The value of whole lesion ADC histogram profiling to differentiate between morphologically indistinguishable ring enhancing lesions-comparison of glioblastomas and brain abscesses

Advances in the Radiological Evaluation of and Theranostics for Glioblastoma

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics

Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs' compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.

The Role of Apparent Diffusion Coefficient Values in Glioblastoma: Differentiating Tumor Progression Versus Treatment-Related Changes

OBJECTIVE

Glioblastoma represents the most common primary brain malignancy with a median survival of 15 months. Follow-up examinations are crucial to establish the presence of tumor recurrence, as well as treatment-associated changes such as ischemic infarction and radiation effects. Even though magnetic resonance imaging is a valuable tool, a histopathological diagnosis is often required because of imaging overlap between tumor recurrence and treatment associated changes. We set out to measure the apparent diffusion coefficient (ADC) values of the lesions in magnetic resonance imaging scans of treated glioblastoma patients to investigate if ADC values could accurately differentiate between tumor progression, radiation-related changes, and ischemic infarctions.

METHODS

We evaluated ADC values among 3 groups, patients with tumor progression, radiation necrosis, and ischemic infarctions. The regions of interest were placed in the areas of greatest hypointensity among solid lesions using the ADC maps, excluding areas with necrotic, cystic, or hemorrhagic changes. The ADC values of the contralateral normal appearing white matter were also measured as the reference value for each patient. The relative ADC (rADC) values were measured for all 3 groups. Comparison between lesions and normal white matter was evaluated by Wilcoxon signed test.

RESULTS

A total of 157 patients were included in the study; 49 patients classified as tumor progression, 58 patients as radiation necrosis, and 50 patients as ischemic infarctions. The mean ± SD ADC value was 752.8 ± 132.5 for tumor progression, 479.0 ± 105.2 for radiation-related changes, and 250.1 ± 57.2 for ischemic infarctions. The mean ± SD rADC value was 1.07 ± 0.22 for tumor progression, 0.66 ± 0.14 for radiation necrosis, and 0.34 ± 0.08 for ischemic infarctions. The mean rADC values were significantly higher in tumor progression, compared with both radiation necrosis and ischemic changes (P < 0.001).

CONCLUSIONS

The present study demonstrates that ADC values are a helpful tool to differentiate between tumor progression, radiation necrosis, and posttreatment ischemic changes.

Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization

Simple Summary

Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics on a cellular and subcellular scale.

Abstract

(1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate-dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Statistical analysis was performed to elucidate associations between histogram features and WHO grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation rate and clinically significant mutations in case of astrocytic gliomas.

Comparison of different ROI analysis methods for liver lesion characterization with simplified intravoxel incoherent motion (IVIM)

This study investigated the impact of different ROI placement and analysis methods on the diagnostic performance of simplified IVIM-DWI for differentiating liver lesions. 1.5/3.0-T DWI data from a respiratory-gated MRI sequence (b = 0, 50, 250, 800 s/mm²) were analyzed in patients with malignant (n = 74/54) and benign (n = 35/19) lesions. Apparent diffusion coefficient ADC = ADC(0,800) and IVIM parameters D₁' = ADC(50,800), D₂' = ADC(250,800), f₁' = f(0,50,800), f₂' = f(0,250,800), and D*' = D*(0,50,250,800) were calculated voxel-wise. For each lesion, a representative 2D-ROI, a 3D-ROI whole lesion, and a 3D-ROI from "good" slices were placed, including and excluding centrally deviating areas (CDA) if present, and analyzed with various histogram metrics. The diagnostic performance of 2D- and 3D-ROIs was not significantly different; e.g. AUC (ADC/D₁'/f₁') were 0.958/0.902/0.622 for 2D- and 0.942/0.892/0.712 for whole lesion 3D-ROIs excluding CDA at 1.5 T (p > 0.05). For 2D- and 3D-ROIs, AUC (ADC/D₁'/D₂') were significantly higher, when CDA were excluded. With CDA included, AUC (ADC/D₁'/D₂'/f₁'/D*') improved when low percentiles were used instead of averages, and was then comparable to the results of average ROI analysis excluding CDA. For lesion differentiation the use of a representative 2D-ROI is sufficient. CDA should be excluded from ROIs by hand or automatically using low percentiles of diffusion coefficients.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Simple Summary

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10⁻³ mm²/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile

Purpose

Glioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.

Methods

Pre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.

Results

Grade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.

