Discrimination of paediatric brain tumours using apparent diffusion coefficient histograms

Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

Conventional anatomic brain MRI is often limited in evaluating pediatric brain tumors, the most common solid tumors and a leading cause of death in children. Advanced brain MRI techniques have great potential to improve diagnostic performance in children with brain tumors and overcome diagnostic pitfalls resulting from diverse tumor pathologies as well as nonspecific or overlapped imaging findings. Advanced MRI techniques used for evaluating pediatric brain tumors include diffusion-weighted imaging, diffusion tensor imaging, functional MRI, perfusion imaging, spectroscopy, susceptibility-weighted imaging, and chemical exchange saturation transfer imaging. Because pediatric brain tumors differ from adult counterparts in various aspects, MRI protocols should be designed to achieve maximal clinical benefits in pediatric brain tumors. In this study, we review advanced MRI techniques and interpretation algorithms for pediatric brain tumors.

MR imaging of brain pilocytic astrocytoma: beyond the stereotype of benign astrocytoma

BACKGROUND

Pilocytic astrocytoma (PA) is the most common pediatric brain glioma and is considered the prototype of benign circumscribed astrocytoma. Despite its low malignancy, the CT and MRI features of brain PA may resemble those of much more aggressive brain tumors. Misdiagnosis of PA is particularly easy when it demonstrates MR morphological and non-morphological findings that are inconsistent with its non-aggressive nature and that overlap with the features of more aggressive brain tumors.

METHOD

Basing on the evidence that the variation in the histological, genetic, and metabolic "fingerprint" for brain PA is dependent on tumor location, and the hypothesis that tumor location is related to the broad spectrum of morphological and non-morphological MR imaging findings, the authors discuss the MR imaging appearance of brain PA using a location-based approach to underline the typical and less typical imaging features and the main differential diagnosis of brain PA. A brief summary of the main pathological and clinical features, the natural history, and the treatment of brain PA is also provided.

RESULT

A combination of morphological and non-morphological MR imaging features and a site-based approach to differential diagnosis are required for a pre-operative diagnosis. The new "cutting-edge" MR imaging sequences have the potential to impact the ease and confidence of pediatric brain tumor interpretation and offer a more efficient diagnostic work-up.

CONCLUSIONS

Although the typical imaging features of brain pilocytic astrocytoma make radiological diagnosis relatively easy, an atypical and more aggressive appearance can lead to misdiagnosis. Knowing the broad spectrum of imaging characteristics on conventional and advanced MR imaging is important for accurate pre-operative radiological diagnosis and correctly interpreting changes during follow-up.

Magnetic resonance imaging based functional imaging in paediatric oncology

Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.

Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade

PURPOSE

To explore the role of histogram analysis of apparent diffusion coefficient (ADC) MRI maps based on entire tumor volume data in determining pancreatic neuroendocrine tumor (PNT) grade.

METHODS AND MATERIALS

Retrospective evaluation of 22 patients with PNTs included low-grade (G1; n = 15), intermediate-grade (G2; n = 4), and high-grade (G3; n = 3) tumors. Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and summated to obtain histograms for entire tumor volume. Calculated histographic parameters included mean ADC (mADC), 5th percentile ADC, 10th percentile ADC, 25th percentile ADC, 50th percentile ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90) and 95th percentile ADC (ADC95), skewness and kurtosis. Histogram parameters were correlated with tumor grade by repeated measures analysis of variance with Tukey-Kramer post hoc comparisons.

RESULTS

The mADC, ADC75, ADC90, and ADC95 were significantly higher in G1 tumors (1283 ± 267; 1404 ± 300; 1495 ± 318; 1562 ± 347 × 10(-6) mm(2)/s) compared to G2 (892 ± 390; 952 ± 381; 1036 ± 384; 1072 ± 374 × 10(-6) mm(2)/s) and to G3 tumors (733 ± 225; 864 ± 284; 1008 ± 288; 1152 ± 192 × 10(-6) mm(2)/s) (p value <0.05). Skewness and kurtosis were significantly different between G1 (0.041 ± 0.466; 2.802 ± 0.679) and G3 (1.01 ± 1.140; 5.963 ± 4.008) tumors (p value <0.05). Tumor volume (mL) was significantly higher on G3 (55 ± 15.7) compared to G1 (1.9 ± 2.7) and G2 (4.5 ± 3.6) tumors (p value <0.05). In this small sample size, we did not detect statistically significant parameters between G2 (n = 4) and G3 (n = 3) tumors.

