ROLE OF DIFFUSION-WEIGHTED MR IMAGING OF THYROID NODULES

Document Type : Original Article

Authors

1 Department of General Surgery, Faculty of Medicine, Al-Azhar University

2 Department of Radio-diagnosis, Faculty of Medicine, Al-Azhar University

Abstract

Background: Thyroid nodules are the most common disorder of thyroid gland. In Egypt, nodular goiter occurs in 4%-7% of the population. New radiological imaging techniques might be promising for the differential diagnosis of thyroid nodularity’s.
Objective: to determine the diagnostic role of diffusion weighted imaging (DWI) in the differentiation of malignant and benign thyroid nodules by using histopathological study as the reference standard.
Patients and Methods: 35 patients were included in this study (28 females and 7 males), their ages vary from (12 years to 75 years) with mean age of 48.65 (±14.72). All patients were referred to the surgical department, El Houssin University Hospital in the time period from July 2018 to August 2019 with thyroid gland enlargement. Results: In our study, there were 7(20.6%) males and 28 (80%) females in the patients group. Percentage of benign thyroid nodule was 52.9%. Percentage of malignant thyroid nodules was 47.1%. apparent diffusion coefficient (ADC) value was calculated for both benign and malignant nodules, the range of ADC values for benign thyroid nodules were (0.03 to 3.5) of mean (1.86 ± 0.82) and (0.5 to 1.5) of mean (1.04 ± 0.2), p value was significant with a cut off value 1.2 with 93% sensitivity and 83% specificity.
Conclusion: DWI provides very useful and promising results on the nature of a thyroid nodule. Even these results may have a role in the selection of nodules that were going to undergo fine-needle aspiration cytology (FNAC). Although these results were promising, further investigations are needed with larger patient groups.

Keywords

Main Subjects


ROLE OF DIFFUSION-WEIGHTED MR IMAGING OF THYROID NODULES

By

Shawky M. Deabes, Mohammad H. El-Shafey, Hytham M. Nafady*, and Hytham A. Fath El- Bab

Department of General Surgery and Department of Radio-diagnosis*, Faculty of Medicine, Al-Azhar University

Corresponding author: Hytham A. Fath El-Bab,

Mobile: 0122437861, E-mail: drhaythm48@gmail.com

ABSTRACT

Background: Thyroid nodules are the most common disorder of thyroid gland. In Egypt, nodular goiter occurs in 4%-7% of the population. New radiological imaging techniques might be promising for the differential diagnosis of thyroid nodularity’s.

Objective: to determine the diagnostic role of diffusion weighted imaging (DWI) in the differentiation of malignant and benign thyroid nodules by using histopathological study as the reference standard.

Patients and Methods: 35 patients were included in this study (28 females and 7 males), their ages vary from (12 years to 75 years) with mean age of 48.65 (±14.72). All patients were referred to the surgical department, El Houssin University Hospital in the time period from July 2018 to August 2019 with thyroid gland enlargement. Results: In our study, there were 7(20.6%) males and 28 (80%) females in the patients group. Percentage of benign thyroid nodule was 52.9%. Percentage of malignant thyroid nodules was 47.1%. apparent diffusion coefficient (ADC) value was calculated for both benign and malignant nodules, the range of ADC values for benign thyroid nodules were (0.03 to 3.5) of mean (1.86 ± 0.82) and (0.5 to 1.5) of mean (1.04 ± 0.2), p value was significant with a cut off value 1.2 with 93% sensitivity and 83% specificity.

Conclusion: DWI provides very useful and promising results on the nature of a thyroid nodule. Even these results may have a role in the selection of nodules that were going to undergo fine-needle aspiration cytology (FNAC). Although these results were promising, further investigations are needed with larger patient groups.

Key words: Diffusion-Weighted MRI, thyroid nodules.

 

 

INTRODUCTION

     Nodular thyroid is commonly detected on palpation in 4%–7% of the population (Zamora and Cassaro, 2018) on sonographic examination in 10%– 40%, and by pathologic examination at autopsy in 50%. In contrast, compared with the high prevalence of nodular thyroid disease, thyroid cancer is rare. The challenge of imaging thyroid nodules is to reassure most patients who have benign disease and to diagnose the minority of patients who will prove to have a malignancy (Ly et al., 2016).

     Ultrasonography has been used in the assessment of the thyroid nodules as a primary imaging technique (Floridi et al., 2019). Currently, there is no single sonographic criterion that can reliably distinguish benign from malignant thyroid nodules (Xie et al., 2016). The results of predicting thyroid cancer with color Doppler sonography are controversial, with some reporting that Doppler sonography is helpful and others reporting that it did not improve diagnostic accuracy (Aslan et al., 2018). The hazards of radiation exposure are unavoidable in nuclear scintigraphy, and not all functioning nodules on scintigraphy are benign. The risk of cancer in a cold nodule is 4 times more common than in a hot nodule. Fine-needle aspiration biopsy (FNAB) with cytologic evaluation is commonly used, but it is inconclusive in 15%–20% of patients, in addition to the possible, but less likely, associated hemorrhage (Popoveniuc Jonklaas, 2012). The incidence of cancer in patients with thyroid nodules selected for FNAB is approximately 9.2%–13%.5 FNAB is considered an effective method for differentiating between benign and malignant thy- roid nodules (Hoang, 2010).

     Routine T1- and T2-weighted MR imaging has a limited role in the evaluation of thyroid nodules. It cannot distinguish benign from malignant nodules or assess the functional status of thyroid nodules (Wang et al., 2018). Diffusion-weighted MR imaging has been used to characterize head and neck tumors, in which there are significant differences in the apparent diffusion coefficient ADC values of malignant tumors and benign lesions.

     The present study aimed to determine the diagnostic role of DWI in the differentiation of malignant and benign thyroid nodules by using histopathological study as the reference standard.

 

PATIENTS AND METHODS

     This retrospective study was conducted on 35 patients (28 females and 7 males), their ages varied between 12 years and 75 years, with a mean age of 48.65. All patients were referred to the Surgical Department, Al-Hussein University Hospital during the time period from July 2018 to August 2019. The study included patients with thyroid nodules, normal thyroid hormonal and bleeding profiles who underwent MR neck for evaluation of thyroid nodule.

     Patients who did not have MRI exam before pathology, MRI exam with degraded quality, or patients who did not have pathology after MRI were excluded from the study.

     All patients were subjected to MRI using Philips Achieva 1.5T, ultrasound-guided PATHOLOGY and Cytological examination and underwent total thyroidectomy. Histopathological analysis was studied.

     MRI was performed using a 1.5-T system (Achieva 1.5-T Pulsar, Philips Healthcare). The basic sequences were obtained in axial planes. The basic MRI protocol consisted of the following sequences; T1-weighted turbo spin-echo imaging (TR/TE: 570/13 & 3-4mm section thickness with a 3-4mm intersection gap), T1- weighted FFE imaging (TR/TE: 300/406 & 3-5mm section thickness with a 3-5mm intersection gap),T2-weighted turbo spin-echo imaging (TR/TE: 6250/105 & 3-4mm section thickness with a 3-4mm intersection gap), diffusion-weighted single-shot turbo spin-echo echo-planar imaging (TR/TE: 2500/70, b factors: 0-400-800s/mm2 & 3-4mm section thickness with a 3-4mm intersection gap). ADC maps were reconstructed.

     MR images were reviewed on DICOM viewer and signal intensities of thyroid nodules and paraspinal muscle on T1-weighted imaging, T2-weighted imaging, ADC were measured pixel by pixel. For each measurement, mean signal intensities were obtained by placing a circular ROI cursor. For thyroid nodules, signal intensities were measured with a circular ROI drawn to encompass the entire nodule at the largest cross-section area as much as possible, carefully excluding artifacts or cystic portions of thyroid nodules. The signal intensity ratio (SIR) was calculated for each sequence as a ratio of signal intensity of the thyroid nodule to that of the Para spinal muscle.

 

Statistical Analysis:

     Data were coded and entered using the statistical package SPSS (Statistical Package for the Social Sciences) version 25. Data were summarized using mean, standard deviation, minimum and maximum in quantitative data and using frequency (count) and relative frequency (percentage) for categorical data. Comparisons between quantitative variables were done using the non-parametric Mann-Whitney test. For comparing categorical data, Chi square (χ2) test was performed. Exact test was used instead when the expected frequency is less than 5. ROC curve was constructed with area under curve analysis performed to detect best cutoff value of ADC and SIR for detection of malignancy. P-values less than 0.05 were considered as statistically significant.


