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转化放射组学:定义策略流程和应用考虑因素-第 2 部分:从临床实施到企业。

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

机构信息

Institute of Computational Health Sciences, UCSF, San Francisco, California.

Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California.

出版信息

J Am Coll Radiol. 2018 Mar;15(3 Pt B):543-549. doi: 10.1016/j.jacr.2017.12.006. Epub 2018 Feb 1.


DOI:10.1016/j.jacr.2017.12.006
PMID:29366598
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7440361/
Abstract

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

摘要

企业影像已经将各种技术创新引入临床放射学领域,包括先进的成像设备和后获取迭代重建工具,以及图像分析和计算机辅助检测工具。最近,定量图像分析领域的进步,加上基于机器学习的数据分析、分类和集成,迎来了放射组学的时代,这一范式转变在临床决策支持以及药物发现方面具有巨大的潜力。然而,要将放射组学纳入临床应用系统和商业可行的解决方案,还需要考虑一些重要问题。在这两部分系列中,我们从方法学到临床实施(第 1 部分)和从临床实施到企业发展(第 2 部分),深入探讨了放射组学转化管道的开发。在这两部分系列的第 2 部分中,我们研究了策略管道的组成部分,从临床实施到构建企业解决方案。

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本文引用的文献

[1]
A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

Tomography. 2016-12

[2]
Collaborative Paradigm of Preventive, Personalized, and Precision Medicine With Point-of-Care Technologies.

IEEE J Transl Eng Health Med. 2016-12-9

[3]
Identification of Histological Correlates of Overall Survival in Lower Grade Gliomas Using a Bag-of-words Paradigm: A Preliminary Analysis Based on Hematoxylin & Eosin Stained Slides from the Lower Grade Glioma Cohort of The Cancer Genome Atlas.

J Pathol Inform. 2017-3-10

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Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques.

J Thorac Imaging. 2018-1

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"What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine.

Value Health. 2017-1

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MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine.

Sci Rep. 2016-11-30

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Stability of radiomic features in CT perfusion maps.

Phys Med Biol. 2016-12-21

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Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.

Radiat Oncol. 2016-11-10

[9]
[18F]FDG PET/CT-based response assessment of stage IV non-small cell lung cancer treated with paclitaxel-carboplatin-bevacizumab with or without nitroglycerin patches.

Eur J Nucl Med Mol Imaging. 2017-1

[10]
Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result.

Nucl Med Mol Imaging. 2014-12

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