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Diagnostic Value of Apparent Diffusion Coefficient for predicting aggressive histological features of papillary thyroid carcinomas
Bin Song1

1Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China

Synopsis

Objective: To investigate the value of preoperative diffusion-weighted magnetic resonance imaging for predicting aggressive histological features in papillary thyroid cancer (PTC). Methods: The mean, minimum apparent diffusion coefficient (ADCmean and ADCmin) and ADC value of the solid component (ADCsolid) were compared among different aggressive histological groups. Analyses of receiver-operating characteristic curve were also performed. Results: The ADC value in the low aggressive PTC were significantly higher than in high aggressive PTC. ADCmin values had the best performance. Conclusion: ADCmin values derived from DWI could be used as quantitative predictors of aggressive histological features in PTC.

Introduction

Papillary thyroid carcinoma (PTC) has a favorable prognosis, with a mortality of 1~2% at 20 years (1). Patients with low aggressive PTC have a disease-specific survival rate of more than 99% (2). Thyroidectomy without prophylactic central neck dissection (PCND) may be appropriate for small (T1 or T2), noninvasive, clinically node-negative (cN0) PTCs. Thyroid lobectomy alone may be enough for the initial treatment of low aggressive PTC (3). Therefore, accurate risk stratification and predicting tumor aggressiveness is the cornerstone for decision making in the management of thyroid cancer (3). Fine-needle aspiration (FNA) is one of the most accurate and cost-effective methods for evaluating thyroid nodules, while can only provide few information with regards to tumor aggressiveness (4). Ultrasound (US) is used as a diagnostic tool of thyroid nodules (5, 6). However, US evaluation is an operator-dependent procedure and cannot always be relied on. Diffusion-weighted imaging (DWI) is a functional magnetic resonance imaging (MRI) technique that quantifies the diffusion of water molecules in tissues (7). Apparent diffusion coefficient (ADC) is a useful imaging biomarker in diagnosis and predicting tumor grade (7, 8, 9). Based on a few reports, ADC values are associated with papillary thyroid carcinoma (PTC) aggressive phenotype (10). However, studies performed to-date had limited numbers of patients and only focused on the mean ADC value (ADCmean) and did not include the minimum ADC (ADCmin) and ADC value of solid component (ADCsolid) in PTC.

Purpose

To investigate the value of preoperative DWI for predicting aggressive histological features in papillary thyroid cancer.

Methods

In this prospective study, 88 patients with 88 PTCs were included. One-way ANOVA or Welch test were performed for multiple comparisons of the mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC value of the solid component (ADCsolid) among the low aggressive PTC, high aggressive PTC without hobnail variants, and hobnail variants PTC groups. A nonparametric Kruskal-Wallis H test was used to analyze the difference of the lesion size among the three groups. Analyses of receiver-operating characteristic (ROC) curve were also performed.

Results

The ADCmean, ADCmin and ADCsolid in the low aggressive PTC (1.35 ± 0.20 ×10-3mm2/s, 1.10 ± 0.17 ×10-3mm2/s, and 1.26 ± 0.23 ×10-3mm2/s, respectively) were significantly (P=0.003, P＜0.001,and P＜0.001, respectively) higher than in high aggressive PTC without hobnail variants (1.16 ± 0.17 ×10-3mm2/s, 0.88 ± 0.16 ×10-3mm2/s, 1.04 ± 0.17 ×10-3mm2/s, respectively). The size of the lesions in hobnail variants PTC (2.19 ± 1.21cm) was significantly larger (P＜0.001) than that of low aggressive PTCs (0.93 ± 0.37cm). The area under the curve (AUC) for ADCmean, ADCmin and ADCsolid between the low aggressive PTCs and high aggressive PTCs without hobnail variants were 0.758, 0.851, 0.787, respectively.

