Current Medical Issues

: 2023  |  Volume : 21  |  Issue : 2  |  Page : 93--97

Diffusion-weighted imaging in differentiating benign versus malignant lymphadenopathy: A cross-sectional study

KM Sumith1, T Vinoth2, P Jenikar3, M Vasantha Kumar3,  
1 Department of Radiology, SRM Institute of Medical Sciences, Chennai, Tamil Nadu, India
2 Department of Radiology, ACS Medical College and Hospital, Chennai, Tamil Nadu, India
3 Department of Radiology, Shri Sathya Sai Medical College and Research Institute, Kanchipuram, Tamil Nadu, India

Correspondence Address:
Dr. M Vasantha Kumar
No. 140, Third Main Road, Ring Road Housing Sector, Madhavaram, Chennai - 600 060, Tamil Nadu


Background: Lymphadenopathy requires differentiation into benign and malignant for appropriate management. The current study was done to find out if diffusion-weighted images and apparent diffusion coefficient (ADC) will be able to differentiate benign from malignant cervical lymphadenopathy. Methods: This cross-sectional study was done in the Department of Radiology at Sri Ramachandra Medical College from April 2016 to August 2018. A total of 54 patients with a history of lymphadenopathy were recruited. Histopathological examination (HPE) and magnetic resonance imaging were done for all patients after a complete history and physical examination. The ADC was correlated with HPE in differentiating benign and malignant lymphadenopathy. Results: Majority (46.30%) were in the age group of 51 years and above. Twenty-eight (51.85%) had benign, whereas 26 (48.15%) had malignant lesions. There was a statistically significant difference between the nature of the lesion in ADC value ([× 10–3 mm2/s] [P < 0.001]). The ADC had good predictive validity in predicting malignancy, as indicated by the area under the curve of 0.904 (95% confidence interval 0.821 to 0.987, P < 0.001). Conclusion: ADC values can be used as a complementary tool in assessing the malignant potential of lymph nodes in various conditions and hence play an essential role in the further course of management.

How to cite this article:
Sumith K M, Vinoth T, Jenikar P, Kumar M V. Diffusion-weighted imaging in differentiating benign versus malignant lymphadenopathy: A cross-sectional study.Curr Med Issues 2023;21:93-97

How to cite this URL:
Sumith K M, Vinoth T, Jenikar P, Kumar M V. Diffusion-weighted imaging in differentiating benign versus malignant lymphadenopathy: A cross-sectional study. Curr Med Issues [serial online] 2023 [cited 2023 Jun 8 ];21:93-97
Available from:

Full Text


Several pathological processes can occur in a lymph node. Lymph nodes are the common places for the occurrence of cancer metastasis, development of lymphoma, and reactive enlargement in various conditions such as tuberculosis.[1] The distinction between the neoplastic status of the lymph nodes during diagnosis decides the patient's prognosis and is important for treatment.[2] However, the differentiation is challenging.[3]

Color Doppler ultrasound is one of the methods used to differentiate benign from malignant lymphadenopathy. The advantage of color Doppler is a noninvasive procedure, with good sensitivity and it can detect even small vessels in nodes.[4] Diffusion-weighted imaging (DWI) and magnetic resonance imaging (MRI) are also alternative diagnostic modalities used for the identification and differentiation of nodes. It measures diffusion in lymph nodes by measuring the apparent diffusion coefficient (ADC). Signal intensity is directly related to the degree of diffusion and the ADC is the quantitative measure of diffusion in DWI.[5] The solid part of malignant tumor cells grew actively and arranged closely. The biofilm structure clearly limits the diffusion of water molecules, the ADC value decreases, and DWI shows a high signal. In contrast, benign tumors have low cell density, relatively large ADC values, and relatively low signal on DWI.[5] Therefore, DWI can help differentiate benign and malignant lymphadenopathy.[6],[7]

Due to high cell density in normal lymph nodes, they have a relatively restricted diffusion (low ADC). In metastatic lymph nodes, high necrotic areas lead to restriction in diffusion, thereby having high ADC.[1],[8]

DWI is helpful in oncologic imaging for the description and categorization of tumors and for distinguishing benign and malignant lesions.[9],[10] It has high sensitivity, low cost, no radiation, and can detect tumor lesions sensitively, and has been widely applied in clinical practice. The clear value of the ability of DWI to highlight the differentiation will significantly improve the clinical response in tumor management. Thus, this study was conducted to find the role of ADC values in differentiating between benign and malignant lesions on DWI.


Study design

This was a cross-sectional study.

Study area and study duration

This study was conducted in the Department of Radiodiagnosis at Sri Ramachandra medical college from April 2016 to August 2018.

Study participants

Fifty-four patients with features of lymphadenopathy referred to the department for radio diagnosis were recruited in this study.

Inclusion criteria

Patients with histopathological examination (HPE) proven malignancy who underwent MRI for staging purposesPatients with suspicious malignancy who underwent MRI later confirmed by HPEIncidentally detected masses in MRI which was later proven by HPE for malignancyPatients with HPE reported benign lymphadenopathy.

Exclusion criteria

Patients with contraindications to MRI such as pacemakers, ferromagnetic surgical clips/staples, cochlear implants, and any metallic foreign bodiesPatients in whom final diagnosis/HPE reports were not available.


A complete history and HPE reports were collected from the patients. The procedure of MRI was explained to the patient, and MRI was taken. GE SIGNA HDxT 1.5T- SIEMENS MAGNETOM AVANTO 1.5T machine was used for MRI. The imaging was done with the patient in the supine position; coil and orientation were adjusted based on the location of the lymphadenopathy. DWI (b value) was 0, 800, and 1000. The ADC map was displayed in grayscale from the DWI sequence. Following the appearance of the ADC map, the ADC values were calculated by placing two Region of Interest (ROI) of area 40 ± 10 mm2 on the lymph nodes. The average of these values provided the mean ADC value. ADC was then computed automatically by the present software (ARGUS IN SIEMENS and FUNCTOOL version 4.4 in GE). Findings on conventional MRI were correlated with findings on DWI (with ADC values). Four examples are these are shown in [Figure 1], [Figure 2], [Figure 3], [Figure 4].{Figure 1}{Figure 2}{Figure 3}{Figure 4}

Ethical consideration

The institutional human ethics committee has approved this study [Reference CSP-MED/16/JUN/29/66] and informed written consent was obtained from all participants. Data confidentiality was maintained.

Sample size calculation

The current study's sample size calculation was done under the assumption that the expected area under the receiver operating characteristic (ROC) curve for the ADC value in predicting malignancy is 0.83 as per the study by Luo et al.[11] The null hypothesis value of the area under the ROC curve was considered as 0.75, and 1:1 sample size ratio in the negative and positive groupS was considered. The alpha error of 5% and 99% power were considered. Based on these calculations, the sample size needed was 18. With consideration for the nonparticipation rate of about 20%, the final sample size arrived was 22, positive and negative each. Sample size calculation was done using coGuide software version 1.03.[12]

Statistical methods

The primary outcome study variable was ADC lesion. The nature of the lesion (benign vs. malignant) was the primary explanatory variable. The descriptive analysis of the data was done through mean and standard deviation for continuous variables, frequency, and proportion for categorical variables. The ability of nomograms to distinguish the models was evaluated by the area under the curve (AUC) of the ROC curve. The sensitivity, specificity, predictive values, and diagnostic accuracy of the screening test with the decided cutoff values along with their 95% confidence interval (CI) were presented. Statistical significance was considered for P < 0.05. Data were analyzed using coGuide software, version 1.03.[12]


The final analysis was done in 54 participants. The majority (46.30%, 25/54) were in the age group of 51 years and above, followed by the age group 41–50 years 13 (24.07%). Males were 20 (37.04%), and 34 patients (62.96%) were female, 28 (51.85%) had benign and 26 (48.15%) had malignant lesions, 18 (33.33%) had positive HPE lymph nodes, 5 (9.26%) were breast cases, 17 (31.48%) were head-and-neck cases, and 9 (16.67%) had hepatobiliary and pancreas lesions. The mean node length was 9.83 ± 1.26 cm, ranging from 8 to 15 cm. The mean node width was 6.78 ± 1.64, ranging from 4 to 10. The mean overall node dimension was 67.56 ± 22.81 cm, ranging from 36 to 150 [Table 1].{Table 1}

