Published online before print October 19, 2007, 10.1148/radiol.2452061535
(Radiology 2007;245:848-854.)
© RSNA, 2007
Tumors in Pediatric Patients at Diffusion-weighted MR Imaging: Apparent Diffusion Coefficient and Tumor Cellularity1
Paul D. Humphries, FRCR,
Neil J. Sebire, MD, MRCPath,
Marilyn J. Siegel, MD, and
Øystein E. Olsen, MD, PhD
1 From the Departments of Radiology (P.D.H., Ø.E.O.) and Histopathology (N.J.S.), Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London WC1N 7JH, England; and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (M.J.S.). Received September 5, 2006; revision requested November 6; revision received December 6; accepted January 15, 2007; final version accepted March 7.
Address correspondence to: Ø.E.O. (e-mail: olseno{at}gosh.nhs.uk).
 |
ABSTRACT
|
|---|
Purpose: To prospectively assess whether there is a relationship between the apparent diffusion coefficient (ADC) and the histopathologic cell count and whether the ADC can enable differentiation of benign and malignant extracranial mass lesions in children.
Materials and Methods: Institutional ethics approval and parent or guardian consent were obtained. Eleven malignant and eight benign lesions in 19 children (11 girls, eight boys; median age, 3.9 years; age range, 11 days to 15.5 years) who underwent magnetic resonance (MR) imaging of extracranial mass lesions—including a diffusion-weighted sequence (with b values 0, 500, and 1000 sec/mm2)—and histopathologic analysis to prove findings were studied. The median ADC within each mass lesion was compared with the median cell count for 10 high-power microscopic fields in the specimen. The inverse regression between cell count and ADC was calculated. The difference in ADC between benign and malignant lesions was assessed by using the Mann-Whitney U test.
Results: There was an inverse relationship between ADC and cell count, expressed as ADC (in x10–3 mm2/sec) = 0.56 + (66.2/cell count), with a relatively good fit to the observed data (analysis of variance R2 = 0.541, F = 20.0, P < .001). The ADCs of benign lesions ranged from (0.84–2.83) x 10–3 mm2/sec (median, 1.35 x 10–3 mm2/sec; standard deviation, 0.68). The ADCs of malignant lesions ranged from (0.73–1.53) x 10–3 mm2/sec (median, 1.00 x 10–3 mm2/sec; standard deviation, 0.29). There was no significant difference in ADC between benign and malignant lesions (Mann-Whitney U = 22, P = .069). All highly cellular (>150 cells per high-power field) lesions had an ADC lower than 1.5 x 10–3 mm2/sec.
Conclusion: Although there is a significant relationship between cellularity and ADC, cell count probably is not the sole determinant of the ADC. Use of the ADC cannot enable accurate differentiation of malignant and benign lesions.
© RSNA, 2007
|
INTRODUCTION
|
|---|
Diffusion-weighted (DW) imaging is now well established in the neuroimaging field (1–6) and reportedly has been used for applications outside of the central nervous system—including renal assessment (7), retroperitoneal mass assessment (8), ovarian mass assessment (9), and prostatic malignancy evaluation (10)—in adult patients.
Non–central nervous system DW imaging is feasible in children (11–13). However, the histopathologic correlate for the apparent diffusion coefficient (ADC) in tumors has not yet been established. The ADC depends on the degree of restriction of water diffusion (14); accordingly, tissues with multiple barriers to diffusion have a low ADC (15). Tumors in pediatric patients have a wide range of cellularity: Malignant embryonal tumors typically have high cell counts, and stroma-rich and/or benign lesions typically have low cell counts (16). Since tissues with high cellularity and those with low cellularity differ in terms of water distribution in the intra- and extracellular compartments, we hypothesized that there is a corresponding difference in ADC values between these two tissue groups. Thus, the purpose of our study was to prospectively assess whether there is a relationship between the ADC and the histopathologic cell count and whether the ADC can enable differentiation between benign and malignant extracranial mass lesions in children.