Discussion

To date, the quantitative study of thyroid nodules has concentrated around DWI. Many studies (11-12) have demonstrated that the ADCs of malignant thyroid nodules are significantly lower than those of benign nodules. Lu et al (10) evaluated whether, DWI before surgery could be used to stratify tumor aggressiveness in PTC. In their study the mean ADC values of PTCs with extrathyroidal extension (ETE) were significantly lower than that of PTCs without ETE. This is consistent with our study. However, our study had relatively large numbers of patients (88 vs 21) and additional parameters (ADCmean, ADCsolid and ADCmin vs ADCmean) were measured when compared with previous studies. We also used relatively high b value (800 s/mm2 vs 500 s/mm2), which was a very important factor that affects image quality and ADC measurements. In our study, high aggressive PTCs without hobnail variants showed significant lower ADCmean, ADCmin and ADCsolid values. ADCmin had the best performance for differentiating low aggressive PTCs from high aggressive PTCs without hobnail variants in this study. The mean AUC of ADCmin was 0.851, which demonstrated that ADCmin value might be a useful quantitative parameter in the preoperative prediction of PTC aggressiveness. Theoretically, the ADCmin value of a tumor corresponds with the highest tumor cellularity, which corresponds with the most active proliferative area. Studies have suggested that the ADCmin may be an effective parameter to differentiate between malignant and benign breast lesions and predictor of tumor grading (13-15). Our present study demonstrated that the optimal cut-off value for ADCmin was 0.982×10-3 mm2/s to differentiate low aggressive PTCs with high aggressive PTCs without hobnail variants. With ADCmin less than 0.982×10-3 mm2/s, the sensitivity and specificity for diagnosing high aggressive PTCs without hobnail variants were 79.66% and 90.48%, respectively.

Conclusion

ADCmin values derived from DWI could be used as quantitative predictors of aggressive histological features during the preoperative evaluation of PTCs.

Acknowledgements

We thank all members of the Department of Radiology, Minhang Branch, Zhongshan Hospital, Fudan University (Hao Wang, Ran Wei, Wenjuan Hu, Lanyun Wang, Yi Ding, Zedong Dai, Xilin Sun)and all members of the Department of Pathology and General Surgery for helpful discussions and invaluable help in manuscript preparation.

References

(1) Brito JP, Hay ID, Morris JC. Low risk papillary thyroid cancer. BMJ 2014;348: g3045 (2) Hay ID. Management of patients with low-risk papillary thyroid carcinoma. Endocr Pract 2007;13: 521-533 (3) Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid 2016; 26:1-133 (4) Baloch ZW, LiVolsi VA, Asa SL, Rosai J, Merino MJ, Randolph G, et al. Diagnostic terminology and morphologic criteria for cytologic diagnosis of thyroid lesions: a synopsis of the National Cancer Institute Thyroid Fine-Needle Aspiration State of the Science Conference. Diagn Cytopathol 2008; 36:425-437 (5) Miyakoshi A, Dalley RW, Anzai Y. Magnetic resonance imaging of thyroid cancer. Top Magn Reson Imaging 2007; 18:293-302 (6) Zhan J, Jin JM, Diao XH, Chen Y. Acoustic radiation force impulse imaging (ARFI) for differentiation of benign and malignant thyroid nodules--A meta-analysis. Eur J Radiol 2015; 84:2181-2186 (7) Waseda Y, Yoshida S, Takahara T, Kwee TC, Matsuoka Y, Saito K, et al. Utility of computed diffusion-weighted MRI for predicting aggressiveness of prostate cancer. J Magn Reson Imaging 2017; 46:490-496 (8) Nishie A, Tajima T, Asayama Y, Ishigami K, Kakihara D, Nakayama T, et al. Diagnostic performance of apparent diffusion coefficient for predicting histological grade of hepatocellular carcinoma. Eur J Radiol 2011;80: e29-33 (9) Lotfalizadeh E, Ronot M, Wagner M, Cros J, Couvelard A, Vullierme MP, et al. Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging. Eur Radiol (10) Lu Y, Moreira AL, Hatzoglou V, Stambuk HE, Gonen M, Mazaheri Y, et al. Using diffusion-weighted MRI to predict aggressive histological features in papillary thyroid carcinoma: a novel tool for pre-operative risk stratification in thyroid cancer. Thyroid 2015 (11) Razek AA, Sadek AG, Kombar OR, Elmahdy TE, Nada N. Role of apparent diffusion coefficient values in differentiation between malignant and benign solitary thyroid nodules. AJNR Am J Neuroradiol 2008; 29:563-568 (12) Bozgeyik Z, Coskun S, Dagli AF, Ozkan Y, Sahpaz F, Ogur E. Diffusion-weighted MR imaging of thyroid nodules. Neuroradiology 2009; 51:193-198 (13) Padhani AR, Koh DM. Diffusion MR imaging for monitoring of treatment response. Magn Reson Imaging Clin N Am 2011; 19:181-209 (14) Hirano M, Satake H, Ishigaki S, Ikeda M, Kawai H, Naganawa S. Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters. AJR Am J Roentgenol 2012; 198:717-722 (15) Kitis O, Altay H, Calli C, Yunten N, Akalin T, Yurtseven T. Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol 2005; 55:393-400