There was a statistically significant difference in ADC value (× 10-3 mm2/s) between the malignant and benign lesions (P < 0.001). Out of 26 participants in malignant lesions, 7.69% (2/26) had breast, git, hepatobiliary, and pancreas lesions, respectively, 34.62% (9/26) were gynecology cases, 38.46% (10/54) were head-and-neck cases, and 7.69% (1/26) were hematology cases. Out of 28 participants in benign, 10.71% (3/28) had breast lesions, 3.57% (1/28) had GIT, 35.71% (10/28) had gynecology lesions, and 25% (7/28) had head-and-neck and hepatobiliary and pancreas for each. Among the malignant group, 21 (80.77%) had ADC ≤0.98, and only 5 (19.23%) had ADC >0.98. Among the benign group, 8 (28.57%) had ADC ≤0.98, and only 20 (71.43%) had ADC >0.98. The difference in the proportion of ADC between the nature of the lesion was statistically significant (P < 0.001) [Table 2]. The ADC had a sensitivity of 80.77% (95% CI 60.65% to 93.45%) in predicting the malignant nature of the lesion. Specificity was 71.43% (95% CI 51.33% to 86.78%), false positive rate was 28.57% (95% CI 13.22% to 48.67%), false negative rate was 19.23% (95% CI 6.55% to 39.35%), positive predictive value was 72.41% (95% CI 52.76% to 87.27%), negative predictive value was 80% (95% CI 59.30% to 93.17%), and the total diagnostic accuracy was 75.93% (95% CI 62.36% to 86.51%) [Table 2].{Table 2}

The ADC had good predictive validity in predicting malignant, as indicated by the AUC of 0.904 (95% CI 0.821 to 0.987, P < 0.001) [Figure 5].{Figure 5}


The differentiation between malignant and benign lymphadenopathy is essential not only for prognosis but also for treatment and further follow-up.[13],[14],[15] This current study aimed to assess the role of DWI in differentiating benign and malignant lymphadenopathy.

The current study findings show that the ADC lesion had good predictive validity in predicting malignancy, as indicated by the AUC of 0.904. Among the malignant group, 21 (80.77%) had ADC ≤0.98, and only 5 (19.23%) had ADC >0.98. This shows that ADC values below the cutoff of 0.98 had malignant lesions.

In this current study, the mean ADC value was 0.83 ± 0.16 × 10 − 3 mm2/s in malignant lesions and 1.1 ± 0.11 × 10 − 3 mm2/s in benign lesions. This was similar to the study conducted by Taha Ali et al. in 2012 on the characterization of neck lymph nodes with diffusion-weighted MRI and ADC. In this study, the benign neck nodes have a mean ADC of (1.51 ± 0.36 × 10 − 3 mm2/s) which was significantly higher than those of the metastatic (0.92 ± 0.13 x 10 − 3 mm2/s).[16] Our findings were comparable to a study conducted by Akduman et al. in 2008, where the researchers did a comparison in abdominal lymph nodes on diffusion-weighted imaging. In this study, 40 benign and 16 malignant abdominal nodes were studied. The findings showed that the ADC value of benign nodes was 2.38 ± 0.29 and malignant nodes was 1.84 ± 0.37 × 10 − 3 mm2/s.[17]

The current study shows that ADC had 80.77% sensitivity and 71.43% specificity in predicting malignant lesions in DWI with 0.98 × 10 − 3 mm2/s as a cutoff value. It can be concluded in our study that a lymph node showing an ADC value of <0.9 × 10 − 3 mm2/s on the DWI sequence has a higher probability of being malignant. However, a precise measurement of the ADC value with small error remains a challenge, particularly in cases with small lymph nodes, due to MRI artifacts. Enhancements can be expected by reducing image distortion and techniques to increase the field strength to improve the signal-to-noise ratio.[11],[18],[19]


The current study shows that ADC had 80.77% sensitivity and 71.43% specificity in predicting malignant lesions in DWI with 0.98 × 10 − 3 mm2/s as a cutoff value. We can hence state that there is a significant drop in ADC values of the malignant lymph nodes as supposed to the benign cases. This can be utilized in the diagnosis of cervical lymphadenopathy.

Limitation of the study

The limitations of the current study are its comparatively small sample size and descriptive nature and thereby causality cannot be proved. Hence, extensive sample-size studies involving a varied pool of benign and malignant cases are recommended in the future.


DWI and ADC can differentiate benign from malignant cervical lymphadenopathy. Hence, this can be used during the management of cervical lymphadenopathy.

Ethical statement

The institutional human ethics committee has approved this study [Reference CSP-MED/16/JUN/29/66], and informed written consent was obtained from all participants. Data confidentiality was maintained.


