Skip to main content
Top
Published in: Journal of Digital Imaging 2/2019

01-04-2019

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

Authors: Rong Yuan, Shuyue Shi, Junhui Chen, Guanxun Cheng

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2019

Login to get access

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.
Literature
2.
go back to reference Kurland BF, Gerstner ER, Mountz JM, Schwartz LH, Ryan CW, Graham MM, Buatti JM, Fennessy FM, Eikman EA, Kumar V, Forster KM, Wahl RL, Lieberman FS: Promise and pitfalls of quantitative imaging in oncology clinical trials. Magnetic Resonance Imaging 30(9):1301–1312, 2012CrossRefPubMedPubMedCentral Kurland BF, Gerstner ER, Mountz JM, Schwartz LH, Ryan CW, Graham MM, Buatti JM, Fennessy FM, Eikman EA, Kumar V, Forster KM, Wahl RL, Lieberman FS: Promise and pitfalls of quantitative imaging in oncology clinical trials. Magnetic Resonance Imaging 30(9):1301–1312, 2012CrossRefPubMedPubMedCentral
3.
go back to reference Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). European Journal of Cancer 45(2):228–247, 2009CrossRefPubMed Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). European Journal of Cancer 45(2):228–247, 2009CrossRefPubMed
4.
go back to reference Levy MA, Rubin DL: Current and future trends in imaging informatics for oncology. Cancer Journal (Sudbury, Mass.) 17(4):203, 2011CrossRef Levy MA, Rubin DL: Current and future trends in imaging informatics for oncology. Cancer Journal (Sudbury, Mass.) 17(4):203, 2011CrossRef
5.
go back to reference Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJWL: Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer 48(4):441–446, 2012CrossRefPubMedPubMedCentral Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJWL: Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer 48(4):441–446, 2012CrossRefPubMedPubMedCentral
6.
go back to reference Hugo JWLA, Emmanuel RV, Ralph L et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. 5:4006, 2014CrossRef Hugo JWLA, Emmanuel RV, Ralph L et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. 5:4006, 2014CrossRef
7.
go back to reference Yanqi H, Changhong L, He L et al.: Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. Journal of Clinical Oncology. 34(18):2157–2164, 2016CrossRef Yanqi H, Changhong L, He L et al.: Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. Journal of Clinical Oncology. 34(18):2157–2164, 2016CrossRef
8.
go back to reference Hui L, Yitan Z, Elizabeth SB et al.: 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. Radiology. 281(2):382–391, 2016CrossRef Hui L, Yitan Z, Elizabeth SB et al.: 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. Radiology. 281(2):382–391, 2016CrossRef
9.
go back to reference Ke N, Liming S, Qin C et al.: Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clinical Cancer Research. 22(21):5256–5264, 2016CrossRef Ke N, Liming S, Qin C et al.: Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clinical Cancer Research. 22(21):5256–5264, 2016CrossRef
10.
go back to reference Jacob A, Satish V, Mirabela R et al.: Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Translational Oncology. 9(2):155–162, 2016CrossRef Jacob A, Satish V, Mirabela R et al.: Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Translational Oncology. 9(2):155–162, 2016CrossRef
11.
go back to reference Szczypinski PM, Strzelecki M, Materka A et al.: MaZda—a software package for image texture analysis. Computer Methods and Programs in Biomedicine. 94(1):66–76, 2009CrossRefPubMed Szczypinski PM, Strzelecki M, Materka A et al.: MaZda—a software package for image texture analysis. Computer Methods and Programs in Biomedicine. 94(1):66–76, 2009CrossRefPubMed
12.
go back to reference Fang YHD, Lin CY, Shih MJ et al.: Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. BioMed Research International.:248505, 2014 Fang YHD, Lin CY, Shih MJ et al.: Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. BioMed Research International.:248505, 2014
13.
go back to reference Deasy JO, Blanco AI, Clark VH: CERR: a computational environment for radiotherapy research. Medical Physics. 30(5):979–985, 2003CrossRefPubMed Deasy JO, Blanco AI, Clark VH: CERR: a computational environment for radiotherapy research. Medical Physics. 30(5):979–985, 2003CrossRefPubMed
14.
go back to reference Apte A, Wang Y, Deasy J: SU-E-I-66: Radiomics and Image Registration Updates for the Computational Environment for Radiotherapy Research (CERR). Medical Physics. 41:145–145, 2014CrossRef Apte A, Wang Y, Deasy J: SU-E-I-66: Radiomics and Image Registration Updates for the Computational Environment for Radiotherapy Research (CERR). Medical Physics. 41:145–145, 2014CrossRef
15.
go back to reference Apte A, Veeraraghavan H, Oh J, Kijewski P, Deasy J: SU-E-J-253: The Radiomics Toolbox in the Computational Environment for Radiological Research (CERR). Medical Physics. 42:3324–3324, 2015CrossRef Apte A, Veeraraghavan H, Oh J, Kijewski P, Deasy J: SU-E-J-253: The Radiomics Toolbox in the Computational Environment for Radiological Research (CERR). Medical Physics. 42:3324–3324, 2015CrossRef
17.
go back to reference Pieper S., Halle M., Kikinis R. 3D Slicer. Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on IEEE. 632–635, 2004 Pieper S., Halle M., Kikinis R. 3D Slicer. Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on IEEE. 632–635, 2004
