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Abstract
Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, 7984Ryerson University, Toronto, ON, Canada
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2
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Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, Zanzotto FM, Bueno-Crespo A, Pérez-Sánchez H. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int J Mol Sci 2021; 22:4394. [PMID: 33922356 PMCID: PMC8122817 DOI: 10.3390/ijms22094394] [Citation(s) in RCA: 20] [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: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/18/2022] Open
Abstract
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Jorge Peña-García
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Adrian Iftene
- Faculty of Computer Science, Universitatea Alexandru Ioan Cuza (UAIC), 700505 Jashi, Romania;
| | - Fiorella Guadagni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Patrizia Ferroni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Noemi Scarpato
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Fabio Massimo Zanzotto
- Dipartimento di Ingegneria dell’Impresa “Mario Lucertini”, University of Rome Tor Vergata, 00133 Rome, Italy;
| | - Andrés Bueno-Crespo
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
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3
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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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Potter BK, Forsberg JA, Silvius E, Wagner M, Khatri V, Schobel SA, Belard AJ, Weintrob AC, Tribble DR, Elster EA. Combat-Related Invasive Fungal Infections: Development of a Clinically Applicable Clinical Decision Support System for Early Risk Stratification. Mil Med 2019; 184:e235-e242. [PMID: 30124943 DOI: 10.1093/milmed/usy182] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Benjamin K Potter
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Jonathan A Forsberg
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD.,Regenerative Medicine Department, Naval Medical Research Center, 503 Robert Grant Avenue, Silver Spring, MD
| | - Elizabeth Silvius
- Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD.,DecisionQ Corporation, 2500 Wilson Blvd #325, Arlington, VA
| | - Matthew Wagner
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Vivek Khatri
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Seth A Schobel
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Arnaud J Belard
- Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Amy C Weintrob
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD.,Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 6720A Rockledge Drive #100, Bethesda, MD.,Veterans Affairs Medical Center, 50 Irving St NW, Washington, DC
| | - David R Tribble
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD
| | - Eric A Elster
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
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Predictors of one and two years' mortality in patients with colon cancer: A prospective cohort study. PLoS One 2018; 13:e0199894. [PMID: 29953553 PMCID: PMC6023168 DOI: 10.1371/journal.pone.0199894] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 06/15/2018] [Indexed: 12/12/2022] Open
Abstract
Background Tools to aid in the prognosis assessment of colon cancer patients in terms of risk of mortality are needed. Goals of this study are to develop and validate clinical prediction rules for 1- and 2-year mortality in these patients. Methods This is a prospective cohort study of patients diagnosed with colon cancer who underwent surgery at 22 hospitals. The main outcomes were mortality at 1 and 2 years after surgery. Background, clinical parameters, and diagnostic tests findings were evaluated as possible predictors. Multivariable multilevel logistic regression and survival models were used in the analyses to create the clinical prediction rules. Models developed in the derivation sample were validated in another sample of the study. Results American Society of Anesthesiologists Physical Status Classification System (ASA), Charlson comorbidity index (> = 4), age (>75 years), residual tumor (R2), TNM stage IV and log of lymph nodes ratio (> = -0.53) were predictors of 1-year mortality (C-index (95% CI): 0.865 (0.792–0.938)). Adjuvant chemotherapy was an additional predictor. Again ASA, Charlson Index (> = 4), age (>75 years), log of lymph nodes ratio (> = -0.53), TNM, and residual tumor were predictors of 2-year mortality (C-index:0.821 (0.766–0.876). Chemotherapy was also an additional predictor. Conclusions These clinical prediction rules show very good predictive abilities of one and two years survival and provide clinicians and patients with an easy and quick-to-use decision tool for use in the clinical decision process while the patient is still in the index admission.
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Cheng Q, Roelofs E, Ramaekers BLT, Eekers D, van Soest J, Lustberg T, Hendriks T, Hoebers F, van der Laan HP, Korevaar EW, Dekker A, Langendijk JA, Lambin P. Development and evaluation of an online three-level proton vs photon decision support prototype for head and neck cancer - Comparison of dose, toxicity and cost-effectiveness. Radiother Oncol 2016; 118:281-5. [PMID: 26924342 DOI: 10.1016/j.radonc.2015.12.029] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 12/01/2015] [Accepted: 12/05/2015] [Indexed: 12/25/2022]
Abstract
To quantitatively assess the effectiveness of proton therapy for individual patients, we developed a prototype for an online platform for proton decision support (PRODECIS) comparing photon and proton treatments on dose metric, toxicity and cost-effectiveness levels. An evaluation was performed with 23 head and neck cancer datasets.
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Affiliation(s)
- Qing Cheng
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Bram L T Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Tim Hendriks
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Hans Paul van der Laan
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Erik W Korevaar
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands.
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Javan Amoli AH, Maserat E, Safdari R, Zali MR. Electronic Risk Assessment System as an Appropriate Tool for the Prevention of Cancer: a Qualitative Study. Asian Pac J Cancer Prev 2016; 16:8595-8. [PMID: 26745122 DOI: 10.7314/apjcp.2015.16.18.8595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Decision making modalities for screening for many cancer conditions and different stages have become increasingly complex. Computer-based risk assessment systems facilitate scheduling and decision making and support the delivery of cancer screening services. The aim of this article was to survey electronic risk assessment system as an appropriate tool for the prevention of cancer. MATERIALS AND METHODS A qualitative design was used involving 21 face-to-face interviews. Interviewing involved asking questions and getting answers from exclusive managers of cancer screening. Of the participants 6 were female and 15 were male, and ages ranged from 32 to 78 years. The study was based on a grounded theory approach and the tool was a semi- structured interview. RESULTS Researchers studied 5 dimensions, comprising electronic guideline standards of colorectal cancer screening, work flow of clinical and genetic activities, pathways of colorectal cancer screening and functionality of computer based guidelines and barriers. Electronic guideline standards of colorectal cancer screening were described in the s3 categories of content standard, telecommunications and technical standards and nomenclature and classification standards. According to the participations' views, workflow and genetic pathways of colorectal cancer screening were identified. CONCLUSIONS The study demonstrated an effective role of computer-guided consultation for screening management. Electronic based systems facilitate real-time decision making during a clinical interaction. Electronic pathways have been applied for clinical and genetic decision support, workflow management, update recommendation and resource estimates. A suitable technical and clinical infrastructure is an integral part of clinical practice guidline of screening. As a conclusion, it is recommended to consider the necessity of architecture assessment and also integration standards.
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