|
MATERIALS AND METHODS
|
|---|
The setting was a tertiary care pediatric hospital. In a prospective observational study conducted from July 2003 to April 2006, children who underwent MR imaging for assessment of extracranial mass lesions before being treated were eligible for inclusion. Patients who had undergone chemotherapy or radiation therapy before the imaging examination were ineligible. Other exclusion criteria were contraindications to sedation, cardiac pacemakers, and intracranial vascular clips. Parent or guardian consent for study participation was obtained. Approval was obtained from the research ethics committee of the Institute of Child Health, University College London, London, England, and Great Ormond Street Hospital for Children.
MR Imaging
All MR examinations were performed by using one of two clinical 1.5-T MR units (Avanto or Symphony; Siemens, Erlangen, Germany). DW single-shot spin-echo sequences were performed with 2500–3300/80–112 (repetition time msec/echo time msec), a section thickness of 5 mm, an intersection gap of 0.5 mm, a field of view of 180–350 mm, an echo-planar readout (matrix of 128 x 88, 66 phase-encoding steps), and a bandwidth of 1260–1500 Hz/pixel. Three signals were acquired per image with diffusion-sensitizing gradients in three orthogonal planes and b values of 0, 500, and 1000 sec/mm2 during free breathing. Fat was suppressed by placing a frequency-selective radiofrequency pulse before the pulse sequence. We acquired 19 sections, which were centered on the mass lesion. DW images were always acquired before gadolinium-based contrast material (gadopentetate dimeglumine, Magnevist; Schering, Berlin, Germany) administration.
The imaging software (Syngo, release 2002; Siemens) calculated ADC maps by averaging the signal intensity in three orthogonal planes for each b value and subsequently calculating the slope of the logarithmic decay curve for signal intensity against b value. To determine whether any single b value was prone to being confounding, we plotted the logarithmically transformed signal intensity of the lesion against each b value obtained during individual image acquisitions. Almost straight lines could be drawn; we therefore chose not to exclude any measurement in calculations of the ADC (Fig 1).
View larger version (9K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 1: Examples of log(signal intensity [SI]) values plotted against b values for mass lesions in four children (Table). Patient 2 (top left) (neuroblastoma, 193 cells per high-power field) and patient 1 (top right) (neuroblastoma, 170 cells per high-power field) had high-cellularity lesions. Patient 16 (bottom left) (multilocular cystic nephroma, 42 cells per high-power field) and patient 14 (bottom right) (lymphangioma, 60 cells per high-power field) had low-cellularity lesions. Three b values were used: 0, 500, 1000 sec/mm2. Since the ADC represents the slope of the logarithmic decay of signal intensity with increasing b value, the almost straight lines in these plots indicate that no single b value was systematically confounding. Therefore, all three values could be used to calculate the ADC.
|
|
For each patient, the central three sections of the lesion were identified by using both ADC maps and conventional short inversion time inversion-recovery spin-echo (3100–4500/60–80/130 [repetition time msec/echo time msec/inversion time msec]) and three-dimensional nonenhanced and intravenous contrast material–enhanced fast low-angle shot (4.2–4.6/2.1–2.3, 25° flip angle) MR images. Within each of these three sections, a region of interest was defined along the circumference—but within the boundary—of the lesion. In partly cystic lesions, only the solid elements, which were identified on conventional T2-weighted images and on pre- and postcontrast T1-weighted images, were included in the regions of interest. All pixel ADC values from the regions of interest on all three sections were collated. Owing to slight skewing, median ADCs were calculated and were used in our study.
A single attending pediatric radiologist (Ø.E.O., 5 years of experience in pediatric MR imaging) drew the regions of interest on the images. To test the reliability of the calculated ADC, the drawing of the regions of interest was repeated after an interval of 3–12 months. The radiologist was blinded to the diagnoses and histopathologic cell counts and during the second reading was blinded to the previously drawn regions of interest and previously calculated ADCs. The sizes of the regions of interest ranged from 1.0 to 82.2 cm2 (median size, 32.4 cm2).
