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  • 1. Astaraki, Mehdi
    et al.
    Wang, Chunliang
    Buizza, Giulia
    Toma-Dasu, Iuliana
    Stockholm University, Faculty of Science, Department of Physics. Karolinska Institutet, Sweden.
    Lazzeroni, Marta
    Stockholm University, Faculty of Science, Department of Physics. Karolinska Institutet, Sweden.
    Smedby, Örjan
    Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method2019In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, p. 58-65Article in journal (Refereed)
    Abstract [en]

    Purpose

    To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.

    Methods

    Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).

    Results

    The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.

    Conclusion

    A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

  • 2. Buizza, Giulia
    et al.
    Toma-Dasu, Iuliana
    Karolinska Institutet, Sweden.
    Lazzeroni, Marta
    Karolinska Institutet, Sweden.
    Paganelli, Chiara
    Riboldi, Marco
    Chang, Yongjun
    Smedby, Örjan
    Wang, Chunliang
    Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans2018In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed)
    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.

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