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Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma.
van Oostenbrugge, Tim J; Spenkelink, Ilse M; Bokacheva, Louisa; Rusinek, Henry; van Amerongen, Martin J; Langenhuijsen, Johan F; Mulders, Peter F A; Fütterer, Jurgen J.
Afiliación
  • van Oostenbrugge TJ; Department of Urology Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: tim.vanoostenbrugge@radboudumc.nl.
  • Spenkelink IM; Department of Radiology and Nuclear Medicine Radboud University Medical Center, Nijmegen, the Netherlands.
  • Bokacheva L; Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • Rusinek H; Center for Advanced Imaging Innovation and Research (CAI2R) and Department of Radiology, New York University School of Medicine, New York, NY, USA.
  • van Amerongen MJ; Department of Radiology and Nuclear Medicine Radboud University Medical Center, Nijmegen, the Netherlands.
  • Langenhuijsen JF; Department of Urology Radboud University Medical Center, Nijmegen, the Netherlands.
  • Mulders PFA; Department of Urology Radboud University Medical Center, Nijmegen, the Netherlands.
  • Fütterer JJ; Department of Radiology and Nuclear Medicine Radboud University Medical Center, Nijmegen, the Netherlands.
Eur J Radiol ; 145: 110013, 2021 Dec.
Article en En | MEDLINE | ID: mdl-34768055
PURPOSE: To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics. METHOD: In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences. RESULTS: ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively. CONCLUSION: A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Adenoma Oxifílico / Neoplasias Renales Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Idioma: En Revista: Eur J Radiol Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Adenoma Oxifílico / Neoplasias Renales Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Idioma: En Revista: Eur J Radiol Año: 2021 Tipo del documento: Article