Skip to main content
Egyptian Journal of Radiology and Nuclear Medicine
Download PDF

Role of diffusion-weighted MRI in evaluation of pediatric musculoskeletal soft tissue masses

Egyptian Journal of Radiology and Nuclear Medicine volume 52, Article number: 263 (2021) Cite this article

Abstract

Background

In pediatric patients, soft tissue masses encompass a wide heterogeneous group of benign and malignant lesions. MRI is a powerful diagnostic tool in the workup of soft tissue tumors in children, and it helps in characterization of lesion and evaluation of the extent of the lesion. However, conventional MRI techniques are not specific in differentiating benign from malignant lesions. So to improve characterization of tumors, DWI was added to MRI techniques as it increases sensitivity and specificity by detecting the micro-diffusion changes of water into intra- and extracellular spaces. The aim of this work was to highlight the diagnostic value of DWI in detection and characterization of different musculoskeletal soft tissue masses in pediatrics.

Results

There was a statistically significant difference regarding the mean ADC value of benign and malignant masses (P value = 0.001*). The mean ADC value for all benign masses (n = 41) was 1.495 ± 0.55 SD × 10–3 mm2/s, while the mean ADC value for all malignant masses (n = 21) was 0.449 ± 0.27 SD × 10–3 mm2/s. The cutoff ADC value between benign and malignant masses was 0.88 × 10–3 mm2/s. This cutoff ADC value has sensitivity of 100.0%, specificity of 92.3%, PPV of 66.7%, NPV of 100.0% and diagnostic accuracy of 93.3%.

Conclusion

In pediatric patients, DWI is an innovative valuable noninvasive imaging technique for characterization of musculoskeletal soft tissue masses and discrimination between benign and malignant masses.

Background

Soft tissue masses and osseous lesions are commonly encountered in pediatric patients. Musculoskeletal soft tissue masses encompass a wide heterogeneous group of benign and malignant lesions that developed from connective tissue other than bone. Although benign in most cases, a significant proportion of pediatric musculoskeletal soft tissue masses constitutes malignant tumors [1]. Patients who have a soft tissue tumor or tumor-like mass usually presented with non-specific clinical findings such as soft tissue swelling or pain. In many cases, a detailed history and physical examination are sufficient to make a diagnosis, but in circumstances when the diagnosis is uncertain, imaging can be very helpful to characterize the mass [2].

MRI is a powerful diagnostic tool in the workup of soft tissue tumors in children due to its high spatial and contrast resolution [3]. However, there is often an overlap in the MRI appearance of benign and malignant soft tissue tumors, resulting in decreasing the diagnostic confidence. The specificity of conventional MRI in differentiating benign from malignant soft tissue tumors was variable among previous studies, presumably because benign and malignant soft tissue tumors may share the same morphologic features regarding the size, margin, location, signal intensities and contrast enhancement [4, 5].

So to improve characterization of tumors, DWI was added to MRI techniques as it increases sensitivity and specificity by detecting the micro-diffusion changes of water into intra- and extracellular spaces [4]. DWI is a non-enhanced functional MRI technique that reflects differences in the Brownian motion of water caused by variations in tissue microstructure. The apparent diffusion coefficient (ADC) is a quantitative measure of Brownian motion: Low ADC values reflect highly cellular microenvironment in which diffusion is limited by an abundance of cell membranes, whereas high ADC values are observed in cellular regions that allow free diffusion of water molecules. Thus, DWI offers quantitative functional assessment of cellularity at the molecular level, with the potential to help differentiate between benign and malignant lesions [6, 7].

DWI was considered an innovative imaging technique for characterization of musculoskeletal lesions which is particularly suitable for pediatric patients and is expected to aid in decision making to perform or postpone biopsy with higher diagnostic confidence. Also, it has particular value to avoid invasive diagnostic procedures in benign pathologies [8]. Several previous studies [6, 9,10,11] evaluated the role of DWI in assessment of soft tissue tumors; to the best of our knowledge, this study is the first to examine musculoskeletal soft tissue masses (benign masses as hemangioma, lymphangioma and Baker’s cyst and malignant masses as rhabdomyosarcoma, lymphoma and Ewing sarcoma) in pediatric population using DWI specifically.

Aim of the work

The purpose of this study was to highlight the diagnostic value of diffusion-weighted imaging (DWI) in detection, delineation and characterization of different musculoskeletal soft tissue masses in pediatric population.

Methods

Patient’s demographic data

This prospective study was approved by our institutional ethics committee, and subjects agreed to participate and publicate images in this study with a written consent obtained from their guardians. This study was performed during the period from December 2019 till June 2021. It included 62 pediatric patients, 37 females (59.7%) and 25 males (40.3%), and their age ranged from 2 to 16 years (mean ± SD = 5.39 ± 5.31 SD). According to the results of histopathological examinations, we had 41 benign lesions and 21 malignant lesions.

Inclusion criteria

Inclusion criteria are as follows: pediatric patients clinically suspected to have musculoskeletal soft tissue masses.

Exclusion criteria

Exclusion criteria are as follows: uncooperative patients or those who had contraindications to magnetic resonance imaging as patients with cochlear implants or prosthetic valve, patients who received chemotherapy or radiotherapy prior to the MRI study and patients who underwent total excision of the tumor or excisional biopsy prior to the MRI study.

MRI technique

MRI examinations were performed for all patients using a 1.5-T MRI unit (Acheiva; Philips Medical Systems, Best, Netherlands). Throughout the examination, most of cases were in supine position using a body phased-array coil, a standard head and neck coil, flexed coil or different surface coils according to patient’s size and examined portion. Before DWI, all patients had routine pulse sequences for conventional non-contrast T1-weighted fast spin-echo images, T2-WI, STIR or fat sat MRI images which were obtained in different planes.

  • The predetermined examination protocol was applied to all patients that included the following:

    1. (1)

      T1WI (TR/TE = 400–700/14–30, FOV 20–35) and T2WI (TR/TE = 2800–4500/80–120, FOV 20–35) in axial, coronal and /or sagittal planes, slice thickness ranging from 4 to 10 mm, interslice gap of 2–3 mm, the matrix for all sequences till 512 * 512.

    2. (2)

      At least one fat-saturated sequence or short inversion recovery (STIR) sequence (TR/TE = 4000–5600/18–40, FOV 20–35).

  • The mean time of conventional MR examination was approximately 15–20 min.

    • DWI was obtained in the axial plane by using a non-breath-hold single-shot spin-echo EPI sequence using different image parameters considering (repetition time, echo time, number of signals acquired, percent phase, field of view, slice thickness, gap, flip angle, echo-train length and matrix) according to site and size of each mass, e.g., shoulder mass parameters (TR/TE = 692/69, flip angle 90°, slice thickness 6 mm, gap 0.6 mm., FOV 22, EPI factor 25). The b-values used in this study were 0, 400 and 800 s/mm2. The mean time of the DW examination for each patient was approximately 3–5 min.

  • ADC maps were automatically generated by the MRI software on the basis of the images obtained. On ADC maps, regions of interest (ROI) were manually positioned and placed over the most homogenous portion of solid and cystic parts of the musculoskeletal soft tissue masses to determine the ADC value, and then, all values were automatically calculated and expressed by × 10−3mm2/s.

  • The representative value used in data and statistical analysis was the mean value.

  1. 1.

    Image analysis DWI analysis included qualitative and quantitative analysis.

  1. A.

    Qualitative analysis

It refers to the visual assessment of the signal intensity of the masses. The masses were classified into masses with low signal and masses with high signal in comparison with the signal intensity of adjacent skeletal muscles. The mass signal on DWI was correlated with the corresponding signal on the automatically generated ADC maps.

Masses with free diffusion showed low signal on DWI and high signal on ADC map, while restricted diffusion was seen as high signal on high b value (800) DWI and low signal on corresponding ADC map. Masses with T2 shine through effect showed high signal in both DWIs and ADC maps.

  1. B.

    Quantitative analysis

ADC values were calculated from the ADC maps. For calculating the mean ADC value of the solid part of a mass, a ROI was drawn on the ADC map and placed on the solid area of the mass, apart from any necrotic areas in mixed masses. Concerning homogenous appearing masses, a circular ROI (1–2) cm was placed on the center of the mass mostly or measuring three ADC values then the mean ADC value was calculated.

  1. 2.

    Standard of reference:

Our MRI results were compared with histopathological results after biopsy (Tru-cut biopsy from solid masses and fine needle aspiration cytology (FNAC) from cystic masses), and then, diagnostic specificity and sensitivity were estimated by statistical analysis.

