Differentiation of Hemangioblastoma from Metastatic Brain Tumor using Dynamic Contrast-enhanced MR Imaging

A case report of cerebellar hemangioblastoma simulated brain metastasis shown by magnetic resonance imaging

RATIONALE

Hemangioblastomas occur both sporadically and as an important component of von Hippel-Lindau (VHL) disease. The typical MRI features of hemangioblastoma are cysts with enhanced cystic wall nodules in the cerebellum or lesions with uniform enhancement on the surface or inside the spinal cord. If there is edema around hemangioblastoma, it is easy to be misdiagnosed as brain metastasis on MRI.

PATIENT CONCERNS

A 41-year-old male patient was found to have a lump in the pancreas during a health examination 3 months ago. Subsequently, the patient underwent surgical treatment. The postoperative pathology suggests that the pancreatic lesion is a neuroendocrine tumor. The patient subsequently came to our hospital for consultation on further treatment plans. Abnormal signals were found in the right cerebellum during pituitary magnetic resonance imaging (MRI) before the development of a treatment plan for neuroendocrine tumors. Subsequently, the patient underwent cerebellar mass resection surgery. The pathological result after the surgery was hemangioblastoma.

DIAGNOSIS

The patient underwent surgery to remove the tumor and was diagnosed with hemangioblastoma by pathological examination. Subsequently, the patient's genetic testing results confirmed the diagnosis of VHL syndrome.

INTERVENTIONS

The patient underwent cerebellar mass resection surgery.

OUTCOMES

The patient recovered after surgical resection.

LESSONS

In this report, we emphasize the atypical MRI manifestations of hemangioblastoma. For patients with VHL syndrome-related hemangioblastoma, genetic testing is necessary for the patient and their family members.

Evaluation of the Value of Perfusion-Weighted Magnetic Resonance Imaging in the Differential Diagnosis of Sellar and Parasellar Tumors

The purpose of this study was to assess the value of perfusion-weighted imaging (PWI) in the differential diagnosis of sellar and parasellar tumors, as an additional sequence in the magnetic resonance imaging (MRI) protocol. Analysis was based on a substantial group of subjects and included 124 brain and pituitary MRI examinations with a dynamic susceptibility contrast (DSC) PWI sequence. The following perfusion parameters were determined for the tumors: relative cerebral blood volume (rCBV), relative peak height (rPH) and relative percentage of signal intensity recovery (rPSR). To ensure greater repeatability, each of the aforementioned parameters was calculated as: arithmetic mean of the values of the whole tumor, arithmetic mean of the maximum values on each axial slice within the tumor and maximum values derived from the whole tumor. In our study, we established that meningiomas compared to both non-functional and hormone-secreting pituitary adenomas (pituitary neuroendocrine tumors-PitNET) had significantly higher values of rCBV with cut-off points set at 3.45 and 3.54, respectively (mean rCBV). Additionally, meningiomas presented significantly higher maximum and mean maximum rPH values compared to adenomas. DSC PWI imaging adds significant value to conventional MRI examinations and can be helpful in differentiating equivocal pituitary tumors.

Imaging of Brain Tumors

OBJECTIVE

This article focuses on neuroimaging as an essential tool for diagnosing brain tumors and monitoring response to treatment.

LATEST DEVELOPMENTS

Neuroimaging is useful at all stages of brain tumor care. Technologic advances have improved the clinical diagnostic capability of neuroimaging as a vital complement to history, examination, and pathologic assessment. Presurgical evaluations are enriched by novel imaging techniques, through improved differential diagnosis and better surgical planning using functional MRI (fMRI) and diffusion tensor imaging. The common clinical challenge of differentiating tumor progression from treatment-related inflammatory change is aided by novel uses of perfusion imaging, susceptibility-weighted imaging (SWI), spectroscopy, and new positron emission tomography (PET) tracers.

ESSENTIAL POINTS

Using the most up-to-date imaging techniques will facilitate high-quality clinical practice in the care of patients with brain tumors.

Differentiation of hemangioblastoma from brain metastasis using MR amide proton transfer imaging

BACKGROUND AND PURPOSE

Differentiation between hemangioblastoma and brain metastasis remains a challenge in neuroradiology using conventional MRI. Amide proton transfer (APT) imaging can provide unique molecular information. This study aimed to evaluate the usefulness of APT imaging in differentiating hemangioblastomas from brain metastases and compare APT imaging with diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging.

