QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

[1]  John O. Prior,et al.  Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows , 2023, CHI.

[2]  A. Brunetti,et al.  Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges , 2022, Cancers.

[3]  F. Bini,et al.  matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model , 2022, J. Imaging.

[4]  B. Baessler,et al.  AutoRadiomics: A Framework for Reproducible Radiomics Research , 2022, Frontiers in Radiology.

[5]  John O. Prior,et al.  Head and neck tumor segmentation in PET/CT: The HECKTOR challenge , 2021, Medical Image Anal..

[6]  R. Cuocolo,et al.  Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment , 2021, European Radiology.

[7]  S. Schoenberg,et al.  [The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples]. , 2021, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[8]  Jan-Martin Kuhnigk,et al.  The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies – First Application Examples Die Internationale Radiomics-Plattform – eine Initiative der Deutschen und Österreichischen Röntgengesellschaften – Erste Anwendungsbeispiele , 2020, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren.

[9]  Indrin J Chetty,et al.  Technical Note: ROdiomX: A Validated Software for Radiomics Analysis of Medical Images in Radiation Oncology. , 2020, Medical physics.

[10]  Hao Liu,et al.  DARWIN: A Highly Flexible Platform for Imaging Research in Radiology , 2020, ArXiv.

[11]  H. Alkadhi,et al.  Radiomics in medical imaging—“how-to” guide and critical reflection , 2020, Insights into Imaging.

[12]  H. Müller,et al.  Standardised convolutional filtering for radiomics , 2020, ArXiv.

[13]  C. Faivre-Finn,et al.  Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform , 2020, European Radiology.

[14]  S. Park,et al.  A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features , 2020, BMC Cancer.

[15]  John O. Prior,et al.  Pulmonary Lymphangitic Carcinomatosis: Diagnostic Performance of High-Resolution CT and 18F-FDG PET/CT in Correlation with Clinical Pathologic Outcome , 2020, The Journal of Nuclear Medicine.

[16]  Leonard Wee,et al.  Technical Note: Ontology‐guided radiomics analysis workflow (O‐RAW) , 2019, Medical physics.

[17]  Christos Davatzikos,et al.  The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview , 2019, BrainLes@MICCAI.

[18]  Alex Zwanenburg,et al.  Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[19]  Martina Sollini,et al.  Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[20]  S. Ashrafinia.  QUANTITATIVE NUCLEAR MEDICINE IMAGING USING ADVANCED IMAGE RECONSTRUCTION AND RADIOMICS , 2019 .

[21]  Daniel L. Rubin,et al.  ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging , 2019, Tomography.

[22]  Ronald Boellaard,et al.  RaCaT: An open source and easy to use radiomics calculator tool , 2019, PloS one.

[23]  Marco Nolden,et al.  MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Gilmer Valdes,et al.  Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  Daniel L. Rubin,et al.  Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images , 2018, Journal of Digital Imaging.

[26]  Harini Veeraraghavan,et al.  Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research. , 2018, Medical physics.

[27]  Irène Buvat,et al.  LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. , 2018, Cancer research.

[28]  Steffen Löck,et al.  Assessing robustness of radiomic features by image perturbation , 2018, Scientific Reports.

[29]  Chia-Feng Lu,et al.  Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas , 2018, Clinical Cancer Research.

[30]  Jinzhong Yang,et al.  Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. , 2018, Journal of visualized experiments : JoVE.

[31]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[32]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[33]  Chi Zhang,et al.  iVAR: Interactive visual analytics of radiomics features from large-scale medical images , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[34]  Henning Müller,et al.  3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms , 2017, IEEE Transactions on Image Processing.

[35]  Stefan Leger,et al.  Image biomarker standardisation initiative version 1 . 4 , 2016, 1612.07003.

[36]  Jinzhong Yang,et al.  Computational resources for radiomics , 2016 .

[37]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[38]  Mauro Vallati,et al.  Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[39]  Jinzhong Yang,et al.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. , 2015, Medical physics.

[40]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[41]  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.

[42]  Michal Strzelecki,et al.  A software tool for automatic classification and segmentation of 2D/3D medical images , 2013 .

[43]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[44]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[45]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[46]  Sebastian Pölsterl,et al.  scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn , 2020, J. Mach. Learn. Res..

[47]  Spyridon Bakas,et al.  Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , 2017, Lecture Notes in Computer Science.

[48]  Daniel L. Rubin,et al.  Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology , 2017 .

[49]  Roger Schaer,et al.  QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT , 2017 .

[50]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..