  1. 3.

    Statistical analysis and data interpretation

Data were fed to the computer and analyzed using IBM SPSS Corp., Released 2013, IBM SPSS Statistics for Windows, version 22.0, Armonk, NY: IBM Corp. Qualitative data were described using number and percent. Quantitative data were described using median (minimum and maximum) for nonparametric data and mean and standard deviation for parametric data after testing normality using Shapiro–Wilk test. Significance of the obtained results was judged at the 0.05 level. The diagnostic performance of a test or the accuracy of a test to discriminate diseased cases from non-diseased cases was evaluated using receiver operating characteristic (ROC) curve analysis. Sensitivity and specificity were detected from the curve, and PPV, NPV and accuracy were calculated through cross-tabulation.

Results

This study included 62 pediatric patients, 37 females (59.7%) and 25 males (40.3%), and their age ranged from 2 to 16 years (mean ± SD = 5.39 ± 5.31 SD). According to the results of histopathological examinations, we had 41 benign lesions and 21 malignant lesions, and out of the 41 benign lesions, we had 9 cases with lipoma, 8 cases with congenital hemangioma (Fig. 1), 5 cases with lymphangioma (Fig. 2), 4 cases with Baker’s cyst, 4 cases with fibroma, 2 cases with vascular malformation, 6 cases with abscess, 2 cases with pigmented villonodular synovitis (Fig. 3) and 1 case with lipoblastoma. Out of the 21 malignant lesions, we had 8 cases with rhabdomyosarcoma, 7 cases with Ewing sarcoma and 6 cases with lymphoma.

Fig. 1
figure 1

A 1-year-old male patient, presented with bilateral dorsal foot swellings. A, B Axial T1WI and axial T1WI with contrast: Fairly defined near symmetrical diffuse subcutaneous soft tissue thickening low signal intensity is seen on the dorsal aspect of both feet with no definite cortical bone disruption or significant abnormal marrow signal. It measures about 2 cm on the right side and 11 mm on the left side in its maximum dimension with intense enhancement in post-contrast series. C, D Axial T2WI and sagittal STIR right foot: Both swellings show high signal intensity in T2WI, not suppressed on STIR with linear signal void patterns (blood threads). E Axial DWI: Each swelling elicits high signal intensity. F Axial DW-ADC map image: high signal intensity recorded for each swelling with high mean ADC value (right = 1.623 × 10−3 mm2/s) and (left = 1.719 × 10–3 mm2/s). Pathological result was bilateral dorsal foot hemangiomas

Full size image
Fig. 2
figure 2

A 6-month-old male patient, presented with a painless mass at the left axilla. A Axial T1WI: A large lobulated multilocular cystic soft tissue mass elicits low signal intensity seen in the left axillary region with no intrathoracic extension. B Coronal T2WI: The mass elicits high signal intensity. C Axial DWI with b value of 800 s/mm2: The mass appears of low signal intensity. D Axial ADC map image: bright signal intensity recorded with high mean ADC value (2.183 × 10–3 mm2/s). Pathological result was a case of lymphangioma

Full size image
Fig. 3
figure 3

A 7-year-old female patient, presented with a painless mass at the anteromedial aspect of the left knee. A Sagittal T1WI: A localized well-defined multilocular cystic mass of low signal intensity is seen in the anteromedial aspect of the left knee with intact articular cartilages, peri-articular musculature, ligaments and preserved intervening fat planes. B, C Sagittal and axial T2WI: the mass elicits intermediate to high signal intensity. D Coronal STIR: the mass appears of high signal intensity with signal void structures seen within. E Axial DWI: The mass elicits high signal intensity. F Axial DW-ADC map image: low signal intensity recorded with mean ADC value (1.058 × 10–3 mm2/s). Pathological result was localized extra-articular pigmented villonodular synovitis

Full size image

In this study, 53.3% of cases show free diffusion in DWI with high signal in ADC map, while 46.7% of cases show restricted diffusion in DWI with low signal recorded in ADC map as shown in Table 1.

Table 1 ADC and DWI expression among the studied cases
Full size table

Regarding the measured ADC values, there was a statistically significant difference between benign and malignant masses (P value = 0.001*). Out of the 62 cases, the mean ADC value for all benign masses (n = 41) was 1.495 ± 0.55 SD × 10–3 mm2/s, which was significantly higher than the mean ADC value for all malignant masses (n = 21) that was 0.449 ± 0.27 SD × 10–3 mm2/s as shown in Table 2.

Table 2 Comparison of DW-ADC values between benign and malignant masses
Full size table

According to ADC values distribution, the highest recorded mean ADC was that of Baker’s cyst (2.6440 ± 0.10889 SD) × 10–3 mm2/s followed by that of lymphangioma (2.2100 ± 0.03960 SD) × 10–3 mm2/s, while the least recorded mean ADC value was that of the malignant cases in the study which were rhabdomyosarcoma (0.4498 ± 0.27050 SD) × 10–3 mm2/s with a statistically significant difference (P < 0.001*) as shown in Fig. 4 and Table 3.

Fig. 4
figure 4

A 3-month-old female patient, presented with a large mass at the left axilla. A, B Coronal T1WI and coronal T1WI with contrast: A large well-defined lobulated soft tissue mass of low signal intensity is seen involving predominantly the left axillary region and compressing chest wall posteromedially with no intervening fat planes between the mass and left lung apex. It is seen lateral abutting head of humerus with areas of cystic necrosis appear as small foci of low signal intensity. Heterogeneous enhancement noted in post-contrast series. C, D Coronal T2WI and coronal STIR: The mass shows heterogeneous high signal with small foci of high signal intensity representing areas of break down. E Axial DWI: The mass elicits high signal intensity. F Axial ADC map: The mass elicits low signal intensity with a mean ADC value of solid portion (0.681 × 10–3 mm2/s). Pathological result was a case of rhabdomyosarcoma

Full size image
Table 3 DW-ADC values distribution according to pathology results among the studied cases
Full size table

In this study, ROC curve analysis of the mean ADC in benign versus malignant masses yielded a confidence interval (CI) of 95% and area under curve (AUC) of 0.981 × 10–3 mm2/s. The cutoff point for all mean ADC value was 0.88 × 10–3 mm2/s with a high statistically significant difference between benign and malignant masses (P ≤ 0.001*), sensitivity of 100.0%, specificity of 92.3%, PPV of 66.7%, NPV of 100.0% and overall diagnostic accuracy of 93.3% as shown in Fig. 5 and Table 4.

Fig. 5
figure 5

ROC curve for DW-ADC in differentiating benign from malignant masses

Full size image
Table 4 The validity of DW-ADC in differentiating benign from malignant masses
Full size table

Discussion

A wide spectrum of entities may give rise to musculoskeletal soft tissue masses in pediatrics, including neoplastic and non-neoplastic masses. MRI plays a significant role in characterization of soft tissue tumors, yet conventional techniques lack specificity for proper differentiation between benign and malignant masses [12]. DWI is a noninvasive method for investigation of tumor histology and used effectively for differentiation between benign and malignant masses [13].

In this study, there was a statistically significant difference between the mean ADC value of benign and malignant masses (P = 0.001*), and the mean ADC value for benign masses was 1.495 ± 0.55 SD × 10−3 mm2/s versus (0.449 ± 0.27 SD) × 10−3 mm2/s for the malignant masses. This is in agreement with previous studies, and one study stated that the mean ADC value of benign masses was 2.31 ± 1.29 × 10−3 mm2/s versus 0.90 ± 0.70 × 10–3 mm2/s for malignant masses (P < 0.001) [13]. Other studies reported that there was a statistically significant difference between the mean ADC value of benign and malignant soft tissue tumors (P < 0.05) [5, 14].

The reported significant difference in our study also matched with that in the study of Zou et al. [15] that showed a significant decrease in the mean ADC value of patients with malignant soft tissue tumors in comparison with the ADC values obtained in patients with benign soft tissue tumors (P < 0.001). Their reported mean ADC value of benign soft tissue tumors was 1.73 × 10–3 mm2/s, while the mean ADC value of malignant soft tissue tumors was 0.8 × 10–3 mm2/s. Also our results matched with the previous study where the reported mean ADC value for malignant soft tissue tumors was 0.90 ± 0.32 × 10–3 mm2/s with a statistically significant difference between benign and malignant masses (P < 0.001) [16]. The same significant difference between benign and malignant soft tissue tumors was also recorded in the previous study (P < 0.0001) [17]. Other study showed that malignant rhabdomyosarcoma had a characteristically low ADC value of 0.71 ± 0.15 × 10−3 mm2/s with a statistically significant difference between benign and malignant masses (P < 0.05) [9]. Another study stated that there was a statistically significant difference between the mean ADC value of benign and malignant masses (P < 0.001) (mean ± SD, 1.43 ± 0.56 × 10−3 mm2/s for benign masses versus 0.74 ± 0.18 × 10−3 mm2/s for malignant masses) [18].

Among benign masses, as they are composed primarily of free water, we found that the highest mean ADC value recorded was that of Baker’s cyst cases (2.6440 ± 0.109 SD) × 10−3 mm2/s. This is in agreement with results of other study [13] in which the mean ADC value of benign cystic masses was the highest among benign cases (2.61 ± 0.35) × 10−3 mm2/s. Also our results are in agreement with Khedr et al.'s [17] where the highest ADC value among benign cases was recorded in cases of ganglion cyst (2.8 ± 0.23) × 10−3 mm2/s and cystic neurofibroma (2.5 ± 0.04) × 10−3 mm2/s. It was reported that in cases in which a cyst is suspected, ADC mapping could prove a useful complement to anatomic imaging. It has been found that the use of a mean ADC value greater than (2.5 × 10−3 mm2/s) yielded a sensitivity of 80% and a specificity of 100% in the diagnosis of benign cystic lesions, indicating that no soft tissue neoplasms are missed with DWI and ADC mapping [19].

Many previous studies reported that not all benign soft tissue tumors have a large extracellular space and not all malignant soft tissue tumors are more cellular than benign tumors [17]. In our study, there was an overlap in the recorded mean ADC values between some benign and malignant masses. The pediatric patients with benign masses demonstrating restricted diffusion like malignancy were patients with abscess (mean ADC value = 0.6810 ± 0.11314 × 10–3 mm2/s) and patients with PVNS (mean ADC value = 1.0580 × 10–3 mm2/s). Our results matched with the previous study where the mean ADC value for soft tissue abscess was 0.877 × 10–3 mm2/s. This could be explained by the high viscosity pus within abscesses that contains inflammatory cells, cellular debris, bacteria and proteins slowing water diffusion [20].

In addition, several theories are plausible for low ADC values in PVNS. The presence of hemosiderin and other blood products in these masses likely affects the ADC measurements. Even if great care was taken to avoid sampling of clearly hemorrhagic components, it is possible that microscopic areas of hemorrhagic products were present also in the sampled pixels and affected ADC values [21]. Other potential reasons for low ADC measurements in PVNS include the nature of intralesional matrix or presence of hypercellular components, nodulated and villose proliferation of the synovium with thickened synovial rinds of hemosiderin-laden tissue [9, 22].

A large number of studies have yielded conflicting results and have found different cutoff ADC values between benign and malignant masses although the most discriminating ADC value appears to be near (1 × 10–3 mm2/s). The reason of this conflicting result in previous studies can be explained by the variations in histopathological types of neoplasms, differences in DWI techniques used (equipment, b values and DWI sequences). However, all previous studies confirmed the fact that aggressive tumor usually records a very low ADC [23].

Regarding the ROC analysis in this study, the cutoff point for the mean ADC value was 0.88 × 10–3 mm2/s with sensitivity of 100.0%, specificity of 92.3% and diagnostic accuracy of 93.3%. This recorded cutoff point was lower than that of previous study [9] in which the cutoff value was 1.235 × 10–3 mm2/s; however, our study recorded higher sensitivity, specificity and accuracy in comparison with their results that were 73% sensitivity, 91.7% specificity and 80.3% overall accuracy. Other study reported that malignant tumors tend to exhibit a lower mean ADC value than that of benign soft tissue tumors and proposed using a threshold mean ADC value of 1.34 × 10−3 mm2/s [10]. The ROC analysis in another study [8] yielded nearly the same area under curve AUC that was 0.89 × 10−3mm2/s and a fairly close cutoff value for mean ADC which was ≤ 1.03 × 10–3 mm2/s, but with relatively lower sensitivity (90%) and specificity (91%) than those of our study.

