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Magn Reson Med. 2016 Apr;75(4):1708-16. doi: 10.1002/mrm.25743. Epub 2015 May 20.

Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI.

Author information

1
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, United Kingdom.
2
Duke University School of Medicine, Durham, North Carolina, USA.
3
Addenbrooke's Hospital Department of Otolaryngology, Cambridge, United Kingdom.
4
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
5
Addenbrooke's Hospital Department of Radiology, Cambridge, United Kingdom.
6
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
7
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
8
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
9
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
10
Cambridge Teaching Hospitals ENT Department, Cambridge, United Kingdom.

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. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

KEYWORDS:

diffusion-weighted MRI; textural analysis; thyroid tumors

PMID:
25995019
PMCID:
PMC4654719
DOI:
10.1002/mrm.25743
[Indexed for MEDLINE]
Free PMC Article

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