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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [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: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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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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Luo Y, Cuneo KC, Lawrence TS, Matuszak MM, Dawson LA, Niraula D, Ten Haken RK, El Naqa I. A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy. Front Oncol 2022; 12:1061024. [PMID: 36568208 PMCID: PMC9782976 DOI: 10.3389/fonc.2022.1061024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background Imbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to more accurate and explainable outcome prediction models. Methods A total of 119 HCC patients with 163 tumors were used in the study. 81 patients with 104 tumors from the University of Michigan Hospital treated with SBRT were considered as a discovery dataset for radiation outcomes model building. The external testing dataset included 59 tumors from 38 patients with SBRT from Princess Margaret Hospital. In the discovery dataset, 100 tumors from 77 patients had local control (LC) (96% of 104 tumors) and 23 patients had at least one grade increment of ALBI (I-ALBI) during six-month follow up (28% of 81 patients). Each patient had a total of 110 features, where 15 or 20 features were identified by physicians as expert knowledge features (EKFs) for LC or I-ALBI prediction. We proposed a HITL based Bayesian network (HITL-BN) approach to enhance the capability of selecting important features from imbalanced data in terms of accuracy and explainability through humans' participation by integrating feature importance ranking and Markov blanket algorithms. A pure data-driven Bayesian network (PD-BN) method was applied to the same discovery dataset of HCC patients as a benchmark. Results In the training and testing phases, the areas under receiver operating characteristic curves of the HITL-BN models for LC or I-ALBI prediction during SBRT are 0.85 (95% confidence interval: 0.75-0.95) or 0.89 (0.81-0.95) and 0.77 or 0.78, respectively. They significantly outperformed the during-treatment PD-BN model in predicting LC or I-ALBI based on the discovery cross-validation and testing datasets from the Delong tests. Conclusion By allowing the human expert to be part of the model building process, the HITL-BN approach yielded significantly improved accuracy as well as better explainability when dealing with imbalanced outcomes in the prediction of post-SBRT treatment response of HCC patients when compared to the PD-BN method.
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Affiliation(s)
- Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States,*Correspondence: Yi Luo,
| | - Kyle C. Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Laura A. Dawson
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
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Jiao F, Guo R, Beckmann JS, Yan Z, Yang Y, Hu J, Wang X, Xie S. Great future or greedy venture: Precision medicine needs philosophy. Health Sci Rep 2021; 4:e376. [PMID: 34541334 PMCID: PMC8439431 DOI: 10.1002/hsr2.376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/06/2021] [Accepted: 08/16/2021] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Over the past decade, we have witnessed the initiation and implementation of precision medicine (PM), a discipline that promises to individualize and personalize medical management and treatment, rendering them ultimately more precise and effective. Despite of the continuing advances and numerous clinical applications, the potential of PM remains highly controversial, sparking heated debates about its future. METHOD The present article reviews the philosophical issues and practical challenges that are critical to the feasibility and implementation of PM. OUTCOME The explanation and argument about the relations between PM and computability, uncertainty as well as complexity, show that key foundational assumptions of PM might not be fully validated. CONCLUSION The present analysis suggests that our current understanding of PM is probably oversimplified and too superficial. More efforts are needed to realize the hope that PM has elicited, rather than make the term just as a hype.