Conclusions

ADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

The value of asphericity derived from T1-weighted MR in differentiating intraparenchymal ring-enhancing lesions-comparison of glioblastomas and brain abscesses

BACKGROUND

Both brain abscess(BA)and glioblastoma (GBM) are common causative pathologies of intraparenchymal ring-enhancing lesions. Advanced MR sequences such as diffusion weighted image (DWI) were often used to increase distinguishability of both entities.

PURPOSE

To evaluate the value of asphericity (ASP) from conventional T1-weighted MR images in differentiating BA from morphologically similar ring-enhancing GBM.

MATERIAL AND METHODS

Twenty-one BA and twenty-nine GBM were retrospectively included in this study. Each region of interest (ROI) was delineated twice with the software of ITK-SNAP on the contrast-enhanced T1 images by two observers. ASP was calculated to define the relative deviation of the ROI's shape from a sphere. Intraclass correlation coefficients (ICC) for inter-observer and intra-observer were calculated. The diagnostic capabilities of ASP and conventional volume (VOL) of ROI were evaluated with receiver operating characteristic (ROC) curve analysis. In addition, areas under the ROC curves of ASP and VOL were compared.

RESULTS

ICC of intra-observer and inter-observer were 0.99 (95% confidence interval, [CI] 0.97-0.99) and 0.98 (0.95-0.99), respectively. Both ASP and VOL showed significant difference between BA and GBM. The mean ASP values for BA and GBM were 66.3±7.8 and 14.7±1.8, respectively. The mean VOL value of BA was also larger than that of GBM (47.2±7.4 vs. 20.7±1.5 mm³). The mean AUC of ASP and VOL were 0.977 (95% CI 0.944-1) and 0.86 (95% CI 0.746-0.974), respectively. The AUC of ASP was significantly higher than that of VOL (p=0.04). The optimal cut point values of ASP and VOL were 24.39 and 24.86 mm³, respectively.

CONCLUSIONS

ASP derived from routine MRI is useful in differentiating BA from GBM.

Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas

BACKGROUND AND PURPOSE

Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified.

MATERIAL AND METHODS

In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated.

RESULTS

In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters).

CONCLUSION

Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas.

Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions

MRI has a vital role in the assessment of intracranial lesions. Conventional MRI has limited specificity and multiparametric MRI using diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance spectroscopy allows more accurate assessment of the tissue microenvironment. The purpose of this educational pictorial review is to demonstrate the role of multiparametric MRI for diagnosis, treatment planning and for assessing treatment response, as well as providing a practical approach for performing and interpreting multiparametric MRI in the clinical setting. A variety of cases are presented to demonstrate how multiparametric MRI can help differentiate neoplastic from non-neoplastic lesions compared to conventional MRI alone.

Associations Between Apparent Diffusion Coefficient Value With Pathological Type, Histologic Grade, and Presence of Lymph Node Metastases of Esophageal Carcinoma

Objective:

To investigate the application value of apparent diffusion coefficient value in the pathological type, histologic grade, and presence of lymph node metastases of esophageal carcinoma.

Materials and Methods:

Eighty-six patients with pathologically confirmed esophageal carcinoma were divided into different groups according to pathological type, histological grade, and lymph node status. All patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging scan, and apparent diffusion coefficient values of tumors were measured. Independent sample t test and 1-way variance were used to compare the difference of apparent diffusion coefficient value in different pathological types, histologic grades, and lymph node status. Correlation between the apparent diffusion coefficient value and the histologic grade was evaluated using Spearman rank correlation test. Receiver operating characteristic curve of apparent diffusion coefficient value was generated to evaluate the differential diagnostic efficiency of poorly and well/moderately differentiated esophageal carcinoma.

Results:

No significant difference was observed in apparent diffusion coefficient value between esophageal squamous cell carcinoma and adenocarcinoma and in patients between those with and without lymph node metastases (P > .05). The differences of apparent diffusion coefficient value were statistically significant between different histologic grades of esophageal carcinoma (P < .05). The apparent diffusion coefficient value was positively correlated with histologic grade (rs = 0.802). The apparent diffusion coefficient value ≤1.25 × 10⁻³ mm²/s as the cutoff value for diagnosis of poorly differentiated esophageal carcinoma with the sensitivity of 84.3%, and the specificity was 94.3%.

Conclusions:

The performance of apparent diffusion coefficient value was contributing to predict the histologic grade of esophageal carcinoma, which might increase lesions characterization before choosing the best therapeutic alternative. However, they do not correlate with pathological type and the presence of lymph node metastases of esophageal carcinoma.

Intracranial peripherally enhancing lesions in cardiac transplant recipients: A rare case series and literature review

Intracranial peripherally enhancing lesions in immunosuppressed solid organ transplant recipients represent a unique diagnostic and management dilemma due to the vast array of differentials that demand consideration. Diagnosis of the underlying pathology is often guided by the use of magnetic resonance imaging (MRI). We present the first published case series of three cardiac transplant recipients with significantly atypical neuroradiological findings contrary to the tenets of contemporary literature. Our rare case series consists of: (1) A sterile Mycobacterium pyogenic abscess mimicking glioblastoma multiforme due to an immunosuppressed state (2) Epstein Barr Virus encephalitis masquerading as Central Nervous System Post-Transplant Lymphoproliferative Disorder (3) An unusual case of partially treated disseminated Nocardiosis warning of the need to consider the immunosuppressed state and partial treatment response obfuscating classical MRI appearances. We utilise these unprecedented cases as the basis of a literature review to understand the pathophysiology behind the peculiar imaging findings in this rarefied cohort of transplant recipients, and rationalise why the MRI findings in each instance contradicts the accepted imaging patterns. In the setting of potential unreliability of neuroradiology in this immunosuppressed unique subgroup, we hope to impart to clinicians that definitive diagnosis obtained by emergent neurosurgical intervention may be necessary to accurately and expediently guide further medical management.

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as “benign” neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.

Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed.

Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status.

Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.

Rheologically Essential Surfactant Proteins of the CSF Interacting with Periventricular White Matter Changes in Hydrocephalus Patients - Implications for CSF Dynamics and the Glymphatic System

Surfactant proteins (SP) are multi-systemic proteins playing crucial roles in the regulation of rheological properties of physiological fluids, host defense, and the clearance of potentially harmful metabolites. Hydrocephalus patients suffer from disturbed central nervous system (CNS) fluid homeostasis and exhibit remarkably altered SP concentrations within the cerebrospinal fluid (CSF). A connection between CSF-SPs, CSF flow, and ventricular dilatation, a morphological hallmark of hydrocephalus, has been reported previously. However, currently there are no studies investigating the link between rheologically active SPs and periventricular white matter changes caused by impaired CSF microcirculation in hydrocephalic conditions. Thus, the aim of this study was to assess their possible relationships. The present study included 47 individuals (27 healthy subjects and 20 hydrocephalus patients). CSF specimens were analyzed for concentrations of SP-A, SP-C, and SP-D by using enzyme-linked immunosorbent assays (ELISAs). Axial T2w turbo inversion recovery magnitude (TIRM) magnetic resonance imaging was employed in all cases. Using a custom-made MATLAB-based tool for quantification of magnetic resonance signal intensities in the brain, parameters related to disturbed deep white matter CSF microcirculation were estimated (TIRM signal intensity (SI)-mean, minimum, maximum, median, mode, standard deviation, and percentiles, p10th, p25th, p75th, p90th, as well as kurtosis, skewness, and entropy of the SI distribution). Subsequently, statistical analysis was performed (IBM SPSS 24™) to identify differences between hydrocephalic patients and healthy individuals and to further investigate the connections between CSF-SP changes and deep white matter signal intensities. SP-A (0.38 ± 0.23 vs. 0.76 ± 0.49 ng/ml) and SP-C (0.54 ± 0.28 vs. 1.27 ± 1.09 ng/ml) differed between healthy controls and hydrocephalus patients in a statistically significant manner. Also, corresponding quantification of white matter signal intensities revealed statistically significant differences between hydrocephalus patients and healthy individuals: SImean (370.41 ± 188.15 vs. 222.27 ± 99.86, p = 0.001), SImax (1115.30 ± 700.12 vs. 617.00 ± 459.34, p = 0.005), SImedian (321.40 ± 153.17 vs. 209.52 ± 84.86, p = 0.001), SImode (276.55 ± 125.63 vs. 197.26 ± 78.51, p = 0.011), SIstd (157.09 ± 110.07 vs. 81.71 ± 64.94, p = 0.005), SIp10 (229.10 ± 104.22 vs. 140.00 ± 63.12, p = 0.001), SIp25 (266.95 ± 122.62 vs. 175.63 ± 71.42, p = 0.002), SIp75 (428.80 ± 226.88 vs. 252.19 ± 110.91, p = 0.001), SIp90 (596.47 ± 345.61 vs. 322.06 ± 176.00, p = 0.001), skewness (1.19 ± 0.68 vs. 0.43 ± 1.19, p = 0.014), and entropy (5.36 ± 0.37 vs. 4.92 ± 0.51, p = 0.002). There were no differences regarding SP-D levels in hydrocephalus patients vs. healthy controls. In the acute hydrocephalic subgroup, correlations were as follows: SP-A showed a statistically significant correlation with SImax (r = 0.670, p = 0.024), SIstd (r = 0.697, p = 0.017), SIp90 (r = 0.621, p = 0.041), and inverse correlation with entropy (r = - 0.700, p = 0.016). SP-C correlated inversely with entropy (r = - 0.686, p = 0.020). For the chronic hydrocephalus subgroup, the following correlations were identified: SP-A correlated with kurtosis of the TIRM histogram (r = - 0.746, p = 0.021). SP-C correlated with SImean (r = - 0.688, p = 0.041), SImax (r = - 0.741, p = 0.022), SImedian (r = - 0.716, p = 0.030), SImode (r = - 0.765, p = 0.016), SIstd (r = - 0.671, p = 0.048), SIp25 (r = - 0.740, p = 0.023), SIp75 (r = - 0.672, p = 0.048), and SIp90 (r = - 0.667, p = 0.050). SP-D apparently does not play a major role in CSF fluid physiology. SP-A and SP-C are involved in different aspects of CNS fluid physiology. SP-A appears to play an essential compensatory role in acute hydrocephalus and seems less involved in chronic hydrocephalus. In contrary, SP-C profile and white matter changes are remarkably connected in CSF of chronic hydrocephalus patients. Considering the association between CSF flow phenomena, white matter changes, and SP-C profiles, the latter may especially contribute to the regulation of paravascular glymphatic physiology.

Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma

BACKGROUND

Medulloblastomas are the most common central nervous system tumors in childhood. Treatment and prognosis strongly depend on histology and transcriptomic profiling. However, the proliferative potential also has prognostical value. Our study aimed to investigate correlations between histogram profiling of diffusion-weighted images and further microarchitectural features.

MATERIAL AND METHODS

Seven patients (age median 14.6 years, minimum 2 years, maximum 20 years; 5 male, 2 female) were included in this retrospective study. Using a Matlab-based analysis tool, histogram analysis of whole apparent diffusion coefficient (ADC) volumes was performed.

RESULTS

ADC entropy revealed a strong inverse correlation with the expression of the proliferation marker Ki67 (r = - 0.962, p = 0.009) and with total nuclear area (r = - 0.888, p = 0.044). Furthermore, ADC percentiles, most of all ADCp90, showed significant correlations with Ki67 expression (r = 0.902, p = 0.036).

DISCUSSION AND CONCLUSION

Diffusion histogram profiling of medulloblastomas provides valuable in vivo information which potentially can be used for risk stratification and prognostication. First of all, entropy revealed to be the most promising imaging biomarker. However, further studies are warranted.

Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas

BACKGROUND: Meningiomas are the most frequently diagnosed intracranial masses, oftentimes requiring surgery. Especially procedure-related morbidity can be substantial, particularly in elderly patients. Hence, reliable imaging modalities enabling pretherapeutic prediction of tumor grade, growth kinetic, realistic prognosis, and—as a consequence—necessity of surgery are of great value. In this context, a promising diagnostic approach is advanced analysis of magnetic resonance imaging data. Therefore, our study investigated whether histogram profiling of routinely acquired postcontrast T1-weighted images is capable of separating low-grade from high-grade lesions and whether histogram parameters reflect Ki-67 expression in meningiomas. MATERIAL AND METHODS: Pretreatment T1-weighted postcontrast volumes of 44 meningioma patients were used for signal intensity histogram profiling. WHO grade, tumor volume, and Ki-67 expression were evaluated. Comparative and correlative statistics investigating the association between histogram profile parameters and neuropathology were performed. RESULTS: None of the investigated histogram parameters revealed significant differences between low-grade and high-grade meningiomas. However, significant correlations were identified between Ki-67 and the histogram parameters skewness and entropy as well as between entropy and tumor volume. CONCLUSIONS: Contrary to previously reported findings, pretherapeutic postcontrast T1-weighted images can be used to predict growth kinetics in meningiomas if whole tumor histogram analysis is employed. However, no differences between distinct WHO grades were identifiable in out cohort. As a consequence, histogram analysis of postcontrast T1-weighted images is a promising approach to obtain quantitative in vivo biomarkers reflecting the proliferative potential in meningiomas.