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Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Politecnico di Milano, CartCasLab, Department of Electronics Information and Bioengineering, piazza Leonardo Da Vinci 42, Milan 20133, Italy.
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2018 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed) Published
Abstract [en]

Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy. 

Place, publisher, year, edition, pages
Associazione Italiana di Fisica Medica , 2018. Vol. 54, p. 21-29
Keywords [en]
Early tumor response, Feature extraction, Non-small cell lung cancer, PET/CT
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-236648DOI: 10.1016/j.ejmp.2018.09.003ISI: 000447271300003Scopus ID: 2-s2.0-85053799575OAI: oai:DiVA.org:kth-236648DiVA, id: diva2:1262842
Funder
Swedish Childhood Cancer Foundation, MT2016-0016The Swedish Brain FoundationThe Cancer Research Funds of Radiumhemmet
Note

Export Date: 22 October 2018; Article; CODEN: PHYME; Correspondence Address: Wang, C.; KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Sweden; email: chunwan@kth.se; Funding details: 673780, MOEA, Ministry of Economic Affairs; Funding details: IDEAS-ERC, FP7 Ideas: European Research Council; Funding details: MT2016-0016, Barncancerfonden; Funding details: UM, Universiteit Maastricht; Funding details: H2020-2015-17, Eurostars; Funding details: 733008, Eurostars; Funding details: FO2016-0175, Hjärnfonden; Funding details: 694812; Funding details: ERC-ADG-2015; Funding details: 10696 DuCAT, STW, Stichting voor de Technische Wetenschappen; Funding text: The Swedish Childhood Cancer Foundation , Grant No. MT2016-0016 , the Swedish Brain Foundation , Grant No. FO2016-0175 , and the Cancer Research Funds of Radiumhemmet supported this work for the study design, article preparation, data interpretation and decision to submit the article. Professor Philippe Lambin and Dr. Wouter van Elmpt from Maastricht University Medical Center are kindly acknowledged for providing the patient data set used in this study. Initial data processing and data collection were supported by: EU FP7 funding (ARTFORCE); ERC advanced grant (ERC-ADG-2015, No. 694812 – Hypoximmuno); the Dutch technology Foundation STW (Grant No. 10696 DuCAT & No. P14-19 Radiomics STRaTegy), which is the applied science division of NWO; the Technology Programme of the Ministry of Economic Affairs; SME Phase 2 (EU proposal 673780 – RAIL); EUROSTARS (DART), the European Program H2020-2015-17 (ImmunoSABR – No. 733008); the Interreg V-A Euregio Meuse-Rhine (“Euradiomics”). QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-13Bibliographically approved

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Smedby, ÖrjanWang, Chunliang

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