Mr. Martijn Starmans Profile

Martijn Starmans

at Erasmus Medical Center

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Proceedings Article | 2 March 2018 Presentation + Paper

Classification of malignant and benign liver tumors using a radiomics approach

Martijn P. Starmans, Razvan Miclea, Sebastian van der Voort, Wiro Niessen, Maarten Thomeer, Stefan Klein

Proceedings Volume 10574, 105741D (2018) https://doi.org/10.1117/12.2293609

KEYWORDS: Tumors, Liver, Magnetic resonance imaging, Image segmentation, Feature selection, Feature extraction, Performance modeling, Biopsy, Image registration

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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.

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