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Affiliation(s)
- Fei Jiao
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Ruoyu Guo
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | | | - Zhonghai Yan
- Department of Medicine, College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Yun Yang
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Jinxia Hu
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
| | - Xin Wang
- Department of Clinical Laboratory & Center of Health Service Training970 Hospital of the PLA Joint Logistic Support ForceYantaiChina
| | - Shuyang Xie
- Department of Biochemistry and Molecular BiologyBinzhou Medical UniversityYantaiChina
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Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
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Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
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Sedlmayr B, Knapp A, Kümmel M, Bathelt F, Sedlmayr M. [Evaluation of a future scenario concerning the use of big data applications to improve the care of people with rare diseases]. ZEITSCHRIFT FUR EVIDENZ FORTBILDUNG UND QUALITAET IM GESUNDHEITSWESEN 2020; 158-159:81-91. [PMID: 33250393 DOI: 10.1016/j.zefq.2020.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 11/28/2022]
Abstract
INTRODUCTION In Germany there are about 4 million people living with a rare disease. Studies have shown that big data applications can improve diagnosis of and research on rare diseases more effectively. However, no concrete comprehensive concept for the use of big data in the care of people with rare diseases has so far been established in Germany. As part of the project "BIDA-SE", which is funded by the German Ministry of Health, a first scenario has been designed to show how big data applications can be usefully incorporated into the care of people with rare diseases. METHODS The aim of the present study was to evaluate this scenario with regard to acceptance, (clinical) benefits, economic aspects, and limitations and barriers to its implementation. To evaluate the scenario, an online survey was conducted in Germany in October/November 2019 amongst a total of N = 9 physicians, N = 69 patients with rare diseases/patient representatives, N = 14 IT experts and N = 21 health care researchers. The online questionnaire consisted of both standardized, validated questions taken from already tested survey instruments and additional questions which were constructed on the basis of a preceding literature analysis. The evaluation of the survey was primarily descriptive, with a calculation of frequencies, mean values and standard deviations. RESULTS The results of the evaluation show that the scenario has been accepted by a majority of all groups surveyed (physicians, patients/patient representatives, IT experts and health care researchers). From the point of view of physicians, patients/patient representatives and health care researchers, the scenario has the potential to accelerate the diagnosis and initiation of therapy and to improve cross-sectoral treatment. From the physician's and health care researcher's perspective, investments in the application presented in the scenario would be profitable. Financing the scenario would, however, require adjusting the reimbursement situation. The limitations and barriers identified by all groups for a medium-term implementation of the scenario can be grouped into seven thematic areas where action is needed: (1) financing and investment, (2) data protection and data security, (3) standards/data sources/data quality, (4) acceptance of technology, (5) integration into the daily work routine, (6) knowledge about availability as well as (7) habits and preferences/physician's role. DISCUSSION With the present study, a first interdisciplinary, practical scenario using big data applications was evaluated with regard to acceptance, benefits and limitations/barriers. The scenario is widely accepted among the majority of all surveyed target groups and is considered (clinically) useful, although legal, organisational and technical barriers still need to be overcome for its medium-term implementation. The evaluation results contribute to the derivation of recommendations for action to ensure the medium-term implementation of the scenario and to channel access to the Centres for Rare Diseases in the future. CONCLUSION Many activities have been initiated at a national level to improve the health care situation of people with rare diseases. The scenario developed in the "BIDA-SE" project complements these research activities and illustrates how big data applications can be usefully implemented into practice to improve the diagnosis and therapy of people with rare diseases in a sustainable way.
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Affiliation(s)
- Brita Sedlmayr
- Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Deutschland; Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland.