METHODS

This retrospective study included 11 patients with hemangioblastoma and 20 patients with brain metastases. Region-of-interest analyses were employed to obtain the mean, minimum, and maximum values of APT signal intensity, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV), and these indices were compared between hemangioblastomas and brain metastases using the unpaired t-test and Mann-Whitney U test. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC). AUCs were compared using DeLong's method.

RESULTS

All MRI-derived indices were significantly higher in hemangioblastoma than in brain metastasis. ROC analysis revealed the best performance with APT-related indices (AUC = 1.000), although pairwise comparisons showed no significant difference between the mean ADC and mean rCBV.

CONCLUSIONS

APT imaging is a useful and robust imaging tool for differentiating hemangioblastoma from metastasis.

Advanced magnetic resonance imaging findings of cerebellar hemangioblastomas: A report of three cases and a literature review

On conventional magnetic resonance imaging (MRI), hemangioblastomas typically appear as mural nodules with an adjacent surrounding cyst or a solid mass in the cerebellum. However, hemangioblastomas sometimes cannot be reliably distinguished using this imaging technique from other tumors, especially pilocytic astrocytomas and metastatic tumors, because of their similar imaging findings and locations. Herein, we report three cases of cerebellar hemangioblastomas and review their findings on conventional and advanced MRI, including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced perfusion-weighted imaging (DSC-PWI), and magnetic resonance spectroscopy (MRS). Solid contrast-enhanced lesions of hemangioblastomas showed increased apparent diffusion coefficient values on DWI, increased relative cerebral blood volume ratio on DSC-PWI, and high lipid/lactate peak on MRS. Therefore, advanced MRI techniques can be helpful in understanding the pathological and metabolic changes of hemangioblastomas and may be useful for their characterization.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Transformational Role of Medical Imaging in (Radiation) Oncology

Simple Summary

Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints.

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Background

Diffusion-weighted imaging (DWI) is an imaging modality using multi-section single-shot spin echo planar imaging (EPI) sequence which is extremely sensitive for detection of water motion within intra, extra, and transcellular regions. This character is important to differentiate between brain tumors either low (benign) or highly (malignant) cellular tumors.

Objective

To evaluate the role of DWI and apparent diffusion coefficient (ADC) in evaluation and differentiation between different brain posterior fossa tumors in children and adults.

Patients and methods

The study included 34 patients with different brain posterior fossa tumors for evaluation by conventional MRI (using 1.5 T MRI PHILIPS Achieva 2.1 Best Netherland) and DWI.

Results

Our study showed that mean ADC values were significantly different between the four groups of posterior fossa tumors in children: juvenile pilocytic astrocytoma (JPA), medulloblastoma, ependymoma, and brain stem glioma while mean ADC values were not significantly different between posterior fossa tumors in the adult group. Regions of interest were manually positioned, and all values were automatically calculated and expressed in 10−3 mm2/s.

Conclusion

DWI is an ideal additional imaging technique, which is a rapid, easy, non-invasive imaging modality, with no contrast injection needed. It has been widely applied in the differentiation between posterior fossa brain tumors and in the diagnosis of various intracranial diseases.

Diagnostic challenges of posterior fossa hemangioblastomas: Refining current radiological classification scheme

Hemangioblastomas (HBMs) are known to exhibit very typical radiological features and thus classified by well-established radiological classification scheme. We reviewed our series of posterior fossa HBMs in order not only to evaluate the relevance of current classification scheme, but also to possibly refine it. Also, we added descriptions on several cases with unusual radiological magnetic resonance imaging (MRI) findings in which differential diagnosis was challenging. We retrospectively reviewed preoperative MRI of 118 patients with pathologically diagnosed posterior fossa HBMs at our institution between 2002 and 2015. Total 128 tumors were included to this study and classified into four categories based on the presence and nature of cystic components: extratumoral cystic (Type Ce, classical cystic with a mural nodule), intratumoral cystic (Type Ci), mixed cystic (Type Cm), and solid (Type S). The association with von Hippel-Lindau (VHL) disease was also investigated. In 118 patients (65 male and 53 female), 79 (66.9%) had solitary HBMs and 39 (33.1%) were diagnosed with VHL disease. Type Ce with typical radiological findings was the most prevalent type of HBM (63.3%), followed by Type S (21.1%). HBMs with intratumoral cysts were uncommon (Type Ci, 11.7%) and mixed extratumoral and intratumoral cysts (Type Cm) accounted for only 3.9%. No intergroup differences were observed in the proportions of each subtype between the solitary and VHL disease-associated HBMs. The blood loss was much lower in Type Ce than in other subtypes. In Type Cm, radical resection was often challenging as the differentiation between intratumoral and extratumoral cysts was difficult resulting in incomplete resection. Refined radiological classification scheme is more practical because it does not only help surgeons determine whether the cystic wall should be removed or not, but also covers cases with atypical radiological presentations. For solid and extraparenchymal HBMs, differential diagnosis is more difficult as well as very critical as surgical removal is often very challenging.