We acknowledge the technical support in data entry, analysis, and manuscript editing by “Evidencian Research Associates.”

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1Gupta A, Rahman K, Shahid M, Kumar A, Qaseem SM, Hassan SA, et al. Sonographic assessment of cervical lymphadenopathy: Role of high-resolution and color Doppler imaging. Head Neck 2011;33:297-302.
2Sumi M, Sakihama N, Sumi T, Morikawa M, Uetani M, Kabasawa H, et al. Discrimination of metastatic cervical lymph nodes with diffusion-weighted MR imaging in patients with head and neck cancer. AJNR Am J Neuroradiol 2003;24:1627-34.
3Kwee TC, Takahara T, Ochiai R, Nievelstein RA, Luijten PR. Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): Features and potential applications in oncology. Eur Radiol 2008;18:1937-52.
4Castelijns JA, van den Brekel MW. Imaging of lymphadenopathy in the neck. Eur Radiol 2002;12:727-38.
5Sumi M, Van Cauteren M, Nakamura T. MR microimaging of benign and malignant nodes in the neck. AJR Am J Roentgenol 2006;186:749-57.
6Steinkamp HJ, Teichgräber UK, Mueffelmann M, Hosten N, Kenzel P, Felix R. Differential diagnosis of lymph node lesions. A semiquantitative approach with power Doppler sonography. Invest Radiol 1999;34:509-15.
7Adibelli ZH, Unal G, Gül E, Uslu F, Koçak U, Abali Y. Differentiation of benign and malignant cervical lymph nodes: Value of B-mode and color Doppler sonography. Eur J Radiol 1998;28:230-4.
8Kwee TC, Takahara T, Luijten PR, Nievelstein RA. ADC measurements of lymph nodes: Inter- and intra-observer reproducibility study and an overview of the literature. Eur J Radiol 2010;75:215-20.
9Dunn DP, Kelsey NR, Lee KS, Smith MP, Mortele KJ. Non-oncologic applications of diffusion-weighted imaging (DWI) in the genitourinary system. Abdom Imaging 2015;40:1645-54.
10Koh DM, Collins DJ. Diffusion-weighted MRI in the body: Applications and challenges in oncology. AJR Am J Roentgenol 2007;188:1622-35.
11Luo N, Su D, Jin G, Liu L, Zhu X, Xie D, et al. Apparent diffusion coefficient ratio between axillary lymph node with primary tumor to detect nodal metastasis in breast cancer patients. J Magn Reson Imaging 2013;38:824-8.
12BDSS Corp. Released 2020. Coguide Statistics Software, Version 1.0.3. India: BDSS corp; 2020. Available from: [Last accessed on 2022 Aug 10].
13Sumi M, Ichikawa Y, Nakamura T. Diagnostic ability of apparent diffusion coefficients for lymphomas and carcinomas in the pharynx. Eur Radiol 2007;17:2631-7.
14Nakayama T, Yoshimitsu K, Irie H, Aibe H, Tajima T, Shinozaki K, et al. Usefulness of the calculated apparent diffusion coefficient value in the differential diagnosis of retroperitoneal masses. J Magn Reson Imaging 2004;20:735-42.
15King AD, Ahuja AT, Yeung DK, Fong DK, Lee YY, Lei KI, et al. Malignant cervical lymphadenopathy: Diagnostic accuracy of diffusion-weighted MR imaging. Radiology 2007;245:806-13.
16Taha Ali TF. Neck lymph nodes: Characterization with diffusion-weighted MRI. Egypt J Radiol Nucl Med 2012;43:173-81.
17Akduman EI, Momtahen AJ, Balci NC, Mahajann N, Havlioglu N, Wolverson MK. Comparison between malignant and benign abdominal lymph nodes on diffusion-weighted imaging. Acad Radiol 2008;15:641-6.
18Holzapfel K, Duetsch S, Fauser C, Eiber M, Rummeny EJ, Gaa J. Value of diffusion-weighted MR imaging in the differentiation between benign and malignant cervical lymph nodes. Eur J Radiol 2009;72:381-7.
19de Bondt RB, Hoeberigs MC, Nelemans PJ, Deserno WM, Peutz-Kootstra C, Kremer B, et al. Diagnostic accuracy and additional value of diffusion-weighted imaging for discrimination of malignant cervical lymph nodes in head and neck squamous cell carcinoma. Neuroradiology 2009;51:183-92.