CONCLUSIONS

Histographic analysis of ADC maps on the basis of the entire tumor volume can be useful in differentiating histologic grades of PNTs.

ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma

PURPOSE

To determine the diagnostic performance of apparent diffusion coefficient (ADC) histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) for differentiating adrenal adenoma from pheochromocytoma.

MATERIALS AND METHODS

We retrospectively evaluated 52 adrenal tumors (39 adenomas and 13 pheochromocytomas) in 47 patients (21 men, 26 women; mean age, 59.3 years; range, 16-86 years) who underwent DW 3.0T MRI. Histogram parameters of ADC (b-values of 0 and 200 [ADC₂₀₀ ], 0 and 400 [ADC₄₀₀ ], and 0 and 800 s/mm² [ADC₈₀₀ ])-mean, variance, coefficient of variation (CV), kurtosis, skewness, and entropy-were compared between adrenal adenomas and pheochromocytomas, using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves for the histogram parameters were generated to differentiate adrenal adenomas from pheochromocytomas. Sensitivity and specificity were calculated by using a threshold criterion that would maximize the average of sensitivity and specificity.

RESULTS

Variance and CV of ADC₈₀₀ were significantly higher in pheochromocytomas than in adrenal adenomas (P < 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC was significantly higher in pheochromocytomas than in adrenal adenomas (all P ≤ 0.001), and showed the highest area under the ROC curve among the ADC histogram parameters for diagnosing adrenal adenomas (ADC₂₀₀ , 0.82; ADC₄₀₀ , 0.87; and ADC₈₀₀ , 0.92), with sensitivity of 84.6% and specificity of 84.6% (cutoff, ≤2.82) with ADC₂₀₀ ; sensitivity of 89.7% and specificity of 84.6% (cutoff, ≤2.77) with ADC₄₀₀ ; and sensitivity of 94.9% and specificity of 92.3% (cutoff, ≤2.67) with ADC₈₀₀ .

CONCLUSION

ADC histogram analysis of DW MRI can help differentiate adrenal adenoma from pheochromocytoma.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:1195-1203.

A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroectodermal tumours (PNETs), based on diffusion-weighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research.

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue

Purpose

To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low‐perfused tissues.

Materials and Methods

Model data simulations relevant to brain and low‐perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed at a series of signal‐to‐noise ratio (SNR) levels to assess the influence of noise on the fitting.

Results

The estimated IVIM parameters from model simulations were found significantly different (P < 0.05) using simultaneous and constrained fitting methods at low SNR. Higher accuracy and reproducibility were achieved with the constrained fitting method. Using this method, the mean error (%) for the estimated IVIM parameters at a clinically relevant SNR = 40 were D 0.35, D* 41.0 and f 4.55 for the tumor model and D 1.87, D* 2.48, and f 7.49 for the gray matter model. The most robust parameters were the IVIM‐D and IVIM‐f. The IVIM‐D* was increasingly overestimated at low perfusion.

Conclusion

A constrained IVIM fitting method provides more accurate and reproducible IVIM parameters in low‐perfused tissue compared with simultaneous fitting.

Level of Evidence: 3

J. MAGN. RESON. IMAGING 2017;45:1325–1334

Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model

BACKGROUND AND PURPOSE

Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D).

MATERIALS AND METHODS

Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, β, and μ) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation.

RESULTS

Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ≤ .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ≥ .706). The receiver operating characteristic analysis showed that β (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or μ (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%.

CONCLUSIONS

The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, β exhibited a better performance than the other 2 parameters, including ADC (or D).