Histopathologic Examinations
Histopathologic results were the standard of reference for analysis of cellularity. Histopathologic specimens (core-needle biopsy and surgical specimens) were obtained. The median time between MR imaging and subsequent biopsy or surgical resection was 6.5 days (range, 1–19 days) for patients with benign lesions and 2 days (range, 1–10 days) for those with malignant lesions. All histopathologic specimens were reviewed for the purpose of this study by a single attending pediatric histopathologist (N.J.S., 10 years of experience in histopathology).
Tumor cellularity was calculated from 10 arbitrarily selected high-power fields by using a computer program (ImageJ; National Institutes of Health, Bethesda, Md) and the following algorithm: First, digitized high-power (x40 objective) fields were taken from original microscopic images with a 512 x 512 display matrix and an 8-bit gray level, which were obtained by using a digital microscope camera (Olympus DP12; Olympus, Tokyo, Japan). Binary image data were then derived from the sample images by using a threshold value estimated from histogram analysis of the sample images. Finally, the software calculated the cellularity based on the number of separate dots produced by the binary procedure. The accuracy of this method was confirmed at prestudy examination of simulated sample images for which the cellularity, determined with manual counting, was already known. The automated counting system performed to within the 5% tolerance in all specimens. Automated settings were identical for all specimens and cell counts. The results for the 10 high-power-field cell counts were used to derive the median cell count for the specimen.
Statistical Analyses
For assessment of intraobserver reliability, we calculated the mean intraobserver discrepancy, with corresponding 95% confidence intervals, for all patients. To assess any systematic error due to the ADC, we calculated the Spearman correlation of the discrepancy in ADC measurement within each patient against the mean ADC of the primary and secondary readings for each patient.
For each tumor, the ADC was plotted against the median cell count. Different models (ie, linear, inverse, logarithmic, and exponential models) native to the curve fit module of the statistical package were compared for goodness of fit to the observed data. An inverse regression yielded the best fit and was therefore chosen. The regression model was examined for systematic error by applying Spearman 
correlation statistics, with residuals of the model and cellularity as variables. The Mann-Whitney U test was used to examine the difference in ADC between benign and malignant lesions. We used SPSS, release 14 (SPSS, Chicago, Ill), for Microsoft Windows (Redmond, Wash) software for all statistical analyses. All tests were two tailed, with an 
level of .05.
|
RESULTS
|
|---|
Patients
Nineteen pediatric patients (11 girls, eight boys; age range, 11 days to 15 years 6 months; median age, 3.9 years) were enrolled in the study. Twenty-seven patients had been excluded either because they had undergone chemotherapy before MR imaging or because no histopathologic proof was available. The histopathologic diagnoses of the tumors were neuroblastoma (n = 3), nephroblastoma (n = 3), rhabdomyosarcoma (n = 2), rhabdoid tumor of the kidney (n = 1), hepatoblastoma (n = 1), diffuse large B-cell lymphoma (n = 1), ganglioneuroma (n = 1), focal nodular hyperplasia (n = 1), lymphangioma (n = 1), fibromatosis (n = 1), multilocular cystic nephroma (n = 1), ovarian cyst (n = 1), teratoma (n = 1), and nonspecific nodular inflammatory infiltrate of the spleen (n = 1) (Table).
Intraobserver Variability
There was a mean discrepancy in ADC between the primary and secondary readings of (–0.00947 ± 0.0545 [standard deviation]) x 10–3 mm2/sec (95% confidence interval: –0.0358 x 10–3 mm2/sec, 0.01681 x 10–3 mm2/sec). As a fraction of the mean ADC for the two readings, there was a mean difference between them of –0.77% ± 4.1 (95% confidence interval: –2.7%, 1.2%). There was no significant difference between discrepancy in ADC and mean ADC for the two readings (Spearman = 0.174, P = .475). Since there was no significant difference, the average of the two ADC readings was used for further analyses.
ADC of Tumors
ADCs ranged from (0.73–1.53) x 10–3 mm2/sec (median, 1.00 x 10–3 mm2/sec; standard deviation, 0.29) in the 11 malignant tumors and from (0.84–2.83) x 10–3 mm2/sec (median, 1.35 x 10–3 mm2/sec; standard deviation, 0.68) in the eight benign tumors (Table, Figs 2–5). The difference in ADC between the benign and malignant tumor groups was not significant (Mann-Whitney U = 22, P = .069).
View larger version (90K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 2a: Patient 16. (a) Transverse fat-suppressed T2-weighted spin-echo MR image (3800/98) shows a septate cystic mass (arrows) in the right kidney in a child with multilocular cystic nephroma. (b) On the corresponding transverse ADC map, the lesion (arrows) demonstrates unrestricted diffusion (median ADC within solid elements, 2.83 x 10–3 mm2/sec). (c) Photomicrograph of representative section through one of the septate areas shows relative hypocellularity with scattered spindle cells set in a hyaline and collagenous background. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (70K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 2b: Patient 16. (a) Transverse fat-suppressed T2-weighted spin-echo MR image (3800/98) shows a septate cystic mass (arrows) in the right kidney in a child with multilocular cystic nephroma. (b) On the corresponding transverse ADC map, the lesion (arrows) demonstrates unrestricted diffusion (median ADC within solid elements, 2.83 x 10–3 mm2/sec). (c) Photomicrograph of representative section through one of the septate areas shows relative hypocellularity with scattered spindle cells set in a hyaline and collagenous background. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (111K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 2c: Patient 16. (a) Transverse fat-suppressed T2-weighted spin-echo MR image (3800/98) shows a septate cystic mass (arrows) in the right kidney in a child with multilocular cystic nephroma. (b) On the corresponding transverse ADC map, the lesion (arrows) demonstrates unrestricted diffusion (median ADC within solid elements, 2.83 x 10–3 mm2/sec). (c) Photomicrograph of representative section through one of the septate areas shows relative hypocellularity with scattered spindle cells set in a hyaline and collagenous background. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (96K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 3a: Patient 4. (a) Transverse short inversion time inversion-recovery T2-weighted spin-echo MR image (5970/114/145) shows a left-sided abdominal mass (arrows) with retroperitoneal lymphadenopathy (arrowhead in a and b) in a patient with nephroblastoma. A pseudocapsule of renal tissue (R in a and b) around the mass suggests a renal origin. (b) Corresponding transverse ADC map shows the lesion (arrows) has relatively restricted diffusion (median ADC, 0.73 x 10–3 mm2/sec). (c) Photomicrograph of representative section through a solid area of the nephroblastoma shows moderate-cellularity blastema composed of ovoid cells with a high nucleus-to-cytoplasm ratio. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (91K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 3b: Patient 4. (a) Transverse short inversion time inversion-recovery T2-weighted spin-echo MR image (5970/114/145) shows a left-sided abdominal mass (arrows) with retroperitoneal lymphadenopathy (arrowhead in a and b) in a patient with nephroblastoma. A pseudocapsule of renal tissue (R in a and b) around the mass suggests a renal origin. (b) Corresponding transverse ADC map shows the lesion (arrows) has relatively restricted diffusion (median ADC, 0.73 x 10–3 mm2/sec). (c) Photomicrograph of representative section through a solid area of the nephroblastoma shows moderate-cellularity blastema composed of ovoid cells with a high nucleus-to-cytoplasm ratio. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (119K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 3c: Patient 4. (a) Transverse short inversion time inversion-recovery T2-weighted spin-echo MR image (5970/114/145) shows a left-sided abdominal mass (arrows) with retroperitoneal lymphadenopathy (arrowhead in a and b) in a patient with nephroblastoma. A pseudocapsule of renal tissue (R in a and b) around the mass suggests a renal origin. (b) Corresponding transverse ADC map shows the lesion (arrows) has relatively restricted diffusion (median ADC, 0.73 x 10–3 mm2/sec). (c) Photomicrograph of representative section through a solid area of the nephroblastoma shows moderate-cellularity blastema composed of ovoid cells with a high nucleus-to-cytoplasm ratio. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (104K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 4a: Patient 2. (a) Transverse T2-weighted spin-echo MR image (3350/97) obtained through the abdomen shows large left-sided abdominal mass (arrows) crossing the midline (arrowheads) in the retroperitoneum in a child with neuroblastoma. (b) Corresponding ADC map shows the mass (arrows) and the extension of the lesion (arrowheads) across the midline, with markedly restricted diffusion (median ADC, 0.75 x 10–3 mm2/sec). This is a high-cellularity tumor (median cell count, 193 cells per high-power field). (c) Photomicrograph of a representative section shows high cellularity: Numerous ovoid cells with a high nucleus-to-cytoplasm ratio within a focally fibrillary stroma indicate neuroblastoma. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (98K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 4b: Patient 2. (a) Transverse T2-weighted spin-echo MR image (3350/97) obtained through the abdomen shows large left-sided abdominal mass (arrows) crossing the midline (arrowheads) in the retroperitoneum in a child with neuroblastoma. (b) Corresponding ADC map shows the mass (arrows) and the extension of the lesion (arrowheads) across the midline, with markedly restricted diffusion (median ADC, 0.75 x 10–3 mm2/sec). This is a high-cellularity tumor (median cell count, 193 cells per high-power field). (c) Photomicrograph of a representative section shows high cellularity: Numerous ovoid cells with a high nucleus-to-cytoplasm ratio within a focally fibrillary stroma indicate neuroblastoma. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (149K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 4c: Patient 2. (a) Transverse T2-weighted spin-echo MR image (3350/97) obtained through the abdomen shows large left-sided abdominal mass (arrows) crossing the midline (arrowheads) in the retroperitoneum in a child with neuroblastoma. (b) Corresponding ADC map shows the mass (arrows) and the extension of the lesion (arrowheads) across the midline, with markedly restricted diffusion (median ADC, 0.75 x 10–3 mm2/sec). This is a high-cellularity tumor (median cell count, 193 cells per high-power field). (c) Photomicrograph of a representative section shows high cellularity: Numerous ovoid cells with a high nucleus-to-cytoplasm ratio within a focally fibrillary stroma indicate neuroblastoma. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (76K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 5a: Patient 15. (a) Transverse fat-suppressed T1-weighted spin-echo MR images (633/20) of the upper region of the right arm before (left) and after (right) intravenous administration of 0.1 mmol gadopentetate dimeglumine per kilogram of body weight show an enhancing subcutaneous mass lesion (arrows) separate from the underlying muscle in a patient with fibromatosis. (b) Corresponding ADC map of the fibromatosis shows an almost total loss of signal from the background and restricted diffusion of the lesion (arrows) (median ADC, 0.93 x 10–3 mm2/sec). (c) Photomicrograph of a representative section through the lesion shows variable cellularity with numerous plump spindle cells within a variably collagenous stroma, characteristic of fibromatosis. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (66K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 5b: Patient 15. (a) Transverse fat-suppressed T1-weighted spin-echo MR images (633/20) of the upper region of the right arm before (left) and after (right) intravenous administration of 0.1 mmol gadopentetate dimeglumine per kilogram of body weight show an enhancing subcutaneous mass lesion (arrows) separate from the underlying muscle in a patient with fibromatosis. (b) Corresponding ADC map of the fibromatosis shows an almost total loss of signal from the background and restricted diffusion of the lesion (arrows) (median ADC, 0.93 x 10–3 mm2/sec). (c) Photomicrograph of a representative section through the lesion shows variable cellularity with numerous plump spindle cells within a variably collagenous stroma, characteristic of fibromatosis. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
View larger version (116K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 5c: Patient 15. (a) Transverse fat-suppressed T1-weighted spin-echo MR images (633/20) of the upper region of the right arm before (left) and after (right) intravenous administration of 0.1 mmol gadopentetate dimeglumine per kilogram of body weight show an enhancing subcutaneous mass lesion (arrows) separate from the underlying muscle in a patient with fibromatosis. (b) Corresponding ADC map of the fibromatosis shows an almost total loss of signal from the background and restricted diffusion of the lesion (arrows) (median ADC, 0.93 x 10–3 mm2/sec). (c) Photomicrograph of a representative section through the lesion shows variable cellularity with numerous plump spindle cells within a variably collagenous stroma, characteristic of fibromatosis. (Hematoxylin-eosin stain; original magnification, x400.)