The limitations of this study were that the number of pediatric patients especially those with malignant tumors was limited in this study. We did not perform a histogram analysis or calculate perfusion effects. At last, we did not obtain inter-observer variations of measurement of ADC values.

Conclusion

In pediatric patients, DWI (with quantitative ADC mapping) is an innovative valuable noninvasive imaging technique for characterization of musculoskeletal soft tissue masses and discrimination between benign and malignant masses. It is particularly suitable for the inconclusive cases of pediatric population without urgent need for invasive diagnostic procedures.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

MRI:

Magnetic resonance imaging

DWI:

Diffusion-weighted imaging

ADC:

Apparent diffusion coefficient

SD:

Standard deviation

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

STIR:

Short inversion recovery

EPI:

Echo planner imaging

TR:

Repetition time

TE:

Echo time

FOV:

Field of view

ROI:

Region of interest

FNAC:

Fine needle aspiration cytology

ROC:

Receiver operating characteristic

PPV:

Positive predictive value

NPV:

Negative predictive value

AUC:

Area under curve

CI:

Confidence interval

PVNS:

Pigmented villonodular synovitis

References

  1. De Schepper AM, De Beuckeleer L, Vandevenne J, Somville J (2000) Magnetic resonance imaging of soft tissue tumors. Eur Radiol 10(2):213–223

    Article  Google Scholar 

  2. Tranvinh E, Yeom KW, Iv M (2015) Imaging neck masses in the neonate and young infant. In: Seminars in ultrasound, CT and MRI, vol 36, no 2, pp 120–137

  3. Piccolo CL, Galluzzo M, Ianniello S, Trinci M, Russo A, Rossi E, Miele V (2017) Pediatric musculoskeletal injuries: role of ultrasound and magnetic resonance imaging. Musculoskelet Surg 101(1):85–102

    Article  CAS  Google Scholar 

  4. Wang T, Wu X, Cui Y, Chu C, Ren G, Li W (2014) Role of apparent diffusion coefficients with diffusion-weighted magnetic resonance imaging in differentiating between benign and malignant bone tumors. World J Surg Oncol 12(1):365

    Article  Google Scholar 

  5. Choi YJ, Lee IN, Song YS, Kim JI, Choi K, Song JW (2019) Diagnostic performance of diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI for the differentiation of benign from malignant soft-tissue tumors. J Magn Reson Imaging 50:798–809

    Article  Google Scholar 

  6. Subhawong TK, Jacobs MA, Fayad LM (2014) Insights into quantitative diffusion-weighted MRI for musculoskeletal tumor imaging. Am J Roentgenol 203(3):560–572

    Article  Google Scholar 

  7. Ahlawat S, Fritz J, Morris CD, Fayad LM (2019) Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: review of conventional features and focus on nonmorphologic imaging. J Magn Reson Imaging 50:11–27

    Article  Google Scholar 

  8. Neubauer H, Evangelista L, Hassold N, Winkler B, Schlegel PG, Köstler H, Beer M (2012) Diffusion-weighted MRI for detection and differentiation of musculoskeletal tumorous and tumor-like lesions in pediatric patients. World J Pediatr 8(4):342–349

    Article  Google Scholar 

  9. Hassanien OA, Younes RL, Dawoud RM (2018) Diffusion weighted MRI of soft tissue masses: can measurement of ADC value help in the differentiation between benign and malignant lesions? Egypt J Radiol Nucl Med 49(3):681–688

    Article  Google Scholar 

  10. Razek A, Nada N, Ghaniem M, Elkhamary S (2012) Assessment of soft tissue tumours of the extremities with diffusion echoplanar MR imaging. Radiol Med (Torino) 117(1):96–101

    Article  CAS  Google Scholar 

  11. Ahlawat S, Fayad LM (2015) De novo assessment of pediatric musculoskeletal soft tissue tumors: beyond anatomic imaging. Pediatrics 136(1):e194–e202

    Article  Google Scholar 

  12. Subhawong TK, Wang X, Durand DJ, Jacobs MA, Carrino JA, Machado AJ, Fayad LM (2012) Proton MR spectroscopy in metabolic assessment of musculoskeletal lesions. Am J Roentgenol 198(1):162–172

