CT texture analysis of renal masses: Pilot study using random forest classification for prediction of pathology

Siva P. Raman, Yifei Chen, James L. Schroeder, Peng Huang, Elliot K. Fishman

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale and Objectives: Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. Materials and Methods: CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases. Results: The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity=89%, specificity=99%), clear cell RCCs in 91% of cases (sensitivity=91%, specificity=97%), cysts in 100% of cases (sensitivity=100%, specificity=100%), and papillary RCCs in 100% of cases (sensitivity=100%, specificity=98%). Conclusions: CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.

Original languageEnglish (US)
Pages (from-to)1587-1596
Number of pages10
JournalAcademic radiology
Volume21
Issue number12
DOIs
StatePublished - Dec 1 2014

Keywords

  • Clear cell renal cell carcinoma
  • Multidetector computed tomography
  • Oncocytoma
  • Papillary renal cell carcinoma
  • Texture analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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