Histogram Analysis of Apparent Diffusion Coefficient in Differentiating Pancreatic Adenocarcinoma and Neuroendocrine Tumor

The aim of this study was to investigate whether histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) can help differentiate pancreatic adenocarcinomas from neuroendocrine tumors.Sixty-four patients with histologically confirmed 53 pancreatic adenocarcinomas or 19 neuroendocrine tumors underwent DW MRI. We evaluated the pixel distribution histogram parameters (mean, skewness, kurtosis, and entropy) of the apparent diffusion coefficient (ADC) values derived from b-values of 0 and 200 (ADC200), 0 and 400 (ADC400), or 0 and 800 (ADC800) s/mm(2). Histogram parameters were compared between pancreatic adenocarcinomas and neuroendocrine tumors, and the diagnostic performance was evaluated by using receiver operating characteristic (ROC) analysis.The mean ADC200 and ADC400 were significantly higher in neuroendocrine tumors than in pancreatic adenocarcinomas (P = 0.001 and P = 0.019, respectively). Pancreatic adenocarcinomas showed significantly higher skewness and kurtosis on ADC400 (P = 0.007 and P = 0.001, respectively) and ADC800 (P = 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC values was significantly higher in pancreatic adenocarcinomas (P < 0.001 for ADC200; P = 0.001 for ADC400; P < 0.001 for ADC800), and showed the highest area under the ROC curve for diagnosing adenocarcinomas (0.77 for ADC200, 0.76 for ADC400, and 0.78 for ADC800).ADC histogram analysis of DW MRI can help differentiate pancreatic adenocarcinomas from neuroendocrine tumors.

Apparent diffusion coefficient histogram metrics correlate with survival in diffuse intrinsic pontine glioma: a report from the Pediatric Brain Tumor Consortium

BACKGROUND

Diffuse intrinsic pontine glioma (DIPG) is associated with poor survival regardless of therapy. We used volumetric apparent diffusion coefficient (ADC) histogram metrics to determine associations with progression-free survival (PFS) and overall survival (OS) at baseline and after radiation therapy (RT).

METHODS

Baseline and post-RT quantitative ADC histograms were generated from fluid-attenuated inversion recovery (FLAIR) images and enhancement regions of interest. Metrics assessed included number of peaks (ie, unimodal or bimodal), mean and median ADC, standard deviation, mode, skewness, and kurtosis.

RESULTS

Based on FLAIR images, the majority of tumors had unimodal peaks with significantly shorter average survival. Pre-RT FLAIR mean, mode, and median values were significantly associated with decreased risk of progression; higher pre-RT ADC values had longer PFS on average. Pre-RT FLAIR skewness and standard deviation were significantly associated with increased risk of progression; higher pre-RT FLAIR skewness and standard deviation had shorter PFS. Nonenhancing tumors at baseline showed higher ADC FLAIR mean values, lower kurtosis, and higher PFS. For enhancing tumors at baseline, bimodal enhancement histograms had much worse PFS and OS than unimodal cases and significantly lower mean peak values. Enhancement in tumors only after RT led to significantly shorter PFS and OS than in patients with baseline or no baseline enhancement.

CONCLUSIONS

ADC histogram metrics in DIPG demonstrate significant correlations between diffusion metrics and survival, with lower diffusion values (increased cellularity), increased skewness, and enhancement associated with shorter survival, requiring future investigations in large DIPG clinical trials.

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors

BACKGROUND AND PURPOSE

Apparent diffusion coefficient (ADC) values have been shown to assist in differentiating cerebellar pilocytic astrocytomas and medulloblastomas. Previous studies have applied only ADC measurements and calculated the mean/median values. Here we investigated the value of diffusion tensor imaging (DTI) histogram characteristics of the entire tumor for differentiation of cerebellar pilocytic astrocytomas and medulloblastomas.

METHODS

Presurgical DTI data were analyzed with a region of interest (ROI) approach to include the entire tumor. For each tumor, histogram-derived metrics including the 25th percentile, 75th percentile, and skewness were calculated for fractional anisotropy (FA) and mean (MD), axial (AD), and radial (RD) diffusivity. The histogram metrics were used as primary predictors of interest in a logistic regression model. Statistical significance levels were set at p < .01.

RESULTS

The study population included 17 children with pilocytic astrocytoma and 16 with medulloblastoma (mean age, 9.21 ± 5.18 years and 7.66 ± 4.97 years, respectively). Compared to children with medulloblastoma, children with pilocytic astrocytoma showed higher MD (P = .003 and P = .008), AD (P = .004 and P = .007), and RD (P = .003 and P = .009) values for the 25th and 75th percentile. In addition, histogram skewness showed statistically significant differences for MD between low- and high-grade tumors (P = .008).

CONCLUSIONS

The 25th percentile for MD yields the best results for the presurgical differentiation between pediatric cerebellar pilocytic astrocytomas and medulloblastomas. The analysis of other DTI metrics does not provide additional diagnostic value. Our study confirms the diagnostic value of the quantitative histogram analysis of DTI data in pediatric neuro-oncology.

Diffusion-weighted imaging and the apparent diffusion coefficient on 3T MR imaging in the differentiation of craniopharyngiomas and germ cell tumors

The apparent diffusion coefficient (ADC) on diffusion-weighted imaging (DWI) plays an important role in diagnosing intracranial tumors and predicting the histopathological grade of the tumor. However, the differences in the ADC values between craniopharyngiomas and germ cell tumors (GCTs) have not been clarified. We therefore evaluated the DWI and ADC values at b = 1000 and b = 4000 s/mm(2) on 3T magnetic resonance (MR) imaging and assessed the possibility of differentiating between craniopharyngiomas and GCTs. We retrospectively reviewed 19 patients with craniopharyngioma and 24 patients with GCT who underwent surgery and received a histopathological diagnosis. Thirty-four patients underwent DWI with b = 1000 and b = 4000 s/mm(2) and nine patients underwent periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) DWI with b = 1000 s/mm(2). The ADC was determined by manually placing regions of interests (ROIs) in the respective tumor regions on the ADC maps and is expressed as the minimum (ADC(MIN)), mean (ADC(MEAN)), and maximum (ADC(MAX)) absolute values. The craniopharyngiomas showed lower intensity on DWI at b = 1000 and b = 4000 s/mm(2) than the GCTs. Furthermore, the craniopharyngiomas demonstrated significantly high ADC values (ADC(MIN), ADC(MEAN), and ADC(MAX)) in comparison with the GCTs on DWI at b = 1000 and b = 4000 s/mm(2). The logistic discriminant analysis clarified the advantage of ADC(MIN) at b = 4000 s/mm(2) in differentiating between craniopharyngiomas and GCTs compared with the other ADC values. DWI and the ADC values may help clinicians to differentiate between craniopharyngiomas and GCTs. The ADC(MIN) at b = 4000 s/mm(2) is particularly useful for differentiation.

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model

PURPOSE

To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors.

MATERIALS AND METHODS

The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis.

RESULTS

None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm(2)/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm(2)/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation.

CONCLUSION

The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.

Diffusion characteristics of pediatric pineal tumors

BACKGROUND

Diffusion weighted imaging (DWI) has been shown to be helpful in characterizing tumor cellularity, and predicting histology. Several works have evaluated this technique for pineal tumors; however studies to date have not focused on pediatric pineal tumors.

OBJECTIVE

We evaluated the diffusion characteristics of pediatric pineal tumors to confirm if patterns seen in studies using mixed pediatric and adult populations remain valid.

MATERIALS AND METHODS

This retrospective study was performed after Institutional Review Board approval. We retrospectively evaluated all patients 18 years of age and younger with pineal tumors from a single institution where preoperative diffusion weighted imaging as well as histologic characterization was available.

RESULTS

Twenty patients (13 male, 7 female) with pineal tumors were identified: seven with pineoblastoma, four with Primitive Neuroectodermal Tumor (PNET), two with other pineal tumors, and seven with germ cell tumors including two germinomas, three teratomas, and one mixed germinoma-teratoma. The mean apparent diffusion coefficient (ADC) values in pineoblastoma (544 ± 65 × 10⁻⁶ mm²/s) and pineoblastoma/PNET (595 ± 144 × 10⁻⁶ mm²/s) was lower than that of the germ cell tumors (1284 ± 334 × 10⁻⁶ mm²/s; p < 0.0001 vs pineoblastoma). One highly cellular germinoma had an ADC value of 694 × 10⁻⁶ mm²/s.

CONCLUSION

ADC values can aid in differentiation of pineoblastoma/PNET from germ cell tumors in a population of children with pineal masses.

Evaluating the apparent diffusion coefficient in MRI studies as a means of determining paediatric brain tumour stages

BACKGROUND

The apparent diffusion coefficient (ADC) in MRI seems to be related to cellularity in brain tumours. Its utility as a tool for distinguishing between histological types and tumour stages remains controversial.

