Multi‐institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion‐weighted MRI. Issue 4 (20th May 2015)

• Record Type: Journal Article
• Authors: Brown, Anna M.; Nagala, Sidhartha; McLean, Mary A.; Lu, Yonggang; Scoffings, Daniel; Apte, Aditya; Gonen, Mithat; Stambuk, Hilda E.; Shaha, Ashok R.; Tuttle, R. Michael; Deasy, Joseph O.; Priest, Andrew N.; Jani, Piyush; Shukla‐Dave, Amita; Griffiths, John
• Is Part Of: Magnetic resonance in medicine. Volume 75:Issue 4(2016)
• Page Start: 1708
• Page End: 1716
• Publication Date: 2015-05-20
• Subjects: textural analysis -- diffusion‐weighted MRI -- thyroid tumors; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.25743
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Ultrasound‐guided fine needle aspirate cytology fails to diagnose many malignant thyroid nodules; consequently, patients may undergo diagnostic lobectomy. This study assessed whether textural analysis (TA) could noninvasively stratify thyroid nodules accurately using diffusion‐weighted MRI (DW‐MRI). Methods: This multi‐institutional study examined 3T DW‐MRI images obtained with spin echo echo planar imaging sequences. The training data set included 26 patients from Cambridge, United Kingdom, and the test data set included 18 thyroid cancer patients from Memorial Sloan Kettering Cancer Center (New York, New York, USA). Apparent diffusion coefficients (ADCs) were compared over regions of interest (ROIs) defined on thyroid nodules. TA, linear discriminant analysis (LDA), and feature reduction were performed using the 21 MaZda‐generated texture parameters that best distinguished benign and malignant ROIs. Results: Training data set mean ADC values were significantly different for benign and malignant nodules ( P = 0.02) with a sensitivity and specificity of 70% and 63%, respectively, and a receiver operator characteristic (ROC) area under the curve (AUC) of 0.73. The LDA model of the top 21 textural features correctly classified 89/94 DW‐MRI ROIs with 92% sensitivity, 96% specificity, and an AUC of 0.97. This algorithm correctly classified 16/18 (89%) patients in the independently obtained test set of thyroid DW‐MRI scans. Conclusion: TA classifies thyroid nodules with high sensitivity and specificity on multi‐institutional DW‐MRI data sets. This method requires further validation in a larger prospective study. Magn Reson Med, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Magn Reson Med 75:1708–1716, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.