| | - Andreas Knapp
- Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Deutschland
| | - Michéle Kümmel
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
| | - Franziska Bathelt
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
| | - Martin Sedlmayr
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
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Cui S, Tseng HH, Pakela J, Ten Haken RK, Naqa IE. Introduction to machine and deep learning for medical physicists. Med Phys 2020; 47:e127-e147. [PMID: 32418339 PMCID: PMC7331753 DOI: 10.1002/mp.14140] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/23/2020] [Accepted: 03/03/2020] [Indexed: 01/01/2023] Open
Abstract
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state-of-the-art tools should be able to solve pressing problems in modern radiation oncology. Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed. Machine learning can be categorized based on the underlying task into supervised learning, unsupervised learning, or reinforcement learning; each of these categories has its own input/output dataset characteristics and aims to solve different classes of problems in medical physics ranging from automation of processes to predictive analytics. It is recognized that data size requirements may vary depending on the specific medical physics application and the nature of the algorithms applied. Data processing, which is a crucial step for model stability and precision, should be performed before training the model. Deep learning as a subset of ML is able to learn multilevel representations from raw input data, eliminating the necessity for hand crafted features in classical ML. It can be thought of as an extension of the classical linear models but with multilayer (deep) structures and nonlinear activation functions. The logic of going "deeper" is related to learning complex data structures and its realization has been aided by recent advancements in parallel computing architectures and the development of more robust optimization methods for efficient training of these algorithms. Model validation is an essential part of ML/DL model building. Without it, the model being developed cannot be easily trusted to generalize to unseen data. Whenever applying ML/DL, one should keep in mind, according to Amara's law, that humans may tend to overestimate the ability of a technology in the short term and underestimate its capability in the long term. To establish ML/DL role into standard clinical workflow, models considering balance between accuracy and interpretability should be developed. Machine learning/DL algorithms have potential in numerous radiation oncology applications, including automatizing mundane procedures, improving efficiency and safety of auto-contouring, treatment planning, quality assurance, motion management, and outcome predictions. Medical physicists have been at the frontiers of technology translation into medicine and they ought to be prepared to embrace the inevitable role of ML/DL in the practice of radiation oncology and lead its clinical implementation.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Julia Pakela
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
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The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report. Eur J Cancer 2020; 131:76-88. [DOI: 10.1016/j.ejca.2020.02.050] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 12/16/2022]
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Pillai M, Adapa K, Das SK, Mazur L, Dooley J, Marks LB, Thompson RF, Chera BS. Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy. J Am Coll Radiol 2019; 16:1267-1272. [DOI: 10.1016/j.jacr.2019.06.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 06/03/2019] [Indexed: 02/06/2023]
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10
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Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research. J Clin Med 2019; 8:jcm8070999. [PMID: 31324026 PMCID: PMC6678083 DOI: 10.3390/jcm8070999] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 07/03/2019] [Accepted: 07/05/2019] [Indexed: 12/31/2022] Open
Abstract
Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
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Gal J, Milano G, Ferrero JM, Saâda-Bouzid E, Viotti J, Chabaud S, Gougis P, Le Tourneau C, Schiappa R, Paquet A, Chamorey E. Optimizing drug development in oncology by clinical trial simulation: Why and how? Brief Bioinform 2019; 19:1203-1217. [PMID: 28575140 DOI: 10.1093/bib/bbx055] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Indexed: 12/11/2022] Open
Abstract
In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.
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Affiliation(s)
- Jocelyn Gal
- Epidemiology and Biostatistics Unit at the Antoine Lacassagne Center, Nice, France
| | | | | | | | | | | | - Paul Gougis
- Pitie´-Salp^etrie`re Hospital in Paris, France
| | | | | | - Agnes Paquet
- Molecular and Cellular Pharmacology Institute of Sophia Antipolis, Valbonne, France
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A big data analytics approach to quality, reliability and risk management. INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2019. [DOI: 10.1108/ijqrm-01-2019-294] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Panayides AS, Pattichis MS, Leandrou S, Pitris C, Constantinidou A, Pattichis CS. Radiogenomics for Precision Medicine With a Big Data Analytics Perspective. IEEE J Biomed Health Inform 2018; 23:2063-2079. [PMID: 30596591 DOI: 10.1109/jbhi.2018.2879381] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
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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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15
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Tseng HH, Luo Y, Ten Haken RK, El Naqa I. The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy. Front Oncol 2018; 8:266. [PMID: 30101124 PMCID: PMC6072876 DOI: 10.3389/fonc.2018.00266] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 06/27/2018] [Indexed: 12/16/2022] Open
Abstract
With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients' clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited.
