期刊
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
卷 54, 期 -, 页码 21-29出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2018.09.003
关键词
Early tumor response; Feature extraction; Non-small cell lung cancer; PET/CT
资金
- Swedish Childhood Cancer Foundation [MT2016-0016]
- Swedish Brain Foundation [FO2016-0175]
- Cancer Research Funds of Radiumhemmet
- EU FP7 funding (ARTFORCE)
- ERC advanced grant (ERC-ADG-2015) [694812]
- Dutch technology Foundation STW [10696 DuCAT, P14-19]
- Technology Programme of the Ministry of Economic Affairs
- SME Phase 2 (EU) [673780 - RAIL]
- EUROSTARS (DART), the European Program H2020-2015-17 (ImmunoSABR) [733008]
- Interreg V-A Euregio Meuse-Rhine (Euradiomics)
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.
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