Classification of malignant and benign liver tumors using a radiomics approach

Martijn P.A. Starmans*, Razvan L. Miclea, Sebastian R. Van Der Voort, Wiro J. Niessen, Maarten G. Thomeer, Stefan Klein

*Corresponding author for this work

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    13 Citations (Scopus)
    130 Downloads (Pure)


    Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

    Original languageEnglish
    Title of host publicationMedical Imaging 2018
    Subtitle of host publicationImage Processing
    ISBN (Electronic)9781510616370
    Publication statusPublished - 2018
    EventMedical Imaging 2018: Ultrasonic Imaging and Tomography - Houston, United States
    Duration: 10 Feb 201815 Feb 2018


    ConferenceMedical Imaging 2018: Ultrasonic Imaging and Tomography
    Country/TerritoryUnited States


    • classification
    • computer-aided diagnosis
    • hepatocellular carcinoma
    • primary liver tumors
    • radiomics


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