PROCEDURES

We retrospectively evaluated children diagnosed with CNS tumours between January 2008 and December 2013. Data collected were age, sex, histological diagnosis, and location of the tumour. We evaluated the ADC and ADC ratio and correlated those values with histological diagnoses.

RESULTS

The study included 55 patients with a median age of 6 years. Histological diagnoses were pilocytic astrocytoma (40%), anaplastic ependymoma (16.4%), ganglioglioma (10.9%), glioblastoma (7.3%), medulloblastoma (5.5%), and other (20%). Tumours could also be classified as low-grade (64%) or high-grade (36%). Mean ADC was 1.3 for low-grade tumours and 0.9 for high-grade tumours (p=.004). Mean ADC ratios were 1.5 and 1.2 for low and high-grade tumours respectively (p=.025). There were no significant differences in ADC/ADC ratio between different histological types.

CONCLUSION

ADC and ADC ratio may be useful in imaging-study based differential diagnosis of low and high-grade tumours, but they are not a substitute for an anatomical pathology study.

Quantified ADC histogram analysis: a new method for differentiating mass-forming focal pancreatitis from pancreatic cancer

BACKGROUND

As their prognosis and management are different, differentiation of mass-forming focal pancreatitis (FP) from pancreatic adenocarcinoma (PC) is important. However, the similar clinical presentations and imaging features of these conditions, along with inconclusive biopsy results can make such differentiation difficult.

PURPOSE

To determine whether apparent diffusion coefficient (ADC) histogram analysis can discriminate between a normal pancreas, FP, and PC.

MATERIAL AND METHODS

In a retrospective study, 25 PC patients, 14 FP patients, and 25 subjects with a normal pancreas underwent breath-hold diffusion-weighted imaging (DWI) on a 3.0 T magnetic resonance (MR) scanner. Regions of interest (ROIs) were drawn on the normal pancreases and on the entire focal lesions of both PC and FP. The ADC value was averaged from the lowest to 10th, 30th, 50th, and 100th percentile of the histogram (i.e. ADC10, ADC30, ADC50, and ADC100, respectively), and the results were analyzed statistically.

RESULTS

There were no significant differences among the head, body, and tail of normal pancreases for any of the mean ADC values (P > 0.05). ADC10, ADC30, and ADC50 values demonstrated significant differences between lesion and non-lesion areas of both PC (P < 0.05) and FP (P < 0.05). Differences in lesion areas between PC and FP were found with ADC50 and ADC100 values (P < 0.05), and helped differentiate a normal pancreas from FP and PC, and FP from PC.

CONCLUSION

Quantified ADC histogram can specifically reflect tissue heterogeneity and help differentiate a normal pancreas from FP and PC.

Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings

RATIONALE AND OBJECTIVES

To investigate the potential value of histogram analysis of apparent diffusion coefficient (ADC) obtained at standard (700 s/mm(2)) and high (1500 s/mm(2)) b values on a 3.0-T scanner in the differentiation of bladder cancer from benign lesions and in assessing bladder tumors of different pathologic T stages and to evaluate the diagnostic performance of ADC-based histogram parameters.

MATERIALS AND METHODS

In all, 52 patients with bladder lesions, including benign lesions (n = 7) and malignant tumors (n = 45; T1 stage or less, 23; T2 stage, 7; T3 stage, 8; and T4 stage, 7), were retrospectively evaluated. Magnetic resonance examination at 3.0 T and diffusion-weighted imaging were performed. ADC maps were obtained at two b values (b = 700 and 1500 s/mm(2); ie, ADC-700 and ADC-1500). Parameters of histogram analysis included mean, kurtosis, skewness, and entropy. The correlations between these parameters and pathologic results were revealed. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic value of histogram parameters.