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Affiliation(s)
- Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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16
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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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17
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18
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Luo Y, McShan D, Ray D, Matuszak M, Jolly S, Lawrence T, Ming Kong F, Ten Haken R, El Naqa I. Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 3:232-241. [PMID: 30854500 DOI: 10.1109/trpms.2018.2832609] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The purpose of this study is to demonstrate that a Bayesian network (BN) approach can explore hierarchical biophysical relationships that influence tumor response and predict tumor local control (LC) in non-small-cell lung cancer (NSCLC) patients before and during radiotherapy from a large-scale dataset. Our BN building approach has two steps. First, relevant biophysical predictors influencing LC before and during the treatment are selected through an extended Markov blanket (eMB) method. From this eMB process, the most robust BN structure for LC prediction was found via a wrapper-based approach. Sixty-eight patients with complete feature information were used to identify a full BN model for LC prediction before and during the treatment. Fifty more recent patients with some missing information were reserved for independent testing of the developed pre- and during-therapy BNs. A nested cross-validation (N-CV) was developed to evaluate the performance of the two-step BN approach. An ensemble BN model is generated from the N-CV sampling process to assess its similarity with the corresponding full BN model, and thus evaluate the sensitivity of our BN approach. Our results show that the proposed BN development approach is a stable and robust approach to identify hierarchical relationships among biophysical features for LC prediction. Furthermore, BN predictions can be improved by incorporating during treatment information.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA,
| | - Daniel McShan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Dipankar Ray
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Theodore Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Feng Ming Kong
- Department of Radiation Oncology, Indiana University, Indianapolis, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
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19
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Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 2018; 114:57-65. [PMID: 29673604 DOI: 10.1016/j.ijmedinf.2018.03.013] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/23/2018] [Indexed: 01/02/2023]
Abstract
BACKGROUND The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. STUDY SELECTION Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. DATA EXTRACTION Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. RESULTS A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. CONCLUSION This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
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Affiliation(s)
| | - Anil Pandit
- Symbiosis Institute of Health Sciences, Pune, India
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20
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Zomnir MG, Lipkin L, Pacula M, Dominguez Meneses E, MacLeay A, Duraisamy S, Nadhamuni N, Al Turki SH, Zheng Z, Rivera M, Nardi V, Dias-Santagata D, Iafrate AJ, Le LP, Lennerz JK. Artificial Intelligence Approach for Variant Reporting. JCO Clin Cancer Inform 2018; 2:CCI.16.00079. [PMID: 30364844 PMCID: PMC6198661 DOI: 10.1200/cci.16.00079] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Purpose Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.
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Affiliation(s)
| | - Lev Lipkin
- All authors: Massachusetts General Hospital, Boston, MA
| | - Maciej Pacula
- All authors: Massachusetts General Hospital, Boston, MA
| | | | | | | | | | | | - Zongli Zheng
- All authors: Massachusetts General Hospital, Boston, MA
| | - Miguel Rivera
- All authors: Massachusetts General Hospital, Boston, MA
| | | | | | | | - Long P. Le
