Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology

Abstract Recent papers in the field of radiomics showed strong evidence that novel image biomarkers based on structural tissue properties have the potential to complement and even surpass invasive and costly biopsy-based molecular assays in certain clinical contexts. To date, very few translations of these research results to clinical practive have been carried out. In addition, a majority of the identified imaging biomarkers are perceived as black boxes by end-users, hindering their acceptance in clinical and research environments. We present a suite of plugins called Quantitative Feature Explore (QFExplore) for the open-access cloud-based ePAD platform enabling the exploration and validation of new imaging biomarkers in a clinical environment. The latter include the extraction, visualization and comparison of intensity- and texture-based quantitative imaging features, regional division of regions of interest to reveal tissue diversity, as well as the construction, use and sharing of user-personalized statistical machine learning models. No software installation is required and the platform can be accessed through any web browser. The relevance of the developed tools is demonstrated in the context of various clinical use-cases. The software is available online.

[1]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[2]  Ronald M. Summers,et al.  Texture analysis in radiology: Does the emperor have no clothes? , 2017, Abdominal Radiology.

[3]  Pol Cirujeda,et al.  A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT. , 2016, IEEE transactions on medical imaging.

[4]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[5]  R. Gillies,et al.  Quantitative imaging in cancer evolution and ecology. , 2013, Radiology.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Adrien Depeursinge,et al.  Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT. , 2015, Medical physics.

[8]  Hung-Ming Wang,et al.  Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images , 2014, BioMed research international.

[9]  Fabio A. González,et al.  Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma , 2015, MICCAI.

[10]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[11]  Henning Müller,et al.  Efficient and fully automatic segmentation of the lungs in CT volumes , 2015, VISCERAL Challenge@ISBI.

[12]  Dimitri Van De Ville,et al.  Rotation–Covariant Texture Learning Using Steerable Riesz Wavelets , 2014, IEEE Transactions on Image Processing.

[13]  Michael Unser,et al.  Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification , 2017, IEEE Transactions on Image Processing.

[14]  Graeme P. Penney,et al.  Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI , 2014, IEEE Transactions on Medical Imaging.

[15]  Adrien Depeursinge,et al.  Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT , 2014, IEEE Transactions on Medical Imaging.

[16]  Daniel L. Rubin,et al.  3D Markup of Radiological Images in ePAD, a Web-Based Image Annotation Tool , 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.

[17]  Adrien Depeursinge,et al.  Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles , 2016, Medical Image Anal..

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  D. Rubin,et al.  Automated tracking of quantitative assessments of tumor burden in clinical trials. , 2014, Translational oncology.

[20]  R. Arceci.  Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2012 .

[21]  Pattanasak Mongkolwat,et al.  Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder. , 2012, Radiographics : a review publication of the Radiological Society of North America, Inc.

[22]  Dimitri Van De Ville,et al.  Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities , 2014, Medical Image Anal..

[23]  Dimitri Van De Ville,et al.  Multiscale Lung Texture Signature Learning Using the Riesz Transform , 2012, MICCAI.

[24]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .