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Egyptian Journal of Radiology and Nuclear Medicine
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Diffusion tensor imaging in characterization of cervical lymphadenopathy

Egyptian Journal of Radiology and Nuclear Medicine volume 54, Article number: 128 (2023) Cite this article

Abstract

Background

It is important to differentiate cervical lymph nodes. So, this study aims to assess the ability of diffusion tensor imaging (DTI) in differentiating cervical lymphadenopathy (LNs).

Materials and methods

This retrospective study was done upon 100 patients with cervical LNs who had DTI over a year period. The fractional anisotropy (FA) and the mean diffusivity (MD) values of LNs were measured.

Results

This study was done upon 100 patients (the mean age 45 ± 2 years (standard deviation [SD]), 63 men). The mean MD and FA of the malignant LNs (0.83 ± 0.14 × 10−3 mm2/s, 0.26 ± 0.07) were significantly different; (P = 0.001) than those of benign LNs (1.32 ± 0.33 × 10–3 mm2/s, 0.22 ± 0.09). MD of 0.94 × 10–3 mm2/s and FA of 0.21 were used to discriminate malignant and benign LNs, AUC 0.892 and 0.758, and 84% and 71% accuracy, respectively. Combined parameters revealed AUC of 0.914 and 81%. The mean MD and FA of the metastatic LNs (0.86 ± 0.12 × 10–3 mm2/s, 0.25 ± 0.07) were statistically different; (P = 0.001, 0.03) than those of lymphomatous nodes (0.66 ± 0.13 × 10–3 mm2/s, 0.28 ± 0.02). The AUC of the MD and FA used to distinguish metastatic from lymphomatous nodes was 0.82, 0.711, (0.71 × 10–3 mm2/s, 0.27) cutoff values, and 95.3%, 73.4% accuracy, respectively. Combined parameters revealed 0.824 AUC, 95.3% accuracy, 98.2% sensitivity, and 75% specificity. There was a significant statistical difference in MD between well-moderately (P = 0.001) versus poorly differentiated metastatic LNs and stages I and II (P = 0.018) versus stages III and IV of metastatic cervical LNs.

Conclusions

Combining FA and MD is a promising technique that can play a major role in distinguishing different categories of cervical LNs.

Background

Assessment of cervical LNs is one of the most challenging issues for the radiologist. When a LN is detected, the radiologist has to delineate whether it is of benign or malignant nature [1, 2]. Ultrasound together with fine-needle aspiration biopsy (FNAC) was widely used, yet it is a relatively invasive, operator-dependent modality that has a tendency to false-positive and -negative results [3, 4]. Differentiation between different types of cervical LNs is difficult with conventional computed tomography (CT) and magnetic resonance (MR) [5,6,7].

Advanced MR imaging modalities as post-contrast, dynamic MRI, MR spectroscopy, as well as arterial spin labeling are used to add in characterization, but unfortunately, their value is relatively limited [7,8,9,10,11]. Advanced CT techniques as CT perfusion and dual-energy CT carry the risks of radiation exposure and contrast administration, and PET-CT is costly, not widely available and of low spatial resolution [12, 13].

Pathological diagnosis is the ideal modality for the evaluation of the nature of cervical LNs, but it may be inconclusive, or of inadequate samples. Fine-needle aspiration biopsy is immensely used, yet it is a relatively invasive, operator-dependent modality with a relatively high rate of inaccurate results [14].

Diffusion-weighted imaging (DWI) is based mainly on the Brownian mobility of water protons in different tissues, which is controlled by the cellular and extra-cellular structures of these tissues, so it has the ability to discriminate different tissues compartments at a cellular level [15,16,17,18,19,20]. Variable studies reported the benefit of DWI in distinguishing between subtypes of LNs in different parts of the body [21,22,23,24,25] and in cervical LNs that might be associated with artifacts in the neck and head region [26,27,28,29,30].

Diffusion tensor imaging (DTI) utilizes the benefit of water flow and diffusivity within tissue within the three primary directions. This motion is relatively decreased in the plane perpendicular to the cellular membranes. The most popular parameters of DTI are FA and MD. The MD delineates the mobility and flow rate of the water molecules as the cellularity within the lesion is the major goal of pathological classification of lesions with DTI. The relation between the degree of cellularity and MD value is negative. The FA delineates the orientation of microstructures and nearby different tissues [31,32,33].

Diffusion tensor evaluates the water protons motion in tissues as well as their spatiality. The protons that move only in a single direction are called anisotropic and their FA equals 1. The protons that move in any direction, in the same manner, are called isotropic and their FA equals 0. A highly cellular tumor is anticipated to have both; low diffusivity and anisotropy as it has limited extra-cellular space, whereas a low cellular tumor that is mostly benign might have relatively higher diffusivity and high anisotropy [34,35,36]. Few studies discuss the reliability of DTI in the valuation of neck and head malignancies, distinguishing recurrent neck and head cancer from post-treatment changes, and characterization of salivary gland tumors [36,37,38,39,40].

Aim of the work is to assess the role of DTI in characterization of cervical LNs.

Materials and methods

Patients

This research was approved by the institutional review board, and the patients’ informed consent was waived because this was a retrospective study. This retrospective study was done on 105 patients from 2019 to 2020 with cervical LNs. The criteria of inclusion were patients with cervical LNs referred for MR suspected to be malignant. Five patients were excluded from this study because of motion artifacts. So, the included patients in the study were 100 patients (63 males and 37 females, with their ages ranging from 18 to 72 years, the mean age 45 ± 2 years). The definitive diagnosis was obtained by surgical biopsy and core biopsy that done 7–14 days after MRI. All patients had routine imaging as well as DTI of the neck and head.

Methods

MR imaging

Magnetic resonance images were obtained on a 1.5-T scanner (Ingenia Philips, Philips Medical Systems, Best Netherlands) with a self-shielding gradient set (30-mTm maximum gradient strength, 120 T/m/s rate) and a 16-channel neurovascular coil. All the patients had T1-weighted image (TR/TE = 800/15 ms) and T2-fast spin-echo weighted image (TR/TE = 6000/80 ms) with these parameters; thickness of section 5 mm, gap between slices 1.5 mm, a field-of-view (FOV) 25–30 cm2, and matrix of acquisition 256 × 224.

Diffusion tensor imaging

Diffusion tensor MR imaging of the neck has been obtained through a single-shot echo-planar sequence (TR/TE 3200/90 ms) together with parallel imaging. Gradients of diffusion had been acquired along 32 axes, b-values of 0 and 1000 s/mm2 were used, matrix = 92 × 88, FOV = 250 × 170 mm2, and voxel dimensions = 2.43 × 2.54 × 2.5 mm3. Forty-eight slices have been obtained, with a thickness of 2.5 mm, no gap. The full scan time was about 7–8 min.

Image analysis

Analysis of the images was carried out by one neck and head radiologist with 10 years’ experience who was blind to the definitive results. Co-registration of DTI maps with T2 images was performed for correct positioning of the region of interest (ROI). The largest LN that showed homogenous texture in each patient was selected for analysis. The ROI was placed at DTI map-guided with T2-weighted imaging around the inner border of the LN using the electronic cursor (Fig. 1). The size of the ROI varied between 4.4 and 19.6 mm2 (mean 9.4 mm2).

Fig. 1
figure 1

Region of interest definition of metastatic cervical LNs. A Axial T2 image displays enlarged left-sided neck LN. B DTI map displays enlarged cervical LN with calculated MD value of 0.91 × 10−3mm2/s and FA of 0.53

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Statistical analysis

The statistical analysis of these data was done using the Statistical Package for the Social Sciences version 22 (SPSS Inc., Chicago, Ill, USA). The mean and standard deviation of MD and FA of metastatic, lymphomatous, and benign LNs have been measured. Analysis of these data was done so as to test the significant statistical differences. The Student's t-test has been used for comparison between MD and FA of LNs. P-value < / = 0.05 was considered significant. The receiver operating characteristic (ROC) curve was used to detect the cutoff points of MD and FA utilized in differentiating malignant from benign LNs and metastatic from lymphomatous LNs with computation of area under the curve (AUC), accuracy, specificity, and sensitivity.

Results

The causes of LNs pathogenesis were; metastatic (n = 56), lymphoma (n = 8), and benign (n = 36). Metastatic LNs were secondary to squamous cell carcinoma of the mouth; (n = 24), larynx; (n = 8), oropharynx; (n = 8), nasopharynx; (n = 8), and sino-nasal; (n = 8). Lymphomatous nodes were non-Hodgkin’s lymphoma (NHL); (n = 5) and Hodgkin’s disease (HD); (n = 3). Benign LNs were reactive (n = 32) and granulomatous (n = 4) either sarcoidosis (n = 2) or tuberculous (n = 2). Table 1 shows MD and FA of cervical LNs. Table 2 shows the ROC results with cutoff points of MD and FA used in differentiating cervical LNs.

Table 1 Mean, SD, minimum, and maximum of MD (× 10–3 mm2/s) and FA of malignant, benign, metastatic, lymphomatous, well-moderately, poorly differentiated, and different stages of cervical lymph nodes
Full size table
Table 2 Validity of MD (× 10–3 mm2/s) and FA in characterization of cervical lymph nodes
Full size table

The mean MD of malignant LNs (0.83 ± 0.14 × 10−3 mm2/s) was statistically lower than (P = 0.001) the MD of benign LNs (1.32 ± 0.33 × 10−3 mm2/s). The mean FA of malignant cervical LNs (0.26 ± 0.07) was statistically higher than (P = 0.02) that of benign cervical LNs (0.22 ± 0.09). The cutoff points of MD and FA used to discriminate benign from malignant LNs were 0.94 × 10−3 mm2/s, 0.21 with AUC equals 0.892, 0.758, accuracy of 84%, 71%, specificity of 88.9%, 50%, and sensitivity of 81.2%, 82.8%, respectively. Combined parameters revealed AUC of 0.914, with accuracy of 81%, specificity of 77.8%, and sensitivity of 92.2% (Fig. 2).

Fig. 2
figure 2

ROC curve of MD and FA of malignant versus benign LNs. A The cutoff value of MD utilized to distinguish malignant from benign LNs is 0.94 × 10–3 mm2/s with AUC of 0.892, an accuracy of 84%, specificity of 88.9%, and sensitivity of 81.2%. B The cutoff value of FA applied to distinguish malignant from benign LNs is 0.21 with an AUC 0.758, an accuracy 71%, specificity 50%, and sensitivity 82.8%. C Combined parameters show AUC 0.914, with an accuracy 81%, specificity 77.8%, and sensitivity 92.2%

Full size image

The mean MD of metastatic LNs (0.86 ± 0.12 × 10−3 mm2/s) was statistically higher than (P = 0.001) the lymphoma (0.66 ± 0.13 × 10−3 mm2/s). The mean FA of metastatic LNs (0.25 ± 0.07) was statistically lower than (P = 0.03) lymphomatous nodes (0.28 ± 0.02). On the ROC curve; the MD and FA area under curve used to distinguish metastatic from lymphomatous nodes were 0.82, 0.711 with the cutoff values of 0.71 × 10–3 mm2/s, 0.27, an accuracy of 95.3%, 73.4%, sensitivity of 98.2%, 76.8%, and specificity of 75%, 50%, respectively. Combined parameters resulted in an AUC of 0.824, with an accuracy of 95.3%, sensitivity 98.2%, and specificity 75% (Fig. 3).

Fig. 3
figure 3

ROC curve of MD and FA of metastatic versus lymphomatous LNs. A The cutoff value of MD applied to distinguish metastatic LNs from lymphomatous LNs is 0.71 × 10–3 mm2/s with AUC 0.820, an accuracy 95.3%, sensitivity 98%, and specificity 75%. B The cutoff value of FA applied to distinguish metastatic from lymphomatous LNs is 0.27 with AUC 0.711, accuracy 73.4%, sensitivity 76.8%, and specificity 50%. C Combined parameters show AUC of 0.824, with accuracy 95.3%, sensitivity 98.2%, and specificity 75%

Full size image

The mean MD of well-moderately differentiated metastatic LNs (n = 39) (0.81 ± 0.06 × 10–3 mm2/s) was statistically less than (P = 0.001) the poorly differentiated metastatic LNs (n = 17), (0.96 ± 0.16 × 10–3 mm2/s). The mean FA of well-moderately differentiated (0.26 ± 0.08) was higher than (P = 0.23) that of poorly differentiated metastatic LNs (0.23 ± 0.04) but did not reach a significant value (P = 0.23). On ROC curve analysis, the AUC of MD utilized to distinguish well-moderately from poorly differentiated metastatic LNs was 0.767 with cutoff point 0.84 × 10–3 mm2/s, an accuracy 80.4%, sensitivity 76.5%, and specificity 82.1% (Fig. 4).

Fig. 4
figure 4

ROC curve of MD for the degree of differentiation of metastatic LNs. The cutoff value of MD applied to categorize well-moderate from poorly differentiated metastatic LNs is 0.84 × 10–3 mm2/s, AUC 0.767, accuracy 80.4%, sensitivity 76.5%, and specificity 82.1%

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The mean of MD of metastatic LNs with stages I and II (n = 15) [0.88 ± 0.13 × 10–3 mm2/s] was more (P = 0.018) than LNs of stages III and IV malignancy (n = 41) [0.80 ± 0.05 × 10–3 mm2/s]. The mean FA of metastatic LNs with stages I and II (0.22 ± 0.04) was lower than the metastatic LNs with stages III and IV (0.26 ± 0.08) but not reach to a significant level (P = 0.69). The AUC of MD utilized to distinguish metastatic LNs of stages I, II and stages III, IV was 0.72 with a cutoff point 0.78 × 10–3 mm2/s, an accuracy 76.8%, sensitivity 82.9%, and specificity 60% (Fig. 5).

Fig. 5
figure 5

ROC curve of MD for the staging of metastatic LNs. The cutoff value of MD 0.78 × 10–3 mm2/s used to differentiate metastatic LNs of stages I, II and stages III, IV with AUC 0.72, accuracy 76.8%, sensitivity 82.9%, and specificity 60%

Full size image

Discussion

The major parameters in this research were MD, and FA were used for characterization of cervical LNs. Malignant cervical LNs had restricted diffusion with higher FA, and benign LNs had unrestricted diffusion with lower FA. Metastatic cervical LNs revealed a higher MD and lower FA compared to lymphomatous nodes, and there was a significant statistical difference between MD of well-moderately differentiated versus poorly differentiated metastatic nodes and between metastatic nodes with stages I and II versus stages III and IV.

Malignant cervical LNs showed decreased MD compared with reactive cervical LNs, likely because of the more free water motion in reactive and the more cellularity in malignant LNs. As diffusion within malignant cervical LNs is blocked by the cellular membranes and macromolecular structures, benign LNs show limited cell mitosis rendering reduced cell concentration that would be evaluated as an elevation in the MD parameter for the enlarged LN [9,10,11, 23]. Previous studies concluded that MD for malignant tumors is less than those for benign lesions [35,36,37,38,39,40]. Another research found that qualitative DWI together with the conventional images allowed diagnosis of malignant LNs with sensitivity 94% and specificity 100% [38].

In this study, FA of malignant LNs was remarkably higher than benign cervical LNs. This may be attributed to that malignant LNs have high cellularity, which may lead the water molecules to flow and diffuse with a higher degree of directionality, in the contrary to non-neoplastic cervical LNs. One recent study stated that there was a significant statistical variation in FA of oral cancer compared to the normal tissue and between metastatic and non-metastatic cervical LNs [34]. Another research added that recurrent neck and head malignancy shows a more FA than post-treatment changes [38]. The combination of MD and FA of the LNs revealed the highest accuracy in distinguishing benign from malignant LNs. A recent study reported that combined MD and FA help in the definition of parotid neoplasms and differentiation of parotid malignancy from benign tumors [40].

In this research, there was a significant statistical variation in MD and FA between metastatic and lymphomatous LNs with lower MD and more FA of lymphomatous than metastatic LNs, and combination of FA and MD increased accuracy for distinguishing metastatic from lymphomatous nodes. The previous studies reported that lymphomatous nodes showed lower MD as lymphoma has greater cellularity, much larger nuclei and also, and little extra-cellular space compared to metastatic nodes [41,42,43].

In this study, there was a significant variation in MD between well-moderately versus poorly differentiated and lower versus high stages of metastatic LNs; however, the FA showed an insignificant difference. The degree of differentiation and staging of cervical LNs in patients with head and cervical malignancy are important for treatment planning and prognosis with a bad prognosis of poorly differentiated and higher stage of metastatic LNs [2,3,4,5]. The previous studies reported significant statistical variation in MD of the different stages and grading of cervical LNs due to increased cellularity and fewer spaces within metastatic LNs with a higher stage and less degree of differentiation [26,27,28,29,30]. One recent study stated that there was a significant inverse correlation of FA with histological grades of metastatic cervical LNs [34].

There were a number of limitations to this study. Firstly, the patients’ population was a small collection of LNs with different pathologies. Future multi-center study with a larger group of patients who study DTI of certain pathology would make results more appropriate. Second, the study only used DTI metrics, future studies discussing multi-parametric imaging together with dynamic susceptibility perfusion-weighted MR imaging [44,45,46], arterial spin labeling [47,48,49], and MR spectroscopy [50] with an application of artificial intelligence and deep learning [51, 52] will improve the accuracy of MR imaging in the characterization of cervical LNs.

Conclusions

Combination of MD and FA is useful non-invasive imaging parameters which could be helpful in characterization of metastatic, lymphomatous, and benign LNs, and MD has a role in grading and staging of metastatic cervical LNs.

Availability of data and materials

On reasonable request.

Abbreviations

AUC:

The area under curve

CT:

Computed tomography

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

FA:

Fractional anisotropy

LNs:

Lymphadenopathy/lymph nodes

MR:

Magnetic resonance

MD:

Mean diffusivity

ROC:

Receptor operating characteristic

ROI:

Region of interest

SD:

Standard deviation

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Acknowledgements

Not applicable.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Mansoura University, Elgomhoria Street, Mansoura, 35516, Egypt

    Zainab A. Ramadan & Ahmed Abdel Razek

Authors
  1. Zainab A. Ramadan
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  2. Ahmed Abdel Razek
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Each author has participated sufficiently in this work. All authors have read and approved the manuscript.

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Correspondence to Zainab A. Ramadan.

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Institutional research board approval was obtained, and informed consent from the patients was waived because this is a retrospective study.

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Ramadan, Z.A., Razek, A.A. Diffusion tensor imaging in characterization of cervical lymphadenopathy. Egypt J Radiol Nucl Med 54, 128 (2023). https://doi.org/10.1186/s43055-023-01074-1

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  • DOI: https://doi.org/10.1186/s43055-023-01074-1

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