- All authors: Massachusetts General Hospital, Boston, MA
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21
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Arnold D, Fuchs CS, Tabernero J, Ohtsu A, Zhu AX, Garon EB, Mackey JR, Paz-Ares L, Baron AD, Okusaka T, Yoshino T, Yoon HH, Das M, Ferry D, Zhang Y, Lin Y, Binder P, Sashegyi A, Chau I. Meta-analysis of individual patient safety data from six randomized, placebo-controlled trials with the antiangiogenic VEGFR2-binding monoclonal antibody ramucirumab. Ann Oncol 2017; 28:2932-2942. [PMID: 28950290 PMCID: PMC5834052 DOI: 10.1093/annonc/mdx514] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Ramucirumab, the human immunoglobulin G1 monoclonal antibody receptor antagonist of vascular endothelial growth factor receptor 2, has been approved for treating gastric/gastroesophageal junction, non-small-cell lung, and metastatic colorectal cancers. With the completion of six global, randomized, double-blind, placebo-controlled, phase III trials across multiple tumor types, an opportunity now exists to further establish the safety parameters of ramucirumab across a large patient population. MATERIALS AND METHODS An individual patient meta-analysis across the six completed phase III trials was conducted and the relative risk (RR) and associated 95% confidence intervals (CIs) were derived using fixed-effects or mixed-effects models for all-grade and high-grade adverse events (AEs) possibly related to vascular endothelial growth factor pathway inhibition. The number needed to harm was also calculable due to the placebo-controlled nature of all six registration standard trials. RESULTS A total of 4996 treated patients (N = 2748 in the ramucirumab arm and N = 2248 in the control, placebo arm) were included in this meta-analysis. Arterial thromboembolic events [ATE; all-grade, RR: 0.8, 95% CI 0.5-1.3; high-grade (grade ≥3), RR: 0.9, 95% CI 0.5-1.7], venous thromboembolic events (VTE; all-grade, RR: 0.7, 95% CI 0.5-1.1; high-grade, RR: 0.7, 95% CI 0.4-1.2), high-grade bleeding (RR: 1.1, 95% CI 0.8-1.5), and high-grade gastrointestinal (GI) bleeding (RR: 1.1, 95% CI 0.7-1.7) did not demonstrate a definite increased risk with ramucirumab. A higher percentage of hypertension, proteinuria, low-grade (grade 1-2) bleeding, GI perforation, infusion-related reaction, and wound-healing complications were observed in the ramucirumab arm compared with the control arm. CONCLUSIONS Ramucirumab may be distinct among antiangiogenic agents in terms of ATE, VTE, high-grade bleeding, or high-grade GI bleeding by showing no clear evidence for an increased risk of these AEs in this meta-analysis of a large and diverse patient population. Ramucirumab is consistent with other angiogenic inhibitors in the risk of developing certain AEs. Clinical Trial Numbers: NCT00917384 (REGARD), NCT01170663 (RAINBOW), NCT01168973 (REVEL), NCT01183780 (RAISE), NCT01140347 (REACH), and NCT00703326 (ROSE).
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MESH Headings
- Angiogenesis Inhibitors/adverse effects
- Angiogenesis Inhibitors/immunology
- Angiogenesis Inhibitors/therapeutic use
- Antibodies, Monoclonal/administration & dosage
- Antibodies, Monoclonal/adverse effects
- Antibodies, Monoclonal/immunology
- Antibodies, Monoclonal/therapeutic use
- Antibodies, Monoclonal, Humanized
- Antineoplastic Agents, Immunological/adverse effects
- Antineoplastic Agents, Immunological/immunology
- Antineoplastic Agents, Immunological/therapeutic use
- Antineoplastic Combined Chemotherapy Protocols/adverse effects
- Antineoplastic Combined Chemotherapy Protocols/immunology
- Antineoplastic Combined Chemotherapy Protocols/therapeutic use
- Clinical Trials, Phase III as Topic
- Humans
- Randomized Controlled Trials as Topic
- Risk Assessment
- Vascular Endothelial Growth Factor Receptor-2/immunology
- Ramucirumab
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Affiliation(s)
- D Arnold
- Oncology, Instituto CUF de Oncologia (I.C.O.), Lisbon, Portugal
| | - C S Fuchs
- Internal Medicine, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - J Tabernero
- Medical Oncology Department, Vall d’Hebron University Hospital and Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A Ohtsu
- Department of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - A X Zhu
