Literature DB >> 30350006

Radiomics in RayPlus: a Web-Based Tool for Texture Analysis in Medical Images.

Rong Yuan1, Shuyue Shi2, Junhui Chen1, Guanxun Cheng3.   

Abstract

Radiomics has been shown to have considerable potential and value in quantifying the tumor phenotype and predicting the treatment response. In most scenarios, the commercial and open-source software programs are available for quantitative analysis in medical images to streamline radiomics research. However, at this stage, most of these programs are local applications and require users to have experience in programming and software engineering, which clinicians usually do not have. Therefore, in this article, a web-based tool was proposed to flexibly support radiomics research workflow tasks. Radiomics in RayPlus requires zero installation, is easy to maintain, and accessible anywhere via any PC or MAC with an Internet connection. The system provides functions including multimodality image import and viewing, ROI definition, feature extraction, and data sharing. As a web application, it appears an effective way to multi-institution and multi-department collaborative radiomics research and moreover, its transparency, flexibility, and portability can greatly accelerate the pace of clinical data analysis.

Entities:  

Keywords:  Medical image; Radiomics; Software design; Texture analysis; Web technology

Mesh:

Year:  2019        PMID: 30350006      PMCID: PMC6456636          DOI: 10.1007/s10278-018-0128-1

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  21 in total

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Authors:  Peter Mildenberger; Marco Eichelberg; Eric Martin
Journal:  Eur Radiol       Date:  2001-09-15       Impact factor: 5.315

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Journal:  Comput Methods Programs Biomed       Date:  2008-10-14       Impact factor: 5.428

Review 4.  Current and future trends in imaging informatics for oncology.

Authors:  Mia A Levy; Daniel L Rubin
Journal:  Cancer J       Date:  2011 Jul-Aug       Impact factor: 3.360

5.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

Authors:  Lifei Zhang; David V Fried; Xenia J Fave; Luke A Hunter; Jinzhong Yang; Laurence E Court
Journal:  Med Phys       Date:  2015-03       Impact factor: 4.071

Review 6.  Radiomics: extracting more information from medical images using advanced feature analysis.

Authors:  Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts
Journal:  Eur J Cancer       Date:  2012-01-16       Impact factor: 9.162

7.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

Authors:  E A Eisenhauer; P Therasse; J Bogaerts; L H Schwartz; D Sargent; R Ford; J Dancey; S Arbuck; S Gwyther; M Mooney; L Rubinstein; L Shankar; L Dodd; R Kaplan; D Lacombe; J Verweij
Journal:  Eur J Cancer       Date:  2009-01       Impact factor: 9.162

8.  MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

Authors:  Hui Li; Yitan Zhu; Elizabeth S Burnside; Karen Drukker; Katherine A Hoadley; Cheng Fan; Suzanne D Conzen; Gary J Whitman; Elizabeth J Sutton; Jose M Net; Marie Ganott; Erich Huang; Elizabeth A Morris; Charles M Perou; Yuan Ji; Maryellen L Giger
Journal:  Radiology       Date:  2016-05-05       Impact factor: 11.105

9.  Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images.

Authors:  Yu-Hua Dean Fang; Chien-Yu Lin; Meng-Jung Shih; Hung-Ming Wang; Tsung-Ying Ho; Chun-Ta Liao; Tzu-Chen Yen
Journal:  Biomed Res Int       Date:  2014-03-17       Impact factor: 3.411

Review 10.  Imaging and cancer: a review.

Authors:  Leonard Fass
Journal:  Mol Oncol       Date:  2008-05-10       Impact factor: 7.449

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  6 in total

1.  EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma.

Authors:  Quan Chen; Yan Li; Qiguang Cheng; Juno Van Valkenburgh; Xiaotian Sun; Chuansheng Zheng; Ruiguang Zhang; Rong Yuan
Journal:  Onco Targets Ther       Date:  2022-05-30       Impact factor: 4.345

2.  18F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia.

Authors:  Hebei Li; Chongrui Xu; Bowen Xin; Chaojie Zheng; Yunyun Zhao; Keji Hao; Qian Wang; Richard L Wahl; Xiuying Wang; Yun Zhou
Journal:  Theranostics       Date:  2019-07-09       Impact factor: 11.556

3.  Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson's Disease.

Authors:  Jingwen Li; Xiaoming Liu; Xinyi Wang; Hanshu Liu; Zhicheng Lin; Nian Xiong
Journal:  Brain Sci       Date:  2022-06-29

4.  Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data.

Authors:  Lin Lu; Shawn H Sun; Hao Yang; Linning E; Pingzhen Guo; Lawrence H Schwartz; Binsheng Zhao
Journal:  Tomography       Date:  2020-06

5.  Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT.

Authors:  Lilang Lv; Bowen Xin; Yichao Hao; Ziyi Yang; Junyan Xu; Lisheng Wang; Xiuying Wang; Shaoli Song; Xiaomao Guo
Journal:  J Transl Med       Date:  2022-02-02       Impact factor: 5.531

6.  Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics.

Authors:  Jiaru Li; Ziyi Yang; Bowen Xin; Yichao Hao; Lisheng Wang; Shaoli Song; Junyan Xu; Xiuying Wang
Journal:  Front Oncol       Date:  2021-07-22       Impact factor: 6.244

  6 in total

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