 

RESULTS

 

 

     ‌Patients were grouped according to final pathological diagnosis into 2 groups the benign group and the malignant group, the former included 19 patients, the latter included 16 patients (Table 1).


 

Table (1):   Distribution according to sex of patients and percentage of benign and malignant lesions

Parameters

Count

Percentage

Sex distribution

 

 

Male

7

20%

Female

28

80%

Pathology

 

 

Benign

19

54.3%

Malignant

16

45.7%

 

 

     The distribution of benign and malignant lesions in males were 6 and 2 lesions respectively, the distribution of benign and malignant lesions in females were 13 and 14 lesions respectively, P value was insignificant (0.405) (Table 2).

 

 

 

Table (2):Demonstrates the distribution of pathology according to sex

Pathology

Benign

Malignant

P value

Count

%

Count

%

 

Sex

M

6

31.6%

2

12.5%

> 0.05

F

13

68.4%

14

87.5%

 

 

     The diagnosis of different lesions by cytology was: 2 cases of medullary thyroid cancer, 10 cases of papillary cancer, 2 cases of follicular carcinoma and 2 cases of thyroiditis with atypical lymphocytic infiltrates. All of the above lesions were malignant. The benign lesions were classified 12 colloid nodular goiters, and 7 benign follicular neoplasm (Table 3).

 

 

Table (3):Different cytological results among the patients

Cytology

Number of patients

Medullary thyroid cancer

2

Papillary thyroid cancer

10

Thyroiditis with atypical lymphocytic infiltration

2

Colloid nodular goiter

12

Benign Follicular neoplasm

7

Follicular carcinoma

2

 

 

     The size of benign nodules were ranged from 0.1 to 25 cm2 with mean of 5.16 ± 5.9 and the size of malignant nodules were ranged from 0.2 to 8 cm2 with mean of 2.36± 2.07, the size of thyroid of thyroid nodules were insignificantly related to the pathology (Table 4).

 

 

Table (4):   Range of size of thyroid nodules involved in the study

Pathology

 

 

P value

Benign

Mean

5.16

> 0.05

SD

5.92

Minimum

0.1

Maximum

25

Malignant

Mean

2.36

 

SD

2.07

Minimum

0.2

Maximum

8

 

 

     Signal intensity ratio of T1 sequence of benign and malignant nodules were calculated, it ranged from (0.01 to 2.75) in benign lesions of mean (1.03 ± 0.51) and (1 to 1.6) of mean (1.22 ± 0.2) in malignant nodules, area under curve was 0.78, p value was insignificant (0.3) (Table 5).

 

 

 

 

 

 

Table (5):   Range of SIR T1 in differentiation between benign and malignant thyroid nodules

SIR1

 

 

P value

Benign

Mean

1.03

> 0.05

SD

0.51

Minimum

0.01

Maximum

2.75

Malignant

Mean

1.22

SD

0.2

Minimum

1

Maximum

1.6

SIR T2

 

 

P value

Benign

Mean

3.7

> 0.05

SD

1.1008

Minimum

1.5

Maximum

6.6

Malignant

Mean

3.2

SD

0.46

Minimum

1.7

Maximum

4.2

 

Area Under the Curve

P value

95% Confidence Interval

Lower bound

Upper bound

0.415

> 0.05

0.210

0.620

 

ADC

 

 

P value

Benign

Mean

1.86

<0.0001

SD

0.82

Minimum

0.03

Maximum

3.5

Malignant

Mean

1.04

SD

0.2

Minimum

0.5

Maximum

1.5

 

 

     Signal intensity ratio of T2 sequence of benign and malignant nodules were calculated, it ranged from (1.5 to 6.6) in benign lesions of mean (3.7 ± 1.1) and (1.7 to 4.2) of mean (3.2 ± 0.46) in malignant nodules as shown in [table 8], area under curve was 0.415, p value was also insignificant.

 

 

 

     ADC value was calculated for both benign and malignant nodules, the range of ADC values for benign thyroid nodules were 1.86 ± 0.82 and 0.5 to 1.5; respectively; p value was significant .

 

 

 

     ROC curve analysis was done to determine optimum thresholds for discrimination between benign and malignant lesions based on ADC values it was about 0.8 (Figure 1).

 

 


Figure (1): ROC curve for detection of malignancy using ADC

     Cut off value was established it measured 1.2 with 93% sensitivity and 83% specificity as demonstrated in. (Table  6)

 

Table (6):   Cut off value, sensitivity and specificity of ADC value

Area Under the Curve

P value

95% Confidence Interval

 

Cut off

Sensitivity %

Specificity %

0.820

<0.0001

<1.2

93

83

 

 

DISCUSSION

     MRI signal intensity characteristics of thyroid lesions may be able to discriminate between different types of thyroid lesions, potentially improving clinical management. Also various studies have assessed the power of diffusion-weighted imaging (DWI) and its quantitative counterpart apparent diffusion correction (ADC) for differentiation between benign and malignant thyroid nodules. The results of these studies have been promising but wide variability has been encountered. It is a noninvasive and complimentary tool in the assessment of thyroid nodules. DWI can lower the burden of unnecessary surgery in cases with inconclusive pathology (Bozgeyik et al., 2013).

     The purpose of our study was to determine the diagnostic role of diffusion- weighted imaging (DWI), ADC, T1 and T2 SIR values in the differentiation of malignant and benign thyroid nodules by using fine needle aspiration biopsy cytology criteria as a reference standard  (Bozgeyik et al., 2013).

     DWI may be a routine sequence in oncologic settings and it provides much useful information about tumoral tissue. It can be added to conventional MRI sequences. The most prominent advantages of this technique are absence of radiation, no necessity for of intravenous contrast material, very quick technique and quantitative information of tissue provided by ADC measurement (White et al., 2014).

     Generally, the ADC values of the malignant nodules were reported to be significantly lower than those of benign thyroid nodules, attributed to cellular density and blood perfusion to the tissue. Malignant nodules of thyroid have compact cellularity, nucleocyte–cytoplasmic ratio, and usually cell membrane, which results in restriction in Brownian motion of water molecules in extracellular space and leads to decreased ADC value (Bozgeyik et al., 2013).

     In our study, evaluation of the ADC values following using b factors 0, 400, and 800 mm2/s revealed high mean values for benign thyroid nodules and reduced mean values for malignant thyroid nodules. The cut off value for differentiating malignant thyroid nodules from benign nodules was significant.

     Khizer et al. (2015) stated that thyroid gland nodules. DWI was done using b-values of 0 and 1000 s/mm2and ADC values were calculated for the thyroid nodules. All of these patients were subjected to ultrasound guided core biopsy and histopathology results were correlated with ADC values. The benign nodules showed facilitated diffusion while malignant nodules showed restricted diffusion. The mean ADC value of the malignant thyroid nodules (0.94 ± 0.16 x 10- 3mm2/s) was significantly lower than that of the benign thyroid nodules (1.93 ± 0.13 x 10-3mm2/s) (p-value =0.05). ADC value of 1.6 x 10-3mm2/s was used as a cut-off, for differentiating benign from malignant thyroid nodules.

     This wide variability in between various studies might be justified by heterogeneity in the design of studies, especially regarding b factors. Heterogeneity of thyroid neoplastic cellular types might also contribute to this variability. Ilica et al. (2013) showed an ADC cutoff value of 0.9 × 10- 3 mm2/s results in sensitivity and specificity of 90% and 100%, respectively, for differentiation of malignant and benign thyroid lesions; however, the study was limited by small number of participants. Several studies have proposed b value of 300 to be enough for characterization of thyroid nodules; however, according to the Meta analysis of using higher b value improves the strength of DWI for characterization of thyroid nodules. Using low b factor might lead to inaccurately high ADC value (Chen et al. 2016; Aghaghazvini et al., 2018).

     MRI T1 and T2 signal intensity ratios (SIR) could probably assist in differentiation between and malignant thyroid nodules; Noda et al. (2015), in a study on the significance of SIR T1, SIR T2 and ADC values in differentiation between benign and malignant papillary thyroid cancer, the quantitative image analysis revealed that the mean SIR of axial TSE T1-weighted imaging of benign nodules and malignant nodules of insignificant value. The mean SIR of axial TSE T2-weighted imaging of benign nodules was significantly higher than malignant thyroid nodules. They had explained that the cause of this significance is related to the pathology of the papillary of thyroid cancer under microscope, i.e. high cellularity and fibrous component, which is not the case in other types of malignant thyroid nodules.

