Brain MRI: tumor evaluation

Coexisting Meningioma and Glioma in the Same Patient: A Case Report

The simultaneous occurrence of two distinct intracranial neoplasms within a single patient presents a unique and exceedingly rare clinical scenario. Such cases pose significant diagnostic and therapeutic challenges, complicating clinical decision-making and treatment planning. Understanding the intricacies of these cases is crucial for improving patient outcomes and advancing clinical knowledge. This report aims to provide valuable insights into the complexities surrounding the management of patients with coexisting different intracranial neoplasms. This case report seeks to enhance the understanding of healthcare professionals dealing with similar cases and contribute to the existing literature on this rare phenomenon. This case report details a patient harboring two different but coexisting intracranial neoplasms. The patient, a 73-year-old woman, presented with behavioral changes and bilateral leg weakness. Initial imaging studies, including CT and MRI scans, revealed the presence of two distinct intracranial masses located in the frontal lobes and the falx cerebri. Neurosurgical, chemotherapy, and radiotherapy were offered; however, the patient's family opted for palliative care. The concurrent presence of two distinct intracranial neoplasms in a single patient underscores the complexity of such cases. This report highlights the diagnostic and therapeutic challenges encountered and the importance of a multidisciplinary approach to managing these patients. By sharing this case, we hope to contribute to a broader understanding of such rare clinical scenarios and aid in developing more effective management strategies in the future.

Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology

Quantitative susceptibility mapping (QSM) is a unique technique for providing quantitative information on tissue magnetic susceptibility using phase image data. QSM can provide valuable information regarding physiological and pathological processes such as iron deposition, hemorrhage, calcification, and myelin. QSM has been considered for use as an imaging biomarker to investigate physiological status and pathological changes. Although various studies have investigated the clinical applications of QSM, particularly regarding the use of QSM in clinical practice, have not been examined well. This review provides on an overview of the basics of QSM and its clinical applications in neuroradiology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study

PURPOSE

This study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T.

METHODS

Twelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared by two independent neuroradiologists in terms of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective image quality score on a ten-point Likert scale. Quantitative indices and subjective quality ratings were compared with paired Student's t tests, and interreader agreement was assessed with the intraclass correlation coefficient (ICC).

RESULTS

A total of 16 enhanced tumour lesions were detected. The SNR was significantly greater at 5.0 T than at 3.0 T in grey matter, white matter and enhanced lesions (p < 0.001). The CNR was also significantly greater at 5.0 T than at 3.0 T for grey matter/tumour lesions, white matter/tumour lesions, and grey matter/white matter (p < 0.001). Subjective evaluation revealed that the internal structure and outline of the tumour lesions were more clearly displayed with a half-dose at 5.0 T (Likert scale 8.1 ± 0.3 at 3.0 T, 8.9 ± 0.3 at 5.0 T, p < 0.001), and the effects of enhancement in the lesions were comparable to those with a full dose at 3.0 T (7.8 ± 0.3 at 3.0 T, 8.7 ± 0.4 at 5.0 T, p < 0.001). All subjective scores were good to excellent at both 5.0 T and 3.0 T.

CONCLUSION

Both quantitative and subjective evaluation parameters suggested that half-dose enhanced scanning via 5.0 T MRI might be feasible for meeting clinical diagnostic requirements, as the image quality remains optimal. Enhanced scanning at 5.0 T with a half-dose of contrast agents might benefit patients with conditions that require less intravenous contrast agent, such as renal dysfunction.

Neurite Damage in Patients with Migraine

We examined neurite orientation dispersion and density imaging in patients with migraine. We found that patients with medication overuse headache exhibited lower orientation dispersion than those without. Moreover, orientation dispersion in the body of the corpus callosum was statistically negatively correlated with migraine attack frequencies. These findings indicate that neurite dispersion is damaged in patients with chronic migraine. Our study results indicate the orientation preference of neurite damage in migraine.

Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology

BACKGROUND AND PURPOSE

Glioblastomas and metastases are the most common malignant intra-axial brain tumors in adults and can be difficult to distinguish on conventional MR imaging due to similar imaging features. We used advanced diffusion techniques and structural histopathology to distinguish these tumor entities on the basis of microstructural axonal and fibrillar signatures in the contrast-enhancing tumor component.

