Giulia Buizza1, Iuliana Toma-Dasu2, Marta Lazzeroni2, Chiara Paganelli3, Marco Riboldi4, Yongjun Chang5, Örjan Smedby5, Chunliang Wang6. 1. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden; Politecnico di Milano, CartCasLab, Department of Electronics Information and Bioengineering, piazza Leonardo Da Vinci 42, Milan 20133, Italy. 2. Karolinska Institute, Medical Radiation Physics, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna 17176, Sweden. 3. Politecnico di Milano, CartCasLab, Department of Electronics Information and Bioengineering, piazza Leonardo Da Vinci 42, Milan 20133, Italy. 4. Politecnico di Milano, CartCasLab, Department of Electronics Information and Bioengineering, piazza Leonardo Da Vinci 42, Milan 20133, Italy; Ludwig-Maximilians-Universität, Faculty of Physics, Faculty of Physics, Coloumbwall 1, Garching bei München 5748, Germany. 5. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden. 6. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden. Electronic address: chunwan@kth.se.
Abstract
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.
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 cancerpatients 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.
Authors: Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa Journal: Int J Radiat Oncol Biol Phys Date: 2019-01-31 Impact factor: 7.038
Authors: Rahul Rajendran; Kevan Iffrig; Deepak K Pruthi; Allison Wheeler; Brian Neuman; Dharam Kaushik; Ahmed M Mansour; Karen Panetta; Sos Agaian; Michael A Liss Journal: Adv Urol Date: 2019-04-23
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