Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach

Takeshi Wada, Hajime Yokota, Takuro Horikoshi, Jay Starkey, Shinya Hattori, Jun Hashiba, Takashi Uno

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background and purpose: The purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability. Materials and methods: One hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model. Results: AUCs of the imaging features from whole-tumor varied between readers (0.50–0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82–0.87) than for one-point (0.66–0.79) in all readers. Conclusion: Some imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.

Original languageEnglish (US)
Pages (from-to)207-214
Number of pages8
JournalJapanese Journal of Radiology
Volume38
Issue number3
DOIs
StatePublished - Mar 1 2020

Keywords

  • Carcinoma ex pleomorphic adenoma
  • Diagnostic performance
  • Machine learning
  • Pleomorphic adenoma
  • Radiomics

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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