MATERIALS AND METHODS

Contrast-enhancing tumor components were analyzed in 22 glioblastomas and 21 brain metastases on 3T MR imaging using DTI-fractional anisotropy, neurite orientation dispersion and density imaging-orientation dispersion, and diffusion microstructural imaging-micro-fractional anisotropy. Available histopathologic specimens (10 glioblastomas and 9 metastases) were assessed for the presence of axonal structures and scored using 4-level scales for Bielschowsky staining (0: no axonal structures, 1: minimal axonal fragments preserved, 2: decreased axonal density, 3: no axonal loss) and glial fibrillary acid protein expression (0: no glial fibrillary acid protein positivity, 1: limited expression, 2: equivalent to surrounding parenchyma, 3: increased expression).

RESULTS

When we compared glioblastomas and metastases, fractional anisotropy was significantly increased and orientation dispersion was decreased in glioblastomas (each P < .001), with a significant shift toward increased glial fibrillary acid protein and Bielschowsky scores. Positive associations of fractional anisotropy and negative associations of orientation dispersion with glial fibrillary acid protein and Bielschowsky scores were revealed, whereas no association between micro-fractional anisotropy with glial fibrillary acid protein and Bielschowsky scores was detected. Receiver operating characteristic curves revealed high predictive values of both fractional anisotropy (area under the curve = 0.8463) and orientation dispersion (area under the curve = 0.8398) regarding the presence of a glioblastoma.

CONCLUSIONS

Diffusion imaging fractional anisotropy and orientation dispersion metrics correlated with histopathologic markers of directionality and may serve as imaging biomarkers in contrast-enhancing tumor components.

MRI radiomics for brain metastasis sub-pathology classification from non-small cell lung cancer: a machine learning, multicenter study

The objective of this study is to develop a machine-learning model that can accurately distinguish between different histologic types of brain lesions in patients with non-small cell lung cancer (NSCLC) when it is not safe or feasible to perform a biopsy. To achieve this goal, the study utilized data from two patient cohorts: 116 patients from Xiangya Hospital and 35 patients from Yueyang Central Hospital. A total of eight machine learning algorithms, including Xgboost, were compared. Additionally, a 3-dimensional convolutional neural network was trained using transfer learning to further evaluate the performance of these models. The SHapley Additive exPlanations (SHAP) method was developed to determine the most important features in the best-performing model after hyperparameter optimization. The results showed that the area under the curve (AUC) for the classification of brain lesions as either lung adenocarcinoma or squamous carcinoma ranged from 0.60 to 0.87. The model based on single radiomics features extracted from contrast-enhanced T1 MRI and utilizing the Xgboost algorithm demonstrated the highest performance (AUC: 0.85) in the internal validation set and adequate performance (AUC: 0.80) in the independent external validation set. The SHAP values also revealed the impact of individual features on the classification results. In conclusion, the use of a radiomics model incorporating contrast-enhanced T1 MRI, Xgboost, and SHAP algorithms shows promise in accurately and interpretably identifying brain lesions in patients with NSCLC.

Advances in the application of neuroinflammatory molecular imaging in brain malignancies

The prevalence of brain cancer has been increasing in recent decades, posing significant healthcare challenges. The introduction of immunotherapies has brought forth notable diagnostic imaging challenges for brain tumors. The tumor microenvironment undergoes substantial changes in induced immunosuppression and immune responses following the development of primary brain tumor and brain metastasis, affecting the progression and metastasis of brain tumors. Consequently, effective and accurate neuroimaging techniques are necessary for clinical practice and monitoring. However, patients with brain tumors might experience radiation-induced necrosis or other neuroinflammation. Currently, positron emission tomography and various magnetic resonance imaging techniques play a crucial role in diagnosing and evaluating brain tumors. Nevertheless, differentiating between brain tumors and necrotic lesions or inflamed tissues remains a significant challenge in the clinical diagnosis of the advancements in immunotherapeutics and precision oncology have underscored the importance of clinically applicable imaging measures for diagnosing and monitoring neuroinflammation. This review summarizes recent advances in neuroimaging methods aimed at enhancing the specificity of brain tumor diagnosis and evaluating inflamed lesions.

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.

