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Autor(en) / Beteiligte
Titel
Diffusion-Weighted Imaging Using a Readout-Segmented, Multishot EPI Sequence at 3 T Distinguishes between Morphologically Differentiated and Undifferentiated Subtypes of Thyroid Carcinoma—A Preliminary Study
Ist Teil von
  • Translational oncology, 2016-10, Vol.9 (5), p.403-410
Ort / Verlag
United States: Elsevier Inc
Erscheinungsjahr
2016
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
  • Abstract BACKGROUND: Thyroid carcinomas represent the most frequent endocrine malignancies. Recent studies were able to distinguish malignant from benign nodules of the thyroid gland with diffusion-weighted imaging (DWI). Although this differentiation is undoubtedly helpful, presurgical discrimination between well-differentiated and undifferentiated carcinomas would be crucial to define the optimal treatment algorithm. Therefore, the aim of this study was to investigate if readout-segmented multishot echo planar DWI is able to differentiate between differentiated and undifferentiated subtypes of thyroid carcinomas. PATIENTS AND METHODS: Fourteen patients with different types of thyroid carcinomas who received preoperative DWI were included in our study. In all lesions, apparent diffusion coefficient (ADC)min, ADCmean, ADCmax, and D were estimated on the basis of region of interest measurements after coregistration with T1-weighted, postcontrast images. All tumors were resected and analyzed histopathologically. Ki-67 index, p53 synthesis, cellularity, and total and average nucleic areas were estimated using ImageJ version 1.48. RESULTS: Analysis of variance revealed a statistically significant difference in ADCmean values between differentiated and undifferentiated thyroid carcinomas ( P = .022). Spearman Rho calculation identified significant correlations between ADCmax and cell count ( r = 0.541, P = .046) as well as between ADCmax and total nuclei area ( r = 0.605, P = .022). CONCLUSION: DWI can distinguish between differentiated and undifferentiated thyroid carcinomas.
Sprache
Englisch
Identifikatoren
ISSN: 1936-5233
eISSN: 1936-5233
DOI: 10.1016/j.tranon.2016.09.001
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_7c65cd9d08b64fa48b721776a59f7502
Format
Schlagworte
Oncology, Original article

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