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

doi:10.1117/12.2293609

2 March 2018 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

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105741D (2018) https://doi.org/10.1117/12.2293609
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Martijn P. A. Starmans, Razvan L. Miclea, Sebastian R. van der Voort, Wiro J. Niessen, Maarten G. Thomeer, and Stefan Klein "Classification of malignant and benign liver tumors using a radiomics approach", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741D (2 March 2018); https://doi.org/10.1117/12.2293609

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available