     However, in our study after comparison between the SIRT1 and SIR T2 values of the benign and malignant thyroid nodules the signal intensity ratio (SIR) of T1weighted images ranges for both benign and malignant thyroid nodules about of insignificant which agreed with the fore mentioned study. Signal intensity ratio (SIR) of T2 weighted images ranges for both benign and malignant thyroid nodules about and of insignificant value, which was different from this study. This might be attributed to the small number of the malignant nodules in our study specifically the papillary malignant subtype.

     The T2 SIR, although it is not significant, it can be used to evaluate the morphological structure of thyroid pathology and to detect pathologically looking cervical lymph nodes; combined T2 SIR and ADC may yield higher diagnostic performance as it helps choosing proper areas for ADC measurement away from cystic areas. Moreover, although T1-weighted imaging was not significant in the differentiation of malignant and benign thyroid nodules, the previous studies convey that T1-weighted imaging (even if not significant) may play a supportive role in detecting hemorrhage or calcification within thyroid nodules.

CONCLUSION

     DWI provided very useful and promising results on the nature of a thyroid nodule. These results may have a role in the selection of nodules that are going to undergo FNAC. However, although these are promising results, further investigations are needed with larger patient groups.

REFERENCES

  1. Aghaghazvini L, Sharifian H, Yazdani N, Hosseiny M, Kooraki S, Pirouzi P, Ghadiri A, Shakiba M and Kooraki S. (2018): Differentiation between benign and malignant thyroid nodules using diffusion-weighted imaging, a 3-T MRI study. The Indian journal of radiology & imaging, 28(4):460.
  2. Aslan A, Sancak S, Aslan M, Ayaz E, Inan I, Ozkanli SS, Alimoğlu O and Yıkılmaz A. (2018): Diagnostic value of duplex doppler ultrasound parameters in papillary thyroid carcinoma. Acta Endocrinologica (Bucharest), 14(1):43.
  3. Bozgeyik Z, Onur MR and Poyraz AK. (2013): The role of diffusion weighted magnetic resonance imaging in oncologic settings. Quantitative imaging in medicine and surgery, 3(5):269.
  4. Chen L, Xu J, Bao J, Huang X, Hu X, Xia Y and Wang J. (2016): Diffusion-weighted MRI in differentiating malignant from benign thyroid nodules: A meta-analysis. BMJ Open, 6:e008413.
  5. Floridi C, Cellina M, Buccimazza G, Arrichiello A, Sacrini A, Arrigoni F, Pompili G, Barile A and Carrafiello G. (2019): Ultrasound imaging classifications of thyroid nodules for malignancy risk stratification and clinical management: state of the art. Gland Surgery. 2019 Sep, 8(Suppl 3):S233.
  6. Hoang J. (2010): Thyroid nodules and evaluation of thyroid cancer risk. Australasian journal of ultrasound in medicine. 2010 Nov, 13(4):33.
  7. Ilica AT, Artaş H, Ayan A, Günal A, Emer O, Kilbas Z, Meric C, Atasoy MM, Uzuner O. (2013): Initial experience of 3 tesla apparent diffusion coefficient values in differentiating benign and malignant thyroid nodules. J Magn Reson Imaging, 37:1077-82.
  8. Khizer A, Raza S and Slehria A. (2015): Diffusion-weighted MR imaging and ADC mapping in differentiating benign from malignant thyroid nodules. J Coll Physicians Surg Pak, 25:785-8.
  9. Ly S, Frates MC, Benson CB, Peters HE, Grant FD, Drubach LA, Voss SD, Feldman HA, Smith JR, Barletta J and Hollowell M. (2016): Features and outcome of autonomous thyroid nodules in children: 31 consecutive patients seen at a single center. The Journal of Clinical Endocrinology & Metabolism. 2016 Aug 8, 101(10):3856-62.
  10. Noda Y, Kanematsu M, Goshima S, Kondo H, Watanabe H, Kawada H and Bae KT. (2015): MRI of the thyroid for differential diagnosis of benign thyroid nodules and papillary carcinomas. American Journal of Roentgenology, 204(3):W332-5.
  11. Popoveniuc G and Jonklaas J. (2012): Thyroid nodules. Medical Clinics., 96(2):329-49.
  12. Tezuka M, Murata Y, Ishida R, Ohashi I, Hirata Y and Shibuya H. (2003): MR imaging of the thyroid: correlation between apparent diffusion coefficient and thyroid gland scintigraphy. J Magn Reson Imaging, 17:163– 69.
  13. Wang H, Wei R, Liu W, Chen Y and Song B. (2018): Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules. BMC medical imaging, 18(1):50.
  14. White NS, McDonald CR, Farid N, Kuperman J, Karow D, Schenker-Ahmed NM, Bartsch H, Rakow-Penner R, Holland D, Shabaik A and Bjørnerud A. (2014): Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging. Cancer research, 74(17):4638-52.
  15. Xie C, Cox P, Taylor N and LaPorte S. (2016): Ultrasonography of thyroid nodules: a pictorial review. Insights into imaging, 7(1):77-86.
  16. Zamora EA and Cassaro S. (2018): Thyroid Nodule. InStatPearls [Internet] 2018 Dec 9. StatPearls Publishing. Available at: https://www.ncbi.nlm.nih.gov/books/NBK535422/ Last accessed December 2019.‏

