Using Big Data Analytics to Advance Precision Radiation Oncology.

Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients. The conceptual model for each use of clinical data, however, is different, and an overview of the implications is discussed. With advancements in technologies and culture to improve the efficiency, accuracy, and breadth of measurements of the patient condition, the concept of an LHS may be realized in precision radiation therapy.

[1]  Todd R McNutt,et al.  How Will Big Data Impact Clinical Decision Making and Precision Medicine in Radiation Therapy? , 2016, International journal of radiation oncology, biology, physics.

[2]  Patricio Simari,et al.  Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: a prospective clinical study. , 2012, International journal of radiation oncology, biology, physics.

[3]  J. Wong,et al.  Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy , 2017, Advances in radiation oncology.

[4]  Issam El-Naqa,et al.  Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..

[5]  Wuyang Yang,et al.  A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients. , 2015, Medical physics.

[6]  R. Carlson,et al.  Implementation Framework for NCCN Guidelines. , 2017, Journal of the National Comprehensive Cancer Network : JNCCN.

[7]  T. McNutt,et al.  Radiation dose to the floor of mouth muscles predicts swallowing complications following chemoradiation in oropharyngeal squamous cell carcinoma. , 2014, Oral oncology.

[8]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .

[9]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[10]  T. Bortfeld,et al.  The Use of Computers in Radiation Therapy , 2000, Springer Berlin Heidelberg.

[11]  Todd McNutt,et al.  A quality control model that uses PTV-rectal distances to predict the lowest achievable rectum dose, improves IMRT planning for patients with prostate cancer. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  Wei Luo,et al.  A framework for feature extraction from hospital medical data with applications in risk prediction , 2014, BMC Bioinformatics.

[13]  R Bellazzi,et al.  Big Data and Biomedical Informatics: A Challenging Opportunity , 2014, Yearbook of Medical Informatics.

[14]  Joseph O Deasy,et al.  Radiotherapy dose-volume effects on salivary gland function. , 2010, International journal of radiation oncology, biology, physics.

[15]  Todd McNutt,et al.  Radio‐morphology: Parametric shape‐based features in radiotherapy , 2019, Medical physics.

[16]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[17]  Steven F Petit,et al.  Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[18]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[19]  Nan Li,et al.  Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials. , 2017, International journal of radiation oncology, biology, physics.

[20]  Russell H. Taylor,et al.  Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning. , 2011, International journal of radiation oncology, biology, physics.

[21]  Russell H. Taylor,et al.  Patient geometry-driven information retrieval for IMRT treatment plan quality control. , 2009, Medical physics.

[22]  Todd R McNutt,et al.  Needs and Challenges for Big Data in Radiation Oncology. , 2016, International journal of radiation oncology, biology, physics.

[23]  K. Pienta,et al.  Prediction of the Pathologic Gleason Score to Inform a Personalized Management Program for Prostate Cancer. , 2017, European urology.

[24]  P. Lambin,et al.  Predicting outcomes in radiation oncology—multifactorial decision support systems , 2013, Nature Reviews Clinical Oncology.

[25]  Ilya Shpitser,et al.  Semi-Parametric Causal Sufficient Dimension Reduction Of High Dimensional Treatments , 2017 .

[26]  Delia Ciardo,et al.  Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients , 2017, Scientific Reports.

[27]  Cristián Zegers Ariztía,et al.  Manual , 2002 .

[28]  Brendan Delaney,et al.  The science of Learning Health Systems: Foundations for a new journal , 2016, Learning health systems.

[29]  Russell H. Taylor,et al.  Creating a Data Science Platform for Developing Complication Risk Models for Personalized Treatment Planning in Radiation Oncology , 2015, 2015 48th Hawaii International Conference on System Sciences.

[30]  C. Compton,et al.  AJCC Cancer Staging Manual , 2002, Springer New York.

[31]  Catharine M West,et al.  Genetics and genomics of radiotherapy toxicity: towards prediction , 2011, Genome Medicine.

[32]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[33]  David Lewis,et al.  Causal decision theory , 1981 .

[34]  Sasa Mutic,et al.  Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126. , 2015, International journal of radiation oncology, biology, physics.

[35]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[36]  Limitations in using dose-volume histograms for radiotherapy dose optimization , 2000 .

[37]  Sasa Mutic,et al.  Predicting dose-volume histograms for organs-at-risk in IMRT planning. , 2012, Medical physics.

[38]  F. Zappa,et al.  The Real Frank Zappa Book , 1989 .