RESULTS

Significant differences were found in mean ADC-700, mean ADC-1500, skewness ADC-1500, and kurtosis ADC-1500 between bladder cancer and benign lesions (P = .002-.032). There were also significant differences in mean ADC-700, mean ADC-1500, and kurtosis ADC-1500 among bladder tumors of different pathologic T stages (P = .000-.046). No significant differences were observed in other parameters. Mean ADC-1500 and kurtosis ADC-1500 were significantly correlated with T stage, respectively (ρ = -0.614, P < .001; ρ = 0.374, P = .011). ROC analysis showed that the combination of mean ADC-1500 and kurtosis ADC-1500 has the maximal area under the ROC curve (AUC, 0.894; P < .001) in the differentiation of benign lesions and malignant tumors, with a sensitivity of 77.78% and specificity of 100%. AUCs for differentiating low- and high-stage tumors were 0.840 for mean ADC-1500 (P < .001) and 0.696 for kurtosis ADC-1500 (P = .015).

CONCLUSIONS

Histogram analysis of ADC-1500 at 3.0 T can be useful in evaluation of bladder lesions. A combination of mean ADC-1500 and kurtosis ADC-1500 may be more beneficial in the differentiation of benign and malignant lesions. Mean ADC-1500 was the most promising parameter for differentiating low- from high-stage bladder cancer.

Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1 - and T2 -weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1 - and T2 -weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1 -weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2 -weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1 - and T2 -weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information.

Use of apparent diffusion coefficient values for diagnosis of pediatric posterior fossa tumors

We prospectively compared the ability of neuroradiologists to diagnose medulloblastoma with novice raters using only apparent diffusion coefficient (ADC) values measured on ADC maps. One hundred and three pediatric patients with pre-operative magnetic resonance imaging scans showing a posterior fossa tumor with histological verification were retrospectively identified from a ten-year period at a tertiary care medical center. A single observer measured the lowest ADC values in all tumors to determine the mean minimum ADC (ADCmin) value that provided greatest accuracy in distinguishing medulloblastomas from other tumors, which was determined to be 0.66×10(-3) mm(2)/s. Imaging studies, including ADC maps, from 90 patients were provided to two neuroradiologists, who provided a diagnosis, which was later dichotomized as medulloblastoma or other. Two medical students measured ADCmin within tumors and those with ADCmin < 0.66×10(-3) mm(2)/s were recorded as medulloblastoma; any other value was recorded as other. Diagnostic accuracy was measured. ADCmin values allowed a correct identification of lesions as either medulloblastoma or other in 91% of cases. After diagnoses by the two neuroradiologists were categorized as either medulloblastoma or other, their diagnoses were correct in 90% and 84% of cases, respectively. In 19 cases, at least one neuroradiologist was incorrect; the addition of ADC values to clinical interpretation would have allowed a correct diagnosis in 63% of such cases. Diagnostic accuracy based on ADC values by medical students was comparable to that of subspecialty-trained neuroradiologists. Our findings suggest that the addition of ADC values to standard film interpretation may improve the diagnostic rate for these tumors.

Combination of MRI and dynamic FET PET for initial glioma grading

AIM

MRI and PET with 18F-fluoro-ethyl-tyrosine (FET) have been increasingly used to evaluate patients with gliomas. Our purpose was to assess the additive value of MR spectroscopy (MRS), diffusion imaging and dynamic FET-PET for glioma grading.

PATIENTS, METHODS

38 patients (42 ± 15 aged, F/M: 0.46) with untreated histologically proven brain gliomas were included. All underwent conventional MRI, MRS, diffusion sequences, and FET-PET within 3±4 weeks. Performances of tumour FET time-activity-curve, early-to-middle SUVmax ratio, choline / creatine ratio and ADC histogram distribution pattern for gliomas grading were assessed, as compared to histology. Combination of these parameters and respective odds were also evaluated.

RESULTS

Tumour time-activity-curve reached the best accuracy (67%) when taken alone to distinguish between low and high-grade gliomas, followed by ADC histogram analysis (65%). Combination of time-activity-curve and ADC histogram analysis improved the sensitivity from 67% to 86% and the specificity from 63-67% to 100% (p < 0.008). On multivariate logistic regression analysis, negative slope of the tumour FET time-activity-curve however remains the best predictor of high-grade glioma (odds 7.6, SE 6.8, p = 0.022).

CONCLUSION

Combination of dynamic FET-PET and diffusion MRI reached good performance for gliomas grading. The use of FET-PET/MR may be highly relevant in the initial assessment of primary brain tumours.

