Journal ArticleDOI
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.
TLDR
Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value, and in multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates.Abstract:
This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.read more
Citations
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Journal ArticleDOI
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
Spyridon Bakas,Hamed Akbari,Aristeidis Sotiras,Michel Bilello,Martin Rozycki,Justin Kirby,John Freymann,Keyvan Farahani,Christos Davatzikos +8 more
TL;DR: This set of labels and features should enable direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as performance evaluation of computer-aided segmentation methods.
Journal ArticleDOI
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg,Alex Zwanenburg,Martin Vallières,Mahmoud A. Abdalah,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Vincent Andrearczyk,Aditya Apte,Saeed Ashrafinia,Spyridon Bakas,Roelof J. Beukinga,Ronald Boellaard,Marta Bogowicz,Luca Boldrini,Irène Buvat,Gary Cook,Christos Davatzikos,Adrien Depeursinge,Marie-Charlotte Desseroit,Nicola Dinapoli,Cuong V. Dinh,Sebastian Echegaray,Issam El Naqa,Issam El Naqa,Andriy Fedorov,Roberto Gatta,Robert J. Gillies,Vicky Goh,Michael Götz,Matthias Guckenberger,Sung Min Ha,Mathieu Hatt,Fabian Isensee,Philippe Lambin,Stefan Leger,Stefan Leger,Ralph T.H. Leijenaar,Jacopo Lenkowicz,Fiona Lippert,Are Losnegård,Klaus H. Maier-Hein,Olivier Morin,Henning Müller,Sandy Napel,Christophe Nioche,Fanny Orlhac,Sarthak Pati,Elisabeth Pfaehler,Arman Rahmim,Arman Rahmim,Arvind Rao,Jonas Scherer,Muhammad Siddique,Nanna M. Sijtsema,Jairo Socarras Fernandez,Emiliano Spezi,Roel J H M Steenbakkers,Stephanie Tanadini-Lang,Daniela Thorwarth,Esther G.C. Troost,Esther G.C. Troost,Taman Upadhaya,Vincenzo Valentini,Lisanne V. van Dijk,Joost J. M. van Griethuysen,Floris H. P. van Velden,Philip Whybra,Christian Richter,Christian Richter,Steffen Löck,Steffen Löck +70 more
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
Journal ArticleDOI
Applications and limitations of radiomics
TL;DR: This technical review will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
Journal ArticleDOI
Radiomics in medical imaging-"how-to" guide and critical reflection.
TL;DR: A practical “how-to” guide for a typical radiomics analysis is delivered and the current limitations of radiomics are discussed, suggesting potential improvements, and relevant literature on the subject is summarized.
Journal ArticleDOI
Characterization of PET/CT images using texture analysis: the past, the present… any future?
Mathieu Hatt,Florent Tixier,Larry Pierce,Paul E. Kinahan,Catherine Cheze Le Rest,Dimitris Visvikis +5 more
TL;DR: Critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images are provided, issues and challenges are identified, and a list of correct formulae for usual features and recommendations regarding implementation are provided.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Least squares quantization in PCM
TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
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Cancer statistics, 2014
TL;DR: The magnitude of the decline in cancer death rates from 1991 to 2010 varies substantially by age, race, and sex, ranging from no decline among white women aged 80 years and older to a 55% decline among black men aged 40 years to 49 years.
Least Squares Quantization in PCM
TL;DR: The corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy.
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