Journal ArticleDOI
Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery
Margarita Kirienko,Luca Cozzi,Lidija Antunovic,Lisa Lozza,Antonella Fogliata,Emanuele Voulaz,Alexia Rossi,Arturo Chiti,Martina Sollini +8 more
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TLDR
A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery.Abstract:
Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery A cohort of 295 patients was selected Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients The endpoint of this study was DFS Both computed tomography (CT) and fluorodeoxyglucose positron emission tomography (PET) images generated from the PET/CT scanner were analysed Textural features were calculated using the LifeX package Statistical analysis was performed using the R platform The datasets were separated into two cohorts by random selection to perform training and validation of the statistical models Predictors were fed into a multivariate Cox proportional hazard regression model and the receiver operating characteristic (ROC) curve as well as the corresponding area under the curve (AUC) were computed for each model built The Cox models that included radiomic features for the CT, the PET, and the PET+CT images resulted in an AUC of 075 (95%CI: 065–085), 068 (95%CI: 057–080), and 068 (95%CI: 058–074), respectively The addition of clinical predictors to the Cox models resulted in an AUC of 061 (95%CI: 051–069), 064 (95%CI: 053–075), and 065 (95%CI: 050–072) for the CT, the PET, and the PET+CT images, respectively A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgeryread more
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Journal ArticleDOI
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Stefano Trebeschi,S.G. Drago,S.G. Drago,Nicolai Juul Birkbak,Nicolai Juul Birkbak,Nicolai Juul Birkbak,Ieva Kurilova,A.M. Cǎlin,A. Delli Pizzi,Ferry Lalezari,Doenja M. J. Lambregts,Maartje W. Rohaan,Chintan Parmar,Elisa A. Rozeman,Koen J. Hartemink,Charles Swanton,Charles Swanton,J.B.A.G. Haanen,Christian U. Blank,Egbert F. Smit,Regina G. H. Beets-Tan,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +22 more
TL;DR: Radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.
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Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
TL;DR: The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.
Journal ArticleDOI
Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.
Abdalla Ibrahim,Sergey Primakov,Sergey Primakov,Manon Beuque,Henry C. Woodruff,Henry C. Woodruff,Iva Halilaj,Guangyao Wu,T. Refaee,T. Refaee,Renée W Y Granzier,Renée W Y Granzier,Y. Widaatalla,Roland Hustinx,Felix M. Mottaghy,Felix M. Mottaghy,Philippe Lambin,Philippe Lambin +17 more
TL;DR: The status of quantitative medical image analysis using radiomics and deep learning is reported, the challenges the field is facing, a framework for robust radiomics analysis is proposed, and future prospects are discussed.
Journal ArticleDOI
Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.
Yuming Jiang,Qingyu Yuan,Wenbing Lv,Sujuan Xi,Weicai Huang,Zepang Sun,Hao Chen,Liying Zhao,Wei Liu,Yanfeng Hu,Lijun Lu,Jianhua Ma,Tuanjie Li,Jiang Yu,Quanshi Wang,Guoxin Li +15 more
TL;DR: The newly developed radiomic signature was a powerful predictor of OS and DFS and could predict which patients could benefit from chemotherapy.
Journal ArticleDOI
Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions
Margarita Kirienko,Luca Cozzi,Alexia Rossi,Emanuele Voulaz,Lidija Antunovic,Antonella Fogliata,Arturo Chiti,Martina Sollini +7 more
TL;DR: PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.
References
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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J.W.L. Aerts,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Chintan Parmar,Patrick Grossmann,Sara Carvalho,Sara Cavalho,Johan Bussink,René Monshouwer,Benjamin Haibe-Kains,Derek H. F. Rietveld,Frank J. P. Hoebers,Michelle M. Rietbergen,C. René Leemans,Andre Dekker,John Quackenbush,Robert J. Gillies,Philippe Lambin +17 more
TL;DR: The data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer, which may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
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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.
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Non-Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and Survivorship
TL;DR: The introduction of angiogenesis, epidermal growth factor receptor inhibitors, and other new anti-cancer agents is changing the present and future of this disease and will certainly increase the number of lung cancer survivors.
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FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
Ronald Boellaard,Roberto Delgado-Bolton,Wim J.G. Oyen,Francesco Giammarile,Klaus Tatsch,W. Eschner,Fred J. Verzijlbergen,Sally F. Barrington,Lucy Pike,Wolfgang A. Weber,Sigrid Stroobants,Dominique Delbeke,Kevin J. Donohoe,Scott Holbrook,Michael M. Graham,Giorgio Testanera,Otto S. Hoekstra,Josée M. Zijlstra,Eric P. Visser,Corneline J. Hoekstra,Jan Pruim,Antoon T.M. Willemsen,Bertjan Arends,Joerg Kotzerke,Andreas Bockisch,Thomas Beyer,Arturo Chiti,Bernd J. Krause +27 more
TL;DR: Both the previous and these new guidelines specifically aim to achieve standardised uptake value harmonisation in multicentre settings.
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The Eighth Edition Lung Cancer Stage Classification
TL;DR: This paper summarizes the eighth edition of lung cancer stage classification, which is the worldwide standard as of January 1, 2017, based on a large global database, a sophisticated analysis, extensive internal validation as well as multiple assessments confirming generalizability.
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