- Department of Medicine, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, USA
| | - E B Garon
- Hematology Oncology, David Geffen School of Medicine at UCLA Translational Research in Oncology-US Network, Santa Monica, USA
| | - J R Mackey
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Canada
| | - L Paz-Ares
- Medical Oncology Department, Hospital Universitario Doce de Octubre, Madrid, Spain
| | - A D Baron
- Division of Hematology Oncology, California Pacific Medical Center, San Francisco, USA
| | - T Okusaka
- Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - T Yoshino
- Department of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - H H Yoon
- Division of Medical Oncology, Mayo Clinic, Rochester, USA
| | - M Das
- Oncology, Eli Lilly and Company, Indianapolis, USA
| | - D Ferry
- Oncology, Eli Lilly and Company, Bridgewater, USA
| | - Y Zhang
- Oncology, Eli Lilly and Company, Bridgewater, USA
| | - Y Lin
- Oncology, Eli Lilly and Company, Indianapolis, USA
| | - P Binder
- Oncology, Eli Lilly and Company, Bridgewater, USA
| | - A Sashegyi
- Oncology, Eli Lilly and Company, Indianapolis, USA
| | - I Chau
- Department of Medicine, Royal Marsden Hospital, Sutton, UK
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22
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Zoppolo F, Porcal W, Oliver P, Savio E, Engler H. Automated One-pot Radiosynthesis of [11C]S-adenosyl Methionine. Curr Radiopharm 2017; 10:203-211. [PMID: 28721805 PMCID: PMC5740492 DOI: 10.2174/1874471010666170718171441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 07/03/2017] [Accepted: 07/11/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Glycine N-methyltransferase is an enzyme overexpressed in some neoplastic tissues. It catalyses the methylation of glycine using S-adenosyl methionine (SAM or AdoMet) as substrate. SAM is involved in a great variety of biochemical processes, including transmethylation reactions. Thus, [11C]SAM could be used to evaluate transmethylation activity in tumours. The only method reported for [11C]SAM synthesis is an enzymatic process with several limitations. We propose a new chemical method to obtain [11C]SAM, through a one-pot synthesis. METHOD The optimization of [11C]SAM synthesis was carried out in the automated TRACERlab® FX C Pro module. Different labelling conditions were performed varying methylating agent, precursor amount, temperature and reaction time. The compound was purified using a semipreparative HPLC. Radiochemical stability, lipophilicity and plasma protein binding were evaluated. RESULTS The optimum labelling conditions were [11C]CH3OTf as the methylating agent, 5 mg of precursor dissolved in formic acid at 60 °C for 1 minute. [11C]SAM was obtained as a diastereomeric mixture. Three batches were produced and quality control was performed according to specifications. [11C]SAM was stable in final formulation and in plasma. Log POCT obtained for [11C]SAM was (-2,01 ± 0,07) (n=4), and its value for plasma protein binding was low. CONCLUSION A new chemical method to produce [11C]SAM was optimized. The radiotracer was obtained as a diastereomeric mixture with a 53:47 [(R,S)-isomer: (S,S)-isomer] ratio. The compound was within the quality control specifications. In vitro stability was verified. This compound is suitable to perform preclinical and clinical evaluations.
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Affiliation(s)
| | - Williams Porcal
- Uruguayan Centre of Molecular Imaging (CUDIM), Montevideo, Uruguay.,Facultad de Quimica, Universidad de la Republica (UdelaR), Montevideo, Uruguay
| | - Patricia Oliver
- Uruguayan Centre of Molecular Imaging (CUDIM), Montevideo, Uruguay
| | - Eduardo Savio
- Uruguayan Centre of Molecular Imaging (CUDIM), Montevideo, Uruguay.,Facultad de Quimica, Universidad de la Republica (UdelaR), Montevideo, Uruguay
| | - Henry Engler
- Uruguayan Centre of Molecular Imaging (CUDIM), Montevideo, Uruguay
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El Naqa I, Kerns SL, Coates J, Luo Y, Speers C, West CML, Rosenstein BS, Ten Haken RK. Radiogenomics and radiotherapy response modeling. Phys Med Biol 2017; 62:R179-R206. [PMID: 28657906 PMCID: PMC5557376 DOI: 10.1088/1361-6560/aa7c55] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
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