|
|
Tumor Cellularity and Relationship between Cellularity and ADC
Tumor cellularity ranged from 82 to 209 cells per high-power field (median, 179 cells per high-power field; standard deviation, 36.5) in the 11 malignant tumors and from 42 to 108.5 cells per high-power field (median, 59.8 cells per high-power field; standard deviation, 24.1) in the eight benign tumors. All highly cellular (>150 cells per high-power field) lesions had an ADC of less than 1.5 x 10–3 mm2/sec. When ADC was plotted against cell count, there was an inverse relationship, with a relatively steep initial slope followed by a less steep slope at higher cell counts. An inverse regression curve—expressed as ADC (in x10–3 mm2/sec) = 0.56 + (66.2/cell count)—described this variable slope, and there was a relatively good fit to the observed data, expressed as an R2 value of 0.541 at analysis of variance of the regression (F = 20.0, P < .001) (Fig 6). There was no systematic pattern of deviation of the residuals of the model over the range of cellularities (Spearman = 0.170, P = .486).
View larger version (8K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 6: ADC versus cell count per high-power field in 19 tumors in pediatric patients. Graph data indicate an inverse relationship between the two parameters, which is best expressed as an inverse function: ADC (in x10–3) = 0.56 + (66.2/cell count) (solid line). Although this regression was significant (F = 20.0, P < .001), the moderate R2 (0.541) suggests that factors other than cellularity contributed to the observed ADC.
|
|
|
DISCUSSION
|
|---|
Neuroblastoma, nephroblastoma, and rhabdomyosarcoma are the most common extracranial solid malignant tumors seen in children (16). Malignant tumors are usually enhanced on conventional T1-weighted gadolinium-enhanced MR images, and they may exhibit infiltration, peritumoral edema, and areas of cystic change due to necrosis and hemorrhage. The cystic changes can lead to nondiagnostic biopsy results because of insufficient amounts of tumor tissue within the sample. Because the ADC is a quantitative objective measure, incorporating DW sequences and ADC mapping into the imaging work-up of extracranial mass lesions in children could potentially aid in determining the diagnosis. For example, being able to identify more densely packed areas of tumor in a heterogeneous mass could help guide the biopsy to areas that would have a higher diagnostic yield, with avoidance of necrotic or less cellular areas. Also, serial ADC evaluation could reveal information about therapeutic success and, if so, facilitate the identification of poor response at an early stage—even before tumor shrinkage is expected. This application has been supported in animal models in which viable tissue and necrotic tumor differed in ADC value (17). Yet, to our knowledge there are no definitive data in the literature regarding the relationship between ADC and tumor cellularity.