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.

Added Value of Arterial Spin-Labeling MR Imaging for the Differentiation of Cerebellar Hemangioblastoma from Metastasis

BACKGROUND AND PURPOSE

In adults with only cerebellar masses, hemangioblastoma and metastasis are the 2 most important differential diagnoses. Our aim was to investigate the added value of arterial spin-labeling MR imaging for differentiating hemangioblastoma from metastasis in patients with only cerebellar masses.

MATERIALS AND METHODS

This retrospective study included a homogeneous cohort comprising patients with only cerebellar masses, including 16 hemangioblastomas and 14 metastases. All patients underwent enhanced MR imaging, including arterial spin-labeling. First, the presence or absence of a hyperperfused mass was determined. Next, in the hyperperfused mass, relative tumor blood flow (mean blood flow in the tumor divided by blood flow measured in normal-appearing cerebellar tissue) and the size ratio (size in the arterial spin-labeling images divided by size in the postcontrast T1WI) were measured. To validate the arterial spin-labeling findings, 2 observers independently evaluated the conventional MR images and the combined set of arterial spin-labeling images.

RESULTS

All patients with hemangioblastomas and half of the patients with metastases presented with a hyperperfused mass (P < .001). The size ratio and relative tumor blood flow were significantly larger for hemangioblastomas than for metastases (P < .001 and P = .039, respectively). The size ratio revealed excellent diagnostic power (area under the curve = 0.991), and the relative tumor blood flow demonstrated moderate diagnostic power (area under the curve = 0.777). The diagnostic accuracy of both observers was significantly improved after the addition of arterial spin-labeling; the area under the curve improved from 0.574 to 0.969 (P < .001) for observer 2 and from 0.683 to 1 (P < .001) for observer 2.

CONCLUSIONS

Arterial spin-labeling imaging can aid in distinguishing hemangioblastoma from metastasis in patients with only cerebellar masses.

Advantage of High b Value Diffusion-Weighted Imaging for Differentiation of Hemangioblastoma from Brain Metastases in Posterior Fossa

OBJECTIVE

It is sometimes difficult to make a differential diagnosis between brain metastases and hemangioblastomas in the posterior fossa. We assessed whether high b value diffusion-weighted image (DWI) at b = 4000 could differentiate these tumors.

METHODS

We acquired DWI at 3-T magnetic resonance imaging with b = 1000 and b = 4000 seconds/mm² in histologically confirmed 12 patients of hemangioblastoma without von Hippel-Lindau disease and 16 patients with brain metastases originating at the posterior fossa. Apparent diffusion coefficient (ADC) values were measured by manually placing regions of interest on ADC maps at the site of enhanced tumor confirmed on contrast-enhanced T1- weighed image. ADC was expressed as the minimum (ADC_MIN), mean (ADC_MEAN), and maximum (ADC_MAX) values.

RESULTS

All the ADC values of hemangioblastomas were statistically higher than those of metastatic tumor in both b = 1000 and b = 4000 (P < 0.0001 in ADC_MIN, ADC_MEAN, and ADC_MAX; Mann-Whitney U test). With the cutoff value at 0.6 × 10^-3 mm²/second, the positive predictive value of ADC_MIN at b = 4000 was higher than that of ADC_MIN at b = 1000 (100% vs. 89.3%, logistic analysis) to differentiate hemangioblastomas from brain metastases. Moreover, we studied the pathologic subtype of hemangioblastoma and confirmed that ADCs (b = 4000_MIN) of cellular subtype were statistically lower than those of reticular subtype (P = 0.03; Mann-Whitney U test).

CONCLUSIONS

High b value DWI reflects diffusion more accurately than does regular b value. Our results showed that ADC calculation by high b value (b = 4000) DWI at 3-T magnetic resonance imaging is clinically useful for differentiating hemangioblastomas from brain metastases.