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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
PublisherSPIE
Volume10134
ISBN (Electronic)9781510607132
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Other

OtherMedical Imaging 2017: Computer-Aided Diagnosis
CountryUnited States
CityOrlando
Period2/13/172/16/17

Keywords

  • Kidney lesions classification
  • Magnetic resonance imaging
  • Pathology
  • Random forest classification
  • Renal cell carcinoma

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Hoang, U. N., Malayeri, A. A., Lay, N. S., Summers, R. M., & Yao, J. (2017). Texture analysis of common renal masses in multiple MR sequences for prediction of pathology. In Medical Imaging 2017: Computer-Aided Diagnosis (Vol. 10134). [101343J] SPIE. https://doi.org/10.1117/12.2254717