Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Isaac Shiri,Hassan Maleki,Ghasem Hajianfar,Hamid Abdollahi,Saeed Ashrafinia,Mathieu Hatt,Mehrdad Oveisi,Arman Rahmim +7 more
Reads0
Chats0
TLDR
It is demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients and showed improved predictive power relative to conventional image-derived metrics.Abstract:
Aim: In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on PET and CT multi-modalities radiomic features and machine learning (ML) algorithms. Methods: Our study involved 211 NSCLC cancer patient with PET and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed Six feature selection methods and twelve classifiers were used to predict gene status in patient and model evaluation was reported on independent validation sets (68 patients). Results: The best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). Conclusion: We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.read more
Citations
More filters
Journal ArticleDOI
A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis.
Fatemeh Khatami,Mohammad Saatchi,Seyed Saeed Tamehri Zadeh,Zahra Sadat Aghamir,Alireza Namazi Shabestari,Leonardo Oliveira Reis,Seyed Mohammad Kazem Aghamir +6 more
TL;DR: In this paper, a meta-analysis study determined the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR, with uncertain accuracy.
Journal ArticleDOI
Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.
Isaac Shiri,Majid Sorouri,Parham Geramifar,Mostafa Nazari,Mohammad Abdollahi,Yazdan Salimi,Bardia Khosravi,Dariush Askari,Leila Aghaghazvini,Ghasem Hajianfar,Amir Kasaeian,Hamid Abdollahi,Hossein Arabi,Arman Rahmim,Amir Reza Radmard,Habib Zaidi +15 more
TL;DR: In this paper, the authors used radiomic features and clinical data separately or in combination to develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic feature extracted from chest CT images.
Journal ArticleDOI
Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients
TL;DR: A robust radiomics-based classifier is developed capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients and may help identifying high-risk patients who require additional treatment and follow up regimens.
Journal ArticleDOI
Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test─retest and image registration analyses
Isaac Shiri,Ghasem Hajianfar,Ahmad Sohrabi,Hamid Abdollahi,Sajad P. Shayesteh,Parham Geramifar,Habib Zaidi,Mehrdad Oveisi,Mehrdad Oveisi,Arman Rahmim,Arman Rahmim +10 more
TL;DR: The authors' results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration, which have implications for appropriate usage in diagnostic and predictive models.
Journal ArticleDOI
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.
Nguyen Quoc Khanh Le,Nguyen Quoc Khanh Le,Quang Hien Kha,Van Hiep Nguyen,Yung-Chieh Chen,Sho-Jen Cheng,Cheng Yu Chen +6 more
TL;DR: In this article, a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with non-small-cell lung cancer (NSCLC) was proposed.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI
Radiomics: Images Are More than Pictures, They Are Data.
TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Journal ArticleDOI
Radiomics: extracting more information from medical images using advanced feature analysis.
Philippe Lambin,Emmanuel Rios-Velazquez,Ralph T.H. Leijenaar,Sara Carvalho,Ruud G.P.M. van Stiphout,Patrick V. Granton,Catharina M.L. Zegers,Robert J. Gillies,Ronald Boellard,Andre Dekker,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +11 more
TL;DR: Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
Journal ArticleDOI
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Andriy Fedorov,Chintan Parmar,Ahmed Hosny,Nicole Aucoin,Vivek Narayan,Regina G. H. Beets-Tan,Regina G. H. Beets-Tan,Jean-Christophe Fillion-Robin,Steve Pieper,Hugo J.W.L. Aerts +12 more
TL;DR: PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung lesions is demonstrated.
Journal ArticleDOI
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Kenneth W. Clark,Bruce A. Vendt,Kirk E. Smith,John Freymann,Justin Kirby,Paul Koppel,Stephen M. Moore,Stanley R. Phillips,David R. Maffitt,Michael Pringle,Lawrence Tarbox,Fred W. Prior +11 more
TL;DR: The management tasks and user support model for TCIA is described, an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer.
Related Papers (5)
Radiomics: the bridge between medical imaging and personalized medicine
Philippe Lambin,Ralph T.H. Leijenaar,Timo M. Deist,Jurgen Peerlings,Evelyn E.C. de Jong,Janita E. van Timmeren,Sebastian Sanduleanu,Ruben T. H. M. Larue,Aniek J.G. Even,Arthur Jochems,Yvonka van Wijk,Henry C. Woodruff,Johan van Soest,Tim Lustberg,Erik Roelofs,Wouter van Elmpt,Andre Dekker,Felix M. Mottaghy,Felix M. Mottaghy,Joachim E. Wildberger,Sean Walsh +20 more
Radiomics: Images Are More than Pictures, They Are Data.
Computational Radiomics System to Decode the Radiographic Phenotype
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