Janita van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Patricia Brouwers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jonathan A Lal
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ludwig Dubois
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ala Yaromina
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evert Jan Van Limbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maaike Berbee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bram Ramaekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Liesbeth J Boersma
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kim M Smits
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Adriana J Berlanga
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sean Walsh
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Prostate Cancer Radiation Therapy: What Do Clinicians Have to Know? BIOMED RESEARCH INTERNATIONAL 2016; 2016:6829875. [PMID: 28116302 PMCID: PMC5225325 DOI: 10.1155/2016/6829875] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/18/2016] [Accepted: 10/31/2016] [Indexed: 12/11/2022]
Abstract
Radiotherapy (RT) for prostate cancer (PC) has steadily evolved over the last decades, with improving biochemical disease-free survival. Recently population based research also revealed an association between overall survival and doses ≥ 75.6 Gray (Gy) in men with intermediate- and high-risk PC. Examples of improved RT techniques are image-guided RT, intensity-modulated RT, volumetric modulated arc therapy, and stereotactic ablative body RT, which could facilitate further dose escalation. Brachytherapy is an internal form of RT that also developed substantially. New devices such as rectum spacers and balloons have been developed to spare rectal structures. Newer techniques like protons and carbon ions have the intrinsic characteristics maximising the dose on the tumour while minimising the effect on the surrounding healthy tissue, but clinical data are needed for confirmation in randomised phase III trials. Furthermore, it provides an overview of an important discussion issue in PC treatment between urologists and radiation oncologists: the comparison between radical prostatectomy and RT. Current literature reveals that all possible treatment modalities have the same cure rate, but a different toxicity pattern. We recommend proposing the possible different treatment modalities with their own advantages and side-effects to the individual patient. Clinicians and patients should make treatment decisions together (shared decision-making) while using patient decision aids.
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Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma. J Thorac Oncol 2016; 12:624-632. [PMID: 27923715 DOI: 10.1016/j.jtho.2016.11.2230] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2016] [Revised: 11/22/2016] [Accepted: 11/24/2016] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Lung adenocarcinomas (ADCs) with a micropapillary pattern have been reported to have a poor prognosis. However, few studies have reported on the imaging-based identification of a micropapillary component, and all of them have been subjective studies dealing with qualitative computed tomography variables. We aimed to explore imaging phenotyping using a radiomics approach for predicting a micropapillary pattern within lung ADC. METHODS We enrolled 339 patients who underwent complete resection for lung ADC. Histologic subtypes and grades of the ADC were classified. The amount of micropapillary component was determined. Clinical features and conventional imaging variables such as tumor disappearance rate and maximum standardized uptake value on positron emission tomography were assessed. Quantitative computed tomography analysis was performed on the basis of histogram, size and shape, Gray level co-occurrence matrix-based features, and intensity variance and size zone variance-based features. RESULTS Higher tumor stage (OR = 3.270, 95% confidence interval [CI]: 1.483-7.212), intermediate grade (OR = 2.977, 95% CI: 1.066-8.316), lower value of the minimum of the whole pixel value (OR = 0.725, 95% CI: 0.527-0.98800), and lower value of the variance of the positive pixel value (OR = 0.961, 95% CI: 0.927-0.997) were identified as being predictive of a micropapillary component within lung ADC. On the other hand, maximum standardized uptake value and tumor disappearance rate were not significantly different in groups with a micropapillary pattern constituting at least 5% or less than 5% of the entire tumor. CONCLUSION A radiomics approach can be used to interrogate an entire tumor in a noninvasive manner. Combining imaging parameters with clinical features can provide added diagnostic value to identify the presence of a micropapillary component and thus, can influence proper treatment planning.
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Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept. Radiother Oncol 2016; 121:459-467. [DOI: 10.1016/j.radonc.2016.10.002] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 08/25/2016] [Accepted: 10/03/2016] [Indexed: 12/22/2022]
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De Ruysscher D, Defraene G, Ramaekers BLT, Lambin P, Briers E, Stobart H, Ward T, Bentzen SM, Van Staa T, Azria D, Rosenstein B, Kerns S, West C. Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement. Radiother Oncol 2016; 121:440-446. [PMID: 27979370 PMCID: PMC5557371 DOI: 10.1016/j.radonc.2016.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 10/25/2016] [Accepted: 11/01/2016] [Indexed: 12/25/2022]
Abstract
The optimal design and patient selection for interventional trials in radiogenomics seem trivial at first sight. However, radiogenomics do not give binary information like in e.g. targetable mutation biomarkers. Here, the risk to develop severe side effects is continuous, with increasing incidences of side effects with higher doses and/or volumes. In addition, a multi-SNP assay will produce a predicted probability of developing side effects and will require one or more cut-off thresholds for classifying risk into discrete categories. A classical biomarker trial design is therefore not optimal, whereas a risk factor stratification approach is more appropriate. Patient selection is crucial and this should be based on the dose-response relations for a specific endpoint. Alternatives to standard treatment should be available and this should take into account the preferences of patients. This will be discussed in detail.
