Your browser doesn't support javascript.
loading
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.
Wada, Takeshi; Yokota, Hajime; Horikoshi, Takuro; Starkey, Jay; Hattori, Shinya; Hashiba, Jun; Uno, Takashi.
Afiliação
  • Wada T; Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
  • Yokota H; Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan. hjmykt@chiba-u.jp.
  • Horikoshi T; Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
  • Starkey J; Department of Radiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR, 97239, USA.
  • Hattori S; Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
  • Hashiba J; Department of Radiology, Chiba University Hospital, 1-8-1, Inohana Chuo-ku, Chiba, 260-8677, Japan.
  • Uno T; Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana Chuo-ku, Chiba, 260-8670, Japan.
Jpn J Radiol ; 38(3): 207-214, 2020 Mar.
Article em En | MEDLINE | ID: mdl-31820265
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.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias das Glândulas Salivares / Interpretação de Imagem Assistida por Computador / Competência Clínica / Adenoma Pleomorfo Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias das Glândulas Salivares / Interpretação de Imagem Assistida por Computador / Competência Clínica / Adenoma Pleomorfo Idioma: En Ano de publicação: 2020 Tipo de documento: Article