 

دور الرنين المغناطيسى بالانتشار فى حالات التضخم العنقودى للغدة الدرقية

شوقى دعبس، محمد الشافعى، هيثم محمود نفادى*، هيثم أحمد شحاته فتح الباب

قسمى الجراحة العامة والأشعة التشخيصية*، کلية الطب، جامعة الأزهر، القاهرة

الناشر: هيثم احمد شحاته فتح الباب، تليفون: 01224378613،

E-mail: drhaythm48@gmail.com

خلفية البحث: يعتبر التضخم العنقودى للغدة الدرقية أحد الاضطرابات الأکثر شيوعاً التى تصيب الغدة الدرقية. وفي مصر، يحدث تضخم الغدة الدرقية العقدي بنسبة تتراوح بين 4٪-7٪ من السکان. وقد تکون تقنيات التصوير الإشعاعي الجديدة واعدةً للتشخيص التفريقي لعقيدة الغدة الدرقية.

الهدف: تحديد الدور التشخيصي للرنين المغناطيسى بالانتشار في التمييز بين العقيدات الدرقية الخبيثة والحميدة باستخدام دراسة التشريح المرضي کمعيار مرجعي.

المرضى وطرق البحث: شملت الدراسة 35 مريضاً (28 من الإناث و 7 من الذکور)، تتراوح أعمارهم بين (12 سنة إلى 75 سنة) بمتوسط عمر 48.65 (.7 14.72). وقد تم إحالة جميع المرضى إلى قسم الجراحة بمستشفى الحسين الجامعي خلال الفترة من يوليو 2018 إلى أغسطس 2019 مع توسيع الغدة الدرقية.

نتائج البحث: في دراستنا، کان هناک 7 (60.6 ٪) ذکور و 28 (79.4 ٪) إناث في مجموعة المرضى. کانت نسبة العقيدات الدرقية الحميدة 52.9 ٪. کانت النسبة المئوية للعقيدات الدرقية الخبيثة 47.1 ٪. تم حساب قيمة معامل الانتشار الظاهري لکل من العقيدات الحميدة والخبيثة، وکان نطاق قيم ADC للعقيدات الدرقية الحميدة (0.03 إلى 3.5) من المتوسط (1.86 ± 0.82) و (0.5 إلى 1.5) من المتوسط (1.04 ± 0.2) ، کانت قيمة p مهم (<0.0001) مع قيمة مقطوعة 1.2 مع حساسية 93 ٪ وخصوصية 83٪.

الاستنتاج: يوفر الرنين المغناطيسى بالانتشار نتائج مفيدة واعدة للغاية فى حالاات التضخم العنقودى للغدة الدرقية. وقد يکون لهذه النتائج دور في اختيار العقيدات التي کانت ستخضع FNAC. وعلى الرغم من أن هذه النتائج واعدة، إلا أن هناک حاجة لمزيد من الدراسات مع مجموعات أکبر من المرضى.

