Literature DB >> 30596591

Radiogenomics for Precision Medicine With a Big Data Analytics Perspective.

Andreas S Panayides, Marios S Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S Pattichis.   

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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Year:  2018        PMID: 30596591     DOI: 10.1109/JBHI.2018.2879381

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  6 in total

Review 1.  Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?

Authors:  Roland Hustinx
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-07       Impact factor: 9.236

Review 2.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Authors:  Sanjay Saxena; Biswajit Jena; Neha Gupta; Suchismita Das; Deepaneeta Sarmah; Pallab Bhattacharya; Tanmay Nath; Sudip Paul; Mostafa M Fouda; Manudeep Kalra; Luca Saba; Gyan Pareek; Jasjit S Suri
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

3.  MRI radiomic features-based machine learning approach to classify ischemic stroke onset time.

Authors:  Yi-Qun Zhang; Ao-Fei Liu; Feng-Yuan Man; Ying-Ying Zhang; Chen Li; Yun-E Liu; Ji Zhou; Ai-Ping Zhang; Yang-Dong Zhang; Jin Lv; Wei-Jian Jiang
Journal:  J Neurol       Date:  2021-07-04       Impact factor: 4.849

4.  High resolution MRI-based radiomic nomogram in predicting perineural invasion in rectal cancer.

Authors:  Yan-Song Yang; Yong-Juan Qiu; Gui-Hua Zheng; Hai-Peng Gong; Ya-Qiong Ge; Yi-Fei Zhang; Feng Feng; Yue-Tao Wang
Journal:  Cancer Imaging       Date:  2021-05-26       Impact factor: 3.909

5.  Radiogenomics predicts the expression of microRNA-1246 in the serum of esophageal cancer patients.

Authors:  Isamu Hoshino; Hajime Yokota; Fumitaka Ishige; Yosuke Iwatate; Nobuyoshi Takeshita; Hiroki Nagase; Takashi Uno; Hisahiro Matsubara
Journal:  Sci Rep       Date:  2020-02-13       Impact factor: 4.379

6.  Popular deep learning algorithms for disease prediction: a review.

Authors:  Zengchen Yu; Ke Wang; Zhibo Wan; Shuxuan Xie; Zhihan Lv
Journal:  Cluster Comput       Date:  2022-09-13       Impact factor: 2.303

  6 in total

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