Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma

Yusuke Uchida, Soichiro Yoshida, Yuki Arita, Hiroki Shimoda, Koichiro Kimura, Ichiro Yamada, Hajime Tanaka, Minato Yokoyama, Yoh Matsuoka, Masahiro Jinzaki, Yasuhisa Fujii

Research output: Contribution to journalArticlepeer-review

Abstract

Preoperative imaging differentiation between ChRCC and RO is difficult with conventional subjective evaluation, and the development of quantitative analysis is a clinical challenge. Forty-nine patients underwent partial or radical nephrectomy preceded by MRI and followed by pathological diagnosis with ChRCC or RO (ChRCC: n = 41, RO: n = 8). The whole-lesion volume of interest was set on apparent diffusion coefficient (ADC) maps of 1.5T-MRI. The importance of selected texture features (TFs) was evaluated, and diagnostic models were created using random forest (RF) analysis. The Mean Decrease Gini as calculated through RF analysis was the highest for mean_ADC_value. ChRCC had a significantly lower mean_ADC_value than RO (1.26 vs. 1.79 × 10−3 mm2/s, p < 0.0001). Feature selection by the Boruta method identified the first-quartile ADC value and GLZLM_HGZE as important features. ROC curve analysis showed that there was no significant difference in the classification performances between the mean_ADC_value-only model and the Boruta model (AUC: 0.954 vs. 0.969, p = 0.236). The mean ADC value had good predictive ability for the distinction between ChRCC and RO, comparable to that of the combination of TFs optimized for the evaluated cohort. The mean ADC value may be useful in distinguishing between ChRCC and RO.

Original languageEnglish
Article number817
JournalDiagnostics
Volume12
Issue number4
DOIs
Publication statusPublished - 2022 Apr

Keywords

  • diffusion magnetic resonance imaging
  • machine learning
  • oncocytoma
  • renal cell carcinoma

ASJC Scopus subject areas

  • Clinical Biochemistry

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