We found that the ADC of the mass lesions studied had an inverse relationship with lesion cellularity. This finding broadly supports a model in which increasing cellularity and consequently more densely packed tissues have a relatively low observed ADC. Because low ADC in vivo represents restriction of the free diffusibility of water molecules, one may speculate that a high concentration of semipermeable membranes and a relatively high proportion of water in the intracellular compared with the extracellular space are important determinants of the ADC in highly cellular tumors (14,18). However, our mathematical model of ADC, as determined according to cellularity, does not offer a perfect fit to the observed data (R2 = 0.541); it is therefore likely that other factors also influence the observed ADC. For example, large abnormal nuclei, which occupy a greater proportion of the cell, may reduce the amount of space in which intracellular water can diffuse freely. Furthermore, an extracellular composition, such as stromal density, may affect water diffusion.
Our study results strongly support the notion that the ADC is related to the histopathologic appearance of a tumor. However, as seen in our results, the curve slope for ADC becomes less pronounced with increasing tumor cellularity. This might pose a problem in obtaining measurements that represent reliable quantifications of therapy response, unless the expected change in cellularity over the course of successful treatment is relatively large. This factor has consequences for the planning of prospective studies. In addition, ADC measurements may help narrow the diagnostic possibilities at presentation; however, in our study, there was no significant difference in ADC between the benign and malignant tumor groups. Some benign tumors had an ADC lower than 1.55 x 10–3 mm2/sec, a value that none of the malignant tumors exceeded.
Although the potential of DW imaging to enable differentiation between benign and malignant lesions has long been described (19), in our cohort, use of ADC measurements alone did not enable perfect differentiation between the two tumor groups. This result has also been reported for adult patients with soft-tissue sarcomas (20). However, if an ADC of 1.55 x 10–3 mm2/sec was theoretically used as the reference level for malignancy, there would be no false-negative lesions and four (21%) false-positive malignancies based on ADC criteria alone within our cohort of 19 lesions. With this consideration aside, we suggest that ADCs should not be used to differentiate benign from malignant lesions.
Study limitations included the relatively small number of patients who were available for inclusion. Although the patient mix included children with a reasonably wide spectrum of tumors, it was still from a single center. Our histopathologic assessments were often performed with core-needle biopsy (rather than excised) specimens, which were not perfectly representative of the tumor tissue: Some of the tumors appeared to be heterogeneous. Ideally, MR and histopathologic (proof) data should be obtained simultaneously. However, we believe that the relatively short interval between MR imaging and histopathologic analysis did not substantially influence the results.
Our study results strongly suggest that there is an inverse relationship between lesion cellularity and ADC. Although the cell count probably is not the sole determinant of the ADC, we believe that the results of our study warrant further investigation—particularly to assess ADC mapping as a tool for stratifying response to chemotherapy at an early stage, before tumor shrinkage is expected.
|
ADVANCES IN KNOWLEDGE
|
|---|
- There is an inverse relationship between the cellularity of extracranial tumors in children and the apparent diffusion coefficient (ADC) of these tumors.
- Use of ADC measurements did not enable differentiation between benign and malignant extracranial lesions in our pediatric patient group.
|
IMPLICATION FOR PATIENT CARE
|
|---|
- Apparent diffusion coefficient (ADC) changes during chemotherapy may help stratify treatment response, although further investigation is needed. We believe that at present, ADC maps cannot be used to support clinical decision making.
|
FOOTNOTES
|
|---|
Abbreviations: ADC = apparent diffusion coefficient • DW = diffusion weighted
Author contributions: Guarantor of integrity of entire study, Ø.E.O.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, P.D.H., Ø.E.O.; clinical studies, P.D.H., N.J.S., Ø.E.O.; statistical analysis, all authors; and manuscript editing, all authors
Authors stated no financial relationship to disclose.
