A guide to outcome modeling in radiotherapy and oncology : listening to the data
著者
書誌事項
A guide to outcome modeling in radiotherapy and oncology : listening to the data
(Series in medical physics and biomedical engineering / editors: C.G. Orton, J.A.E. Spaan, J.G. Webster)
CRC Press, 2018
- : hardback
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 301-364) and index
内容説明・目次
内容説明
This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches.
This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications.
Features:
Covers top-down approaches applying statistical, machine learning, and big data analytics and bottom-up approaches using first principles and multi-scale techniques, including numerical simulations based on Monte Carlo and automata techniques
Provides an overview of the available software tools and resources for outcome model development and evaluation, and includes hands-on detailed examples throughout
Presents a diverse selection of the common applications of outcome modeling in a wide variety of areas: treatment planning in radiotherapy, chemotherapy and immunotherapy, utility-based and biomarker applications, particle therapy modeling, oncological surgery, and the design of adaptive and SMART clinical trials
目次
Section I: Multiple sources of data. Chapter 1: Introduction to data sources and outcome models. Chapter 2: Cinical data in outcome models. Chapter 3: Imaging data: Radiomics. Chapter 4: Dosimetric data. Chapter 5: Pre-Clinical Radiobiological insights to inform modelling of radiotherapy outcome. Chapter 6: Biological data: The use of omics in outcome models. Section II: Top-down Modeling Approaches. Chapter 7: Analytical and mechanistic modeling. Chapter 8: Data driven approaches I: using conventional statistical inference methods, including linear and logistic regression. Chapter 9: Data driven approaches II: Machine Learning. Section III: Bottom-up Modeling Approaches. Chapter 10: Stochastic multiscale modelling of biological effects induced by ionizing radiation. Chapter 11: Multiscale modeling approaches: Application in Chemo and immunotherapies. Section IV: Example Applications in Oncology. Chapter 12: Outcome Modeling in Treatment Planning. Chapter 13: A Utility Based Approach to Individualized and Adaptive Radiation Therapy. Chapter 14: Outcome modeling in Particle therapy. Chapter 15: Modeling response to oncological surgery. Chapter 16: Tools for the precision medicine era: Developing highly adaptive and personalized treatment recommendations using SMARTs.
「Nielsen BookData」 より