Figures

Fig.1. Three different aggressive thyroid papillary carcinoma and region of interest selection in the DWI and ADC map High aggressive PTC without hobnail variants in a 59-year-old man, of which ADCmin was 0.77×10−3 mm2/s (A, B, C, D, E), low aggressive PTC in a 41-year-old woman, of which ADCmin was 1.12×10−3 mm2/s (F, G, H, I, J), and hobnail variants PTC in a 29-year-old woman with ADCmin values of 1.25×10−3 mm2/s (K, L, M, N, O). Axial DWI images with b value of 800 s/mm2 (A, F, K), zoomed DWI images with ROIs of ADCmean, ADCmin, and ADCsolid (B, G, L), ADC map (C, H, M), zoomed ADC map with ROIs of ADCmean, ADCmin, and ADCsolid (D, I, N), and microscopic image (hematoxylin-eosin stain; original magnification, x100 (E, J, O). In the zoomed DWI images and ADC map, the largest irregular circle represent ADCmean, medium size circle represent ADCsolid, and small circles represent ADCmin. E. Photomicrograph of histological specimens shows higher cellularity, enlarged nuclei, and higher nuclear/cytoplasmic ratio； J. Photomicrograph of histological specimen showing relatively low cellularity and nuclear/cytoplasmic ratio； o. Photomicrograph of histological specimen showing loosely or individually arranged cancer cells and enlarged gap in tumors. ADC = apparent diffusion coefficient; PTC = papillary thyroid carcinoma; DWI= Diffusion-weighted imaging; ADCmean=the mean ADC value; ADCmin= the minimum ADC value; ADCsolid= the ADC value of the solid component; ROIs = regions of interest.

Fig.2. Flowchart illustrates study group selection MRI= magnetic resonance imaging. PTC = papillary thyroid carcinoma. n*= the number of patients. n= the number of lesions.

Fig.3. Bland–Altman plots demonstrates the measurements of the ADCmean, ADCmin, and ADCsolid values, and demonstrating inter-observer conformity Plot A shows the results of the ADCmean value between the two observers, and plot B shows the results of the ADCsolid value between the two observers, and plot C shows the results of the ADCmin value between the two observers. The solid line indicates mean difference and the dash line indicates 95% limits of agreement. ADC=apparent diffusion coefficient; ADCmean=the mean ADC value; ADCmin= the minimum ADC value; ADCsolid= the ADC value of solid component.

Fig.4 Box plots illustrating the distribution of the arithmetic means of ADCmean, ADCmin, and ADCsolid values for the low aggressive PTC, high aggressive PTC without hobnail variants, and hobnail variants PTC group The center boxes represent the values from the lower to upper quartile (25-75 percentiles). The lines include all values except for outliers represented by dotted circles. A dotted circle defines a value that is smaller than the lower quartile minus 1.5 times the interquartile range, or larger than the upper quartile plus 1.5 times the interquartile range. ADC = apparent diffusion coefficient; PTC = papillary thyroid carcinoma; ADCmean=the mean ADC value; ADCmin= the minimum ADC value; ADCsolid= the ADC value of solid component.

Fig.5 ROC curve of the different ADC values (ADCmean, ADCmin, ADCsolid) used to distinguish high aggressive PTC without hobnail variants from low aggressive PTC and size of the lesion used to distinguish hobnail variants PTC from low aggressive PTC. A. The AUC of the different ADC values (ADCmean, ADCmin, ADCsolid) were 0.758 (blue line), 0.851 (yellow line), 0.787 (green line), respectively. B. The AUC for size of the lesion between the low aggressive PTCs and hobnail variants PTC group was 0.896. ROC = Receiver operator characteristic. ADC = apparent diffusion coefficients. PTC = papillary thyroid carcinoma. AUC = area under the curve. ADCmean=the mean ADC value. ADCmin= the minimum ADC value. ADCsolid= the ADC value of solid component.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
3481