Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

Uyen N. Hoang; Ashkan A. Malayeri; Nathan S. Lay; Ronald M. Summers; Jianhua Yao

doi:10.1117/12.2254717

3 March 2017 Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

Uyen N. Hoang, Ashkan A. Malayeri, Nathan S. Lay, Ronald M. Summers, Jianhua Yao

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343J (2017) https://doi.org/10.1117/12.2254717
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

This pilot study performs texture analysis on multiple magnetic resonance (MR) images of common renal masses for differentiation of renal cell carcinoma (RCC). Bounding boxes are drawn around each mass on one axial slice in T1 delayed sequence to use for feature extraction and classification. All sequences (T1 delayed, venous, arterial, pre-contrast phases, T2, and T2 fat saturated sequences) are co-registered and texture features are extracted from each sequence simultaneously. Random forest is used to construct models to classify lesions on 96 normal regions, 87 clear cell RCCs, 8 papillary RCCs, and 21 renal oncocytomas; ground truths are verified through pathology reports.

The highest performance is seen in random forest model when data from all sequences are used in conjunction, achieving an overall classification accuracy of 83.7%. When using data from one single sequence, the overall accuracies achieved for T1 delayed, venous, arterial, and pre-contrast phase, T2, and T2 fat saturated were 79.1%, 70.5%, 56.2%, 61.0%, 60.0%, and 44.8%, respectively. This demonstrates promising results of utilizing intensity information from multiple MR sequences for accurate classification of renal masses.

Citation Download Citation

Uyen N. Hoang, Ashkan A. Malayeri, Nathan S. Lay, Ronald M. Summers, and Jianhua Yao "Texture analysis of common renal masses in multiple MR sequences for prediction of pathology", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343J (3 March 2017); https://doi.org/10.1117/12.2254717

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KEYWORDS

Data modeling

Magnetic resonance imaging

Tumors

Kidney

Pathology

Tumor growth modeling

Feature extraction

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