|
References
|
|---|
- Castillo M, Mukherji SK. Diffusion-weighted imaging in the evaluation of intracranial lesions. Semin Ultrasound CT MR 2000;21:405–416.[CrossRef][Medline]
- Ebisu T, Tanaka C, Umeda M, et al. Discrimination of brain abscess from necrotic or cystic tumors by diffusion-weighted echo planar imaging. Magn Reson Imaging 1996;14:1113–1116.[CrossRef][Medline]
- Leuthardt EC, Wippold FJ, Oswood MC, Rich KM. Diffusion-weighted MR imaging in the preoperative assessment of brain abscesses. Surg Neurol 2002;58:395–402.[CrossRef][Medline]
- Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology 2000;217:331–345.[Abstract/Free Full Text]
- Warach S, Chien D, Li W, Ronthal M, Edelman RR. Fast magnetic resonance diffusion-weighted imaging of acute human stroke. Neurology 1992;42:1717–1723.[Abstract/Free Full Text]
- Tsuruda JS, Chew WM, Moseley ME, Norman D. Diffusion-weighted MR imaging of the brain: value of differentiating between extraaxial cysts and epidermoid tumors. AJNR Am J Neuroradiol 1990;11:925–931.[Abstract]
- Cova M, Squillaci E, Stacul F, et al. Diffusion-weighted MRI in the evaluation of renal lesions: preliminary results. Br J Radiol 2004;77:851–857.[Abstract/Free Full Text]
- Nakayama T, Yoshimitsu K, Irie H, et al. Usefulness of the calculated apparent diffusion coefficient value in the differential diagnosis of retroperitoneal masses. J Magn Reson Imaging 2004;20:735–742.[CrossRef][Medline]
- Nakayama T, Yoshimitsu K, Irie H, et al. Diffusion-weighted echo-planar MR imaging and ADC mapping in the differential diagnosis of ovarian cystic masses: usefulness of detecting keratinoid substances in mature cystic teratomas. J Magn Reson Imaging 2005;22:271–278.[CrossRef][Medline]
- Issa B. In vivo measurement of the apparent diffusion coefficient in normal and malignant prostatic tissues using echo-planar imaging. J Magn Reson Imaging 2002;16:196–200.[CrossRef][Medline]
- Humphries PD, Wynne CS, Sebire NJ, Ølsen OE. Atypical abdominal paediatric lymphangiomatosis: diagnosis aided by diffusion-weighted MRI. Pediatr Radiol 2006;36:857–859.[CrossRef][Medline]
- Olsen ØE, Sebire NJ. Apparent diffusion coefficient maps of pediatric mass lesions with free-breathing diffusion-weighted magnetic resonance: feasibility study. Acta Radiol 2006;47:198–204.[CrossRef][Medline]
- Uhl M, Altehoefer C, Kontny U, Il'yasov K, Büchert M, Langer M. MRI-diffusion imaging of neuroblastomas: first results and correlation to histology. Eur Radiol 2002;12:2335–2338.[Medline]
- Szafer A, Zhong J, Anderson AW, Gore JC. Diffusion-weighted imaging in tissues: theoretical models. NMR Biomed 1995;8:289–296.[Medline]
- Benveniste H, Hedlund LW, Johnson GA. Mechanism of detection of acute cerebral ischemia in rats by diffusion-weighted magnetic resonance microscopy. Stroke 1992;23:746–754.[Abstract/Free Full Text]
- Askin FB. Neuroblastoma and peripheral neuroectodermal tumours: a clinicopathological review. In: Stocker JT, Askin FB, eds. Pathology of solid tumours in children. London, England: Chapman and Hall, 1998; 25–50.
- Herneth AM, Guccione S, Bednarski M. Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 2003;45:208–213.[CrossRef][Medline]
- Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 2000;43:828–836.[CrossRef][Medline]
- Le Bihan DJ. Differentiation of benign versus pathologic compression fractures with diffusion-weighted MR imaging: a closer step toward the "holy grail" of tissue characterization? Radiology 1998;207:305–307.[Free Full Text]
- Einarsdóttir H, Karlsson M, Wejde J, Bauer HC. Diffusion-weighted MRI of soft tissue tumours. Eur Radiol 2004;14:959–963.[CrossRef][Medline]
TOP
ABSTRACT
INTRODUCTION