Evaluation of apparent diffusion coefficient thresholds for diagnosis of medulloblastoma using diffusion-weighted imaging

We assess a diffusion-weighted imaging (DWI) analysis technique as a potential basis for computer-aided diagnosis (CAD) of pediatric posterior fossa tumors. A retrospective medical record search identified 103 children (mean age: 87 months) with posterior fossa tumors having a total of 126 preoperative MR scans with DWI. The minimum ADC (ADCmin) and normalized ADC (nADC) values [ratio of ADCmin values in tumor compared to normal tissue] were measured by a single observer blinded to diagnosis. Receiver operating characteristic (ROC) curves were generated to determine the optimal threshold for which the nADC and ADCmin values would predict tumor histology. Inter-rater reliability for predicting tumor type was evaluated using values measured by two additional observers. At histology, ten tumor types were identified, with astrocytoma (n=50), medulloblastoma (n=33), and ependymoma (n=9) accounting for 89%. Mean ADCmin (0.54 × 10(-3) mm(2)/s) and nADC (0.70) were lowest for medulloblastoma. Mean ADCmin (1.28 × 10(-3) mm(2)/s) and nADC (1.64) were highest for astrocytoma. For the ROC analysis, the area under the curve when discriminating medulloblastoma from other tumors using nADC was 0.939 and 0.965 when using ADCmin. The optimal ADCmin threshold was 0.66 × 10(-3) mm(2)/s, which yielded an 86% positive predictive value, 97% negative predictive value, and 93% accuracy. Inter-observer variability was very low, with near perfect agreement among all observers in predicting medulloblastoma. Our data indicate that both ADCmin and nADC could serve as the basis for a CAD program to distinguish medulloblastoma from other posterior fossa tumors with a high degree of accuracy.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors

BACKGROUND AND PURPOSE

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features.

MATERIALS AND METHODS

This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data.

RESULTS

ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix.

CONCLUSIONS

Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology.

Challenges for the functional diffusion map in pediatric brain tumors

Background

The functional diffusion map (fDM) has been suggested as a tool for early detection of tumor treatment efficacy. We aim to study 3 factors that could act as potential confounders in the fDM: areas of necrosis, tumor grade, and change in tumor size.

Methods

Thirty-four pediatric patients with brain tumors were enrolled in a retrospective study, approved by the local ethics committee, to examine the fDM. Tumors were selected to encompass a range of types and grades. A qualitative analysis was carried out to compare how fDM findings may be affected by each of the 3 confounders by comparing fDM findings to clinical image reports.

Results

Results show that the fDM in areas of necrosis do not discriminate between treatment response and tumor progression. Furthermore, tumor grade alters the behavior of the fDM: a decrease in apparent diffusion coefficient (ADC) is a sign of tumor progression in high-grade tumors and treatment response in low-grade tumors. Our results also suggest using only tumor area overlap between the 2 time points analyzed for the fDM in tumors of varying size.

Conclusions

Interpretation of fDM results needs to take into account the underlying biology of both tumor and healthy tissue. Careful interpretation of the results is required with due consideration to areas of necrosis, tumor grade, and change in tumor size.

Apparent diffusion coefficient of pediatric cerebellar tumors: a biomarker of tumor grade?

BACKGROUND

The role of diffusion weighted imaging (DWI) to reliably differentiate tumor types and grades in pediatric cerebellar tumors is controversial. We aimed to clarify the discrepancy reported in previous articles.

PROCEDURES

We retrospectively evaluated the apparent diffusion coefficient (ADC) values of the enhancing, solid parts of cerebellar tumors and correlated the absolute tumor ADC values and cerebellar and thalamic ratios with histology in a cohort of children with cerebellar tumors.

RESULTS

Twenty-four children (12 females) were included in the study. The median age at pre-surgical MRI was 10 years (range 29 days-18.5 years). Absolute ADC values (mean 1.49, SD 0.25 vs. 0.63 ± 0.18), cerebellar (2.04 ± 0.33 vs. 0.83 ± 0.25), and thalamic ratio (1.98 ± 0.35 vs. 0.79 ± 0.23) were significantly higher in low- than in high-grade tumors (P < 0.0001). Absolute ADC values and cerebellar and thalamic ratios were significantly higher in low-grade astrocytomas than in MBs. Overlap was seen for WHO grade II and III ependymomas. One hundred percent specific cutoff ADC values of >1.2 × 10(3) and <0.8 × 10(-3) mm(2) /s were established for low- and high-grade tumors.