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Affiliation(s)
- Dirk De Ruysscher
- Maastricht University Medical Center, Department of Radiation Oncology (MAASTRO Clinic), The Netherlands; KU Leuven, Radiation Oncology, Belgium.
| | | | - Bram L T Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, The Netherlands
| | - Philippe Lambin
- Maastricht University Medical Center, Department of Radiation Oncology (MAASTRO Clinic), The Netherlands
| | | | | | - Tim Ward
- Patient Advocate, Manchester, UK
| | | | - Tjeerd Van Staa
- The University of Manchester, Manchester Academic Health Science Centre, UK
| | - David Azria
- Department of Radiation Oncology and Medical Physics, Institut Regional du Cancer Montpellier, France
| | - Barry Rosenstein
- Department of Radiation Oncology and Medical Physics, Institut Regional du Cancer Montpellier, France
| | | | - Catharine West
- The University of Manchester, Translational Radiobiology Group I Institute of Cancer Sciences, The Christie NHS Foundation Trust, UK
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Eekers DB, Roelofs E, Jelen U, Kirk M, Granzier M, Ammazzalorso F, Ahn PH, Janssens GO, Hoebers FJ, Friedmann T, Solberg T, Walsh S, Troost EG, Kaanders JH, Lambin P. Benefit of particle therapy in re-irradiation of head and neck patients. Results of a multicentric in silico ROCOCO trial. Radiother Oncol 2016; 121:387-394. [DOI: 10.1016/j.radonc.2016.08.020] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 08/29/2016] [Accepted: 08/29/2016] [Indexed: 01/21/2023]
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Walsh S, Roelofs E, Kuess P, Lambin P, Jones B, Georg D, Verhaegen F. A validated tumor control probability model based on a meta-analysis of low, intermediate, and high-risk prostate cancer patients treated by photon, proton, or carbon-ion radiotherapy. Med Phys 2016; 43:734-47. [PMID: 26843237 DOI: 10.1118/1.4939260] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate-risk prostate cancer. Extending the TCP model for low and high-risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon-ions are becoming increasingly available. Consequently, there is a need for a complete TCP model. METHODS A TCP model was fitted and validated to a primary endpoint of 5-year biological no evidence of disease clinical outcome data obtained from a review of the literature for low, intermediate, and high-risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon-ions. The review followed the preferred reporting item for systematic reviews and meta-analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied. RESULTS The TCP model achieves a good level of fit overall, linear regression results in a p-value of <0.000 01 with an adjusted-weighted-R(2) value of 0.77 and a weighted root mean squared error (wRMSE) of 1.2%, to the fitted clinical outcome data. Validation of the model utilizing three independent datasets obtained from the literature resulted in an adjusted-weighted-R(2) value of 0.78 and a wRMSE of less than 1.8%, to the validation clinical outcome data. The weighted mean absolute residual across the entire dataset is found to be 5.4%. CONCLUSIONS This TCP model fitted and validated to clinical outcome data, appears to be an appropriate model for the inclusion of all clinical prostate cancer risk categories, and allows evaluation of current EBRT modalities with regard to tumor control prediction.
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Affiliation(s)
- Seán Walsh
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC+), Maastricht 6229 ET, The Netherlands and Department of Oncology, Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC+), Maastricht 6229 ET, The Netherlands
| | - Peter Kuess
- Department of Radiation Oncology and Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna 1090, Austria
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC+), Maastricht 6229 ET, The Netherlands
| | - Bleddyn Jones
- Department of Oncology, Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Dietmar Georg
- Department of Radiation Oncology and Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna 1090, Austria
| | - Frank Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center (MUMC+), Maastricht 6229 ET, The Netherlands and Medical Physics Unit, Department of Oncology, McGill University, Montréal, Québec H4A 3J1, Canada
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