الکلمات الدالة : الانتشار – التردد المغناطيسى – حبيبات الغدة الدرقية .

  1. REFERENCES

    1. Aghaghazvini L, Sharifian H, Yazdani N, Hosseiny M, Kooraki S, Pirouzi P, Ghadiri A, Shakiba M and Kooraki S. (2018): Differentiation between benign and malignant thyroid nodules using diffusion-weighted imaging, a 3-T MRI study. The Indian journal of radiology & imaging, 28(4):460.
    2. Aslan A, Sancak S, Aslan M, Ayaz E, Inan I, Ozkanli SS, Alimoğlu O and Yıkılmaz A. (2018): Diagnostic value of duplex doppler ultrasound parameters in papillary thyroid carcinoma. Acta Endocrinologica (Bucharest), 14(1):43.
    3. Bozgeyik Z, Onur MR and Poyraz AK. (2013): The role of diffusion weighted magnetic resonance imaging in oncologic settings. Quantitative imaging in medicine and surgery, 3(5):269.
    4. Chen L, Xu J, Bao J, Huang X, Hu X, Xia Y and Wang J. (2016): Diffusion-weighted MRI in differentiating malignant from benign thyroid nodules: A meta-analysis. BMJ Open, 6:e008413.
    5. Floridi C, Cellina M, Buccimazza G, Arrichiello A, Sacrini A, Arrigoni F, Pompili G, Barile A and Carrafiello G. (2019): Ultrasound imaging classifications of thyroid nodules for malignancy risk stratification and clinical management: state of the art. Gland Surgery. 2019 Sep, 8(Suppl 3):S233.
    6. Hoang J. (2010): Thyroid nodules and evaluation of thyroid cancer risk. Australasian journal of ultrasound in medicine. 2010 Nov, 13(4):33.
    7. Ilica AT, Artaş H, Ayan A, Günal A, Emer O, Kilbas Z, Meric C, Atasoy MM, Uzuner O. (2013): Initial experience of 3 tesla apparent diffusion coefficient values in differentiating benign and malignant thyroid nodules. J Magn Reson Imaging, 37:1077-82.
    8. Khizer A, Raza S and Slehria A. (2015): Diffusion-weighted MR imaging and ADC mapping in differentiating benign from malignant thyroid nodules. J Coll Physicians Surg Pak, 25:785-8.
    9. Ly S, Frates MC, Benson CB, Peters HE, Grant FD, Drubach LA, Voss SD, Feldman HA, Smith JR, Barletta J and Hollowell M. (2016): Features and outcome of autonomous thyroid nodules in children: 31 consecutive patients seen at a single center. The Journal of Clinical Endocrinology & Metabolism. 2016 Aug 8, 101(10):3856-62.
    10. Noda Y, Kanematsu M, Goshima S, Kondo H, Watanabe H, Kawada H and Bae KT. (2015): MRI of the thyroid for differential diagnosis of benign thyroid nodules and papillary carcinomas. American Journal of Roentgenology, 204(3):W332-5.
    11. Popoveniuc G and Jonklaas J. (2012): Thyroid nodules. Medical Clinics., 96(2):329-49.
    12. Tezuka M, Murata Y, Ishida R, Ohashi I, Hirata Y and Shibuya H. (2003): MR imaging of the thyroid: correlation between apparent diffusion coefficient and thyroid gland scintigraphy. J Magn Reson Imaging, 17:163– 69.
    13. Wang H, Wei R, Liu W, Chen Y and Song B. (2018): Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules. BMC medical imaging, 18(1):50.
    14. White NS, McDonald CR, Farid N, Kuperman J, Karow D, Schenker-Ahmed NM, Bartsch H, Rakow-Penner R, Holland D, Shabaik A and Bjørnerud A. (2014): Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging. Cancer research, 74(17):4638-52.
    15. Xie C, Cox P, Taylor N and LaPorte S. (2016): Ultrasonography of thyroid nodules: a pictorial review. Insights into imaging, 7(1):77-86.
    16. Zamora EA and Cassaro S. (2018): Thyroid Nodule. InStatPearls [Internet] 2018 Dec 9. StatPearls Publishing. Available at: https://www.ncbi.nlm.nih.gov/books/NBK535422/ Last accessed December 2019.‏