18.
go back to reference Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE: IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Medical Physics. 42(3):1341–1353, 2015CrossRefPubMedPubMedCentral Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE: IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Medical Physics. 42(3):1341–1353, 2015CrossRefPubMedPubMedCentral
19.
go back to reference Fave X, Mackin D, Lee J et al.: Computational resources for radiomics. Translational Cancer Research. 5(4):340–348, 2016CrossRef Fave X, Mackin D, Lee J et al.: Computational resources for radiomics. Translational Cancer Research. 5(4):340–348, 2016CrossRef
20.
go back to reference Yuan R, Luo M, Sun Z, Shi S, Xiao P, Xie Q: RayPlus: a Web-Based Platform for Medical Image Processing. Journal of Digital Imaging. 30(2):197–203, 2017CrossRefPubMed Yuan R, Luo M, Sun Z, Shi S, Xiao P, Xie Q: RayPlus: a Web-Based Platform for Medical Image Processing. Journal of Digital Imaging. 30(2):197–203, 2017CrossRefPubMed
21.
go back to reference Mildenberger P, Eichelberg M, Martin E: Introduction to the DICOM standard. European Radiology. 12(4):920–927, 2002CrossRefPubMed Mildenberger P, Eichelberg M, Martin E: Introduction to the DICOM standard. European Radiology. 12(4):920–927, 2002CrossRefPubMed
22.
go back to reference Kumar V, Gu Y, Basu S et al.: Radiomics: the process and the challenges. Magnetic resonance imaging. 30(9):1234–1248, 2013CrossRef Kumar V, Gu Y, Basu S et al.: Radiomics: the process and the challenges. Magnetic resonance imaging. 30(9):1234–1248, 2013CrossRef
23.
go back to reference Danielsson PE: Euclidean distance mapping. Computer Graphics and Image Processing. 14(3):227–248, 1980CrossRef Danielsson PE: Euclidean distance mapping. Computer Graphics and Image Processing. 14(3):227–248, 1980CrossRef
24.
go back to reference Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 9(1): 62–66, 197S Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 9(1): 62–66, 197S
25.
go back to reference Huertas A, Medioni G: Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-8(5):651–664, 1986CrossRef Huertas A, Medioni G: Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-8(5):651–664, 1986CrossRef
26.
go back to reference Xu Y, Weaver JB, Healy DM, Lu J: Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Transactions on Image Processing. 3(6):747–758, 1994CrossRefPubMed Xu Y, Weaver JB, Healy DM, Lu J: Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Transactions on Image Processing. 3(6):747–758, 1994CrossRefPubMed
27.
go back to reference Weldon TP, Higgins WE, Dunn DF: Efficient Gabor filter design for texture segmentation. Pattern Recognition. 29(12):2005–2015, 1996CrossRef Weldon TP, Higgins WE, Dunn DF: Efficient Gabor filter design for texture segmentation. Pattern Recognition. 29(12):2005–2015, 1996CrossRef
28.
go back to reference Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA: Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 10(1):137–143, 2010CrossRefPubMedPubMedCentral Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA: Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 10(1):137–143, 2010CrossRefPubMedPubMedCentral
29.
go back to reference Haralick RM, Shanmugam K: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC-3(6):610–621, 1973CrossRef Haralick RM, Shanmugam K: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC-3(6):610–621, 1973CrossRef
30.
go back to reference Thibault G, Fertil B, Navarro C et al.: Shape and texture indexes application to cell nuclei classification. International Journal of Pattern Recognition and Artificial Intelligence. 27(01):1357002, 2013CrossRef Thibault G, Fertil B, Navarro C et al.: Shape and texture indexes application to cell nuclei classification. International Journal of Pattern Recognition and Artificial Intelligence. 27(01):1357002, 2013CrossRef
31.
go back to reference Galloway MM: Texture classification using gray level run length. Computer Graphic Image Process 4(2):172–179, 1975CrossRef Galloway MM: Texture classification using gray level run length. Computer Graphic Image Process 4(2):172–179, 1975CrossRef
32.
go back to reference Amadasun M, King R: Textural features corresponding to textural properties. IEEE Transactions on Systems, Man, and Cybernetics. 19(5):1264–1274, 1989CrossRef Amadasun M, King R: Textural features corresponding to textural properties. IEEE Transactions on Systems, Man, and Cybernetics. 19(5):1264–1274, 1989CrossRef
33.
go back to reference Tang X: Texture information in run-length matrices. IEEE Transactions on Image Processing. 7(11):1602–1609, 1998CrossRefPubMed Tang X: Texture information in run-length matrices. IEEE Transactions on Image Processing. 7(11):1602–1609, 1998CrossRefPubMed
Metadata
Title
Radiomics in RayPlus: a Web-Based Tool for Texture Analysis in Medical Images
Authors
Rong Yuan
Shuyue Shi
Junhui Chen
Guanxun Cheng
Publication date
01-04-2019
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 2/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-018-0128-1

Other articles of this Issue 2/2019

Journal of Digital Imaging 2/2019 Go to the issue