    Article  Google Scholar 

  13. Pekcevik Y, Kahya MO, Kaya A (2015) Characterization of soft tissue tumors by diffusion-weighted imaging. Iran J Radiol 12(3):e15478

    Article  Google Scholar 

  14. Lee SK, Jee WH, Jung CK, Chung YG (2020) Multiparametric quantitative analysis of tumor perfusion and diffusion with 3T MRI: differentiation between benign and malignant soft tissue tumors. BJR 93(1115):20191035

    Article  Google Scholar 

  15. Zou Y, Wang QD, Zong M, Zou YF, Shi HB (2016) Apparent diffusion coefficient measurements with diffusion-weighted imaging for differential diagnosis of soft-tissue tumor. J Cancer Res Ther 12(2):864–872

    Article  Google Scholar 

  16. Nassif MA, Abdul Rahim SA, Hussien RZ (2017) The role of diffusion-weighted MRI in the characterization of musculoskeletal soft tissue tumors. Egypt J Hosp Med 69(2):1895–1900

    Article  Google Scholar 

  17. Khedr SA, Hassaan MA, Abdelrazek NM, yehia Sakr, A. (2012) Diagnostic impact of echo planar diffusion-weighted magnetic resonance imaging (DWI) in musculoskeletal neoplastic masses using apparent diffusion coefficient (ADC) mapping as a quantitative assessment tool. Egypt J Radiol Nucl Med 43(2):219–226

    Article  Google Scholar 

  18. Romeih M, Raafat T, Khalaf M (2018) The diagnostic value of diffusion-weighted magnetic resonance imaging in characterization of musculoskeletal soft tissue tumors. Egypt J Radiol Nucl Med 49(2):400–407

    Article  Google Scholar 

  19. Subhawong TK, Jacobs MA, Fayad LM (2014) Diffusion-weighted MR imaging for characterizing musculoskeletal lesions. Radiographics 34(5):1163–1177

    Article  Google Scholar 

  20. Chun CW, Jung JY, Baik JS, Jee WH, Kim SK, Shin SH (2018) Detection of soft-tissue abscess: comparison of diffusion-weighted imaging to contrast-enhanced MRI. J Magn Reson Imaging 47:60–68

    Article  Google Scholar 

  21. Ashikyan O, Chalian M, Moore D, Xi Y, Pezeshk P, Chhabra A (2019) Evaluation of giant cell tumors by diffusion weighted imaging–fractional ADC analysis. Skelet Radiol 48(11):1765–1773

    Article  Google Scholar 

  22. Li Y, Lou H, Wang R, Wang C, Zhu W, Xia L, Zhu D (2003) MRI-pathologic correlation of pigmented villonodular synovitis. Chin J Radiol 37(6):493–498

    Google Scholar 

  23. Dallaudière B, Lecouvet F, Berg BV, Omoumi P, Perlepe V, Cerny M, Larbi A (2015) Diffusion-weighted MR imaging in musculoskeletal diseases: current concepts. Diagn Interv Imaging 96(4):327–340

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

No funding resources.

Author information

Authors and Affiliations

  1. Faculty of Medicine, Mansoura University, Mansoura, Egypt

    Mohamed Ali El-Adalany, Nevertiti Kamal El-Din Eid & Dina El-Metwally

  2. Resident of Diagnostic Radiology, Ministry of Health, Mansoura, Egypt

    Salma Othman

Authors
  1. Mohamed Ali El-Adalany
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nevertiti Kamal El-Din Eid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salma Othman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dina El-Metwally
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ME revised the collected data and the manuscript. SO and DM analyzed the MRI images of all patients. ME and DM wrote the manuscript and performed the statistical analysis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dina El-Metwally.

Ethics declarations

Ethics approval and consent to participate

The study was approved by our institution’s ethics committee (Mansoura Faculty of Medicine Institutional Research Board) (ethics committee reference number is MS/17.07.61), and all patients gave their written informed consent before inclusion in the study.

Consent for publication

Written informed consent for the publication of these data was obtained from the parents of the patients or from their legal guardian.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El-Adalany, M.A., Eid, N.K.ED., Othman, S. et al. Role of diffusion-weighted MRI in evaluation of pediatric musculoskeletal soft tissue masses. Egypt J Radiol Nucl Med 52, 263 (2021). https://doi.org/10.1186/s43055-021-00643-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43055-021-00643-6

Keywords