CONCLUSION

ADC analysis of the solid, contrast enhancing components of pediatric cerebellar tumors may facilitate differentiation between various tumor histologies.

Malignant glioma with angiocentric features

Angiocentric glioma is a recently recognized benign brain tumor with unknown histogenesis. Most of these tumors are mitotically low in activity in accord with their benign clinical course. However, increased mitotic activity has been noted in several cases, one of which had an ultimately fatal outcome. Here, the authors present a tumor showing angiocentric glioma and glioblastoma-like features, with recurrence of the lower-grade component after radiotherapy. A 15-year-old boy presented with a 3-month history of progressive left-sided weakness and headache. Magnetic resonance imaging showed a large heterogeneous mass in the right frontal lobe, with mild post-Gd enhancement. A gross-total resection was obtained. Histopathological examination of the resected tissue revealed a tumor with 2 distinct appearances: 1) a mildly to moderately cellular infiltrating tumor with angiocentric glioma characteristics, and 2) a markedly cellular glioblastoma-like tissue with necrosis and microvascular proliferation. The patient received a course of postoperative radiotherapy to 59.4 Gy in 33 fractions administered over the course of 6.5 weeks, but his tumor recurred 4 months after resection. A second resection was then performed. The recurrent tumor exhibited radiation-induced changes and persistent characteristics of angiocentric glioma, but it had fewer malignant features; the mitotic activity was lower, and there was no necrosis or microvascular proliferation. The findings in this case, along with those in several previously reported cases, suggest that angiocentric gliomas may have a malignant variant or malignant transformation. Angiocentric gliomas with malignant features tend to recur, for which surgical intervention followed by radiotherapy and chemotherapy should be offered as a therapeutic option.

Functional imaging in adult and paediatric brain tumours

Imaging is a key component in the management of brain tumours, with MRI being the preferred modality for most clinical scenarios. However, although conventional MRI provides mainly structural information, such as tumour size and location, it leaves many important clinical questions, such as tumour type, aggressiveness and prognosis, unanswered. An increasing number of studies have shown that additional information can be obtained using functional imaging methods (which probe tissue properties), and that these techniques can give key information of clinical importance. These techniques include diffusion imaging, which can assess tissue structure, and perfusion imaging and magnetic resonance spectroscopy, which measures tissue metabolite profiles. Tumour metabolism can also be investigated using PET, with 18F-deoxyglucose being the most readily available tracer. This Review discusses these methods and the studies that have investigated their clinical use. A strong emphasis is placed on the measurement of quantitative parameters, which is a move away from the qualitative nature of conventional radiological reporting and presents major challenges, particularly for multicentre studies.

Survival analysis for apparent diffusion coefficient measures in children with embryonal brain tumours

Embryonal brain tumors constitute a large and important subgroup of pediatric brain tumors. Apparent diffusion coefficient (ADC) measures have been previously used in the analysis of these tumors. We investigated a newly described ADC-derived parameter, the apparent transient coefficient in tumor (ATCT), a measure of the gradient change of ADC from the peri-tumoral edema into the tumor core, to study whether ATCT correlates with survival outcome. Sixty-one patients with histologically proven embryonal brain tumors and who had diffusion-weighted imaging (DWI) as part of their clinical imaging were enrolled in a retrospective study correlating ADC measures with survival. Kaplan-Meier survival curves were constructed for extent of surgical resection, age <3 years at diagnosis, tumor type, and metastasis at presentation. A multivariate survival analysis was performed that took into consideration ATCT and variables found to be significant in the Kaplan-Meier analysis as covariates. Results from the multivariate analysis showed that ATCT was the only significant covariate (P < .001). Survival analysis using Kaplan-Meier curves, dividing the patients into 4 groups of increasing values of ATCT, showed that more negative values of ATCT were significantly associated with a poorer prognosis (P < .001). A statistically significant difference was observed for survival data with respect to the change in ADC from edema into the tumor volume. Results show that more negative ATCT values are significantly associated with a poorer survival among children with embryonal brain tumors, irrespective of tumor type, extent of resection, age <3 years at diagnosis, and metastasis at presentation.