Before the Knife: A Detailed Step-by-Step Description of an Optimized Semi-Sitting Position in Posterior Fossa Surgery

BACKGROUND

In an ample armamentarium in neurosurgery, the semi-sitting position has produced debate regarding its benefits and risks. Although the position is apparently intuitive, many have abandoned its use since its initial inception, because of reported complexity and potential complications, leading to impracticality. However, through standardization, it has been shown not only to be safe but to carry with it many advantages, including less risk of secondary neurovascular injuries and better visualization of the surgical field. As with any surgical technical nuance, the semi-sitting position has advantages and disadvantages that must be weighed before the decision is made to adopt it or not, not only in a case-by-case scenario but also from a departmental standpoint. As we attempt to show, the advantages from a standardized approach for the semi-sitting position in experienced institutions may be more than sufficient to significantly outweigh the disadvantages, making it the preferable option for most, although not all, posterior fossa surgical interventions.

METHODS

In the present study, we aim to elaborate a straightforward narrative of the steps before incision, in an attempt to simplify the complexity of the position, alleviating its disadvantages and exponentially concentrating on its benefits. In nearly 100 steps, we carefully describe the points that culminate with the skin incision, initiating the intraoperative part of the procedure. Each step, therefore, is detailed in full, not in an effort to create a strict manual of the semi-sitting position but rather to facilitate understanding and put the technique into effect in a real-life scenario, thus simplifying what some depict as complex and time consuming.

CONCLUSIONS

Although several of the steps described are also relevant and integral parts of other surgical positioning, we intend to create a protocol, in a stepwise fashion, to allow facilitated following, to be easily implemented in departments with different levels of experience. The steps comprise nursing care through to electrophysiologic and anesthesiologic approaches, along with neurosurgical cooperation, making it a team approach, not only to avoid position-related complications but also to optimize preoperative standardization, constructing a safe, efficient, and patient-centered scenario, to set the best possible stage for the next step: the intraoperative part of the intervention.

Uncommon and atypical meningiomas and imaging variants: A report of 7 cases

Meningiomas constitute the most common extra-axial tumor of the central nervous system and can have a wide-ranging manifestation of imaging. There are several types of unusual depictions depicted with the magnetic resonance imaging (MRI) of meningiomas that have been established thus far. It is thus crucial for the reporting radiologist or neurosurgeon to have an in-depth knowledge of their variable manifestations in order to be able to differentiate these neoplasms from the numerous tumors that can mimic their appearance. Meningioma is frequently challenging to diagnose when imaging variants are present. Nevertheless, a number of unusual histological variants have imaging or clinical features which are related to typical meningiomas and, in numerous cases, these require specific surgical management. The present study describes 7 cases of meningiomas, which were either simple atypical, unusual gigantic extracranial intracranial parasagittal, or not visible meningiomas. These uncommon and atypical imaging variants of meningiomas are described herein in an aim to underline their various potential presentations.

Recurrent Facial Focal Seizures With Chronic Striatopathy and Caudate Atrophy-A Double Whammy in an Elderly Woman With Diabetes Mellitus

Seizures and involuntary movements are relatively rare, but well-known neurological complications of non-ketotic hyperglycemia. While hemichorea-hemiballism secondary to diabetic striatopathy is increasingly being reported, unilateral caudate atrophy resulting from chronic vascular insufficiency/insult in a backdrop of poorly controlled diabetes mellitus is sparsely described in literature. We herein report a 75-year-old woman with poorly controlled diabetes mellitus who presented with concurrent epilepsia partialis continua involving left side of her face and hemichorea on the right side in the context of non-ketotic hyperglycemia. Neuroimaging revealed a space-occupying lesion suggestive of low-grade glioma in the right superior frontal cortex and left-sided caudate atrophy as well. Possibly, space-occupying lesion in motor cortex acted as an inciting factor for seizures and non-ketotic hyperglycemia further lowered the seizures threshold. On the other hand, atrophied left caudate had led to persistent choreiform movements secondary to chronic uncontrolled hyperglycemia. The simultaneous presence of acute and chronic neurological complications of diabetes mellitus makes this case unique. It also highlights the need for strict control of blood glucose and utility of appropriate neuroimaging to rapidly diagnose and prevent further complications.

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Somatic mitochondrial DNA mutations in different grades of glioma

Aim: Mitochondrial DNA (mtDNA) alterations play an important role in the multistep processes of cancer development. Gliomas are among the most diagnosed brain cancer. The relationship between mtDNA alterations and different grades of gliomas are still elusive. This study aimed to elucidate the profile of somatic mtDNA mutations in different grades of gliomas and correlate it with clinical phenotype. Materials & methods: Forty histopathologically confirmed glioma tissue samples and their matched blood were collected and subjected for mtDNA sequencing. Results & conclusion: About 75% of the gliomas harbored at least one somatic mutation in the mtