Original paper| Volume 60, P58-65, April 2019

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Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method

  • Mehdi Astaraki
    KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden

    Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden
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  • Chunliang Wang
    Corresponding author at: Hälsovägen 11C, SE-14157 Huddinge, Sweden.
    KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden
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  • Giulia Buizza
    Politecnico di Milano, Department of Electronics, Information and Bioengineering, piazza Leonardo da Vinci 42, Milan 20133, Italy
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  • Iuliana Toma-Dasu
    Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden

    Stockholm University, Department of Physics, SE-10691 Stockholm, Sweden
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  • Marta Lazzeroni
    Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden

    Stockholm University, Department of Physics, SE-10691 Stockholm, Sweden
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  • Örjan Smedby
    KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden
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Published:March 27, 2019DOI:


      • Introducing a physiologically meaningful pattern to capture intra tumor heterogeneity.
      • Proposed features are highly robust against differences in patients’ variable.
      • Survival prediction power is 0.90 (AUC) without feature selection.
      • Combined with radiomics, prediction power increased up to 0.95 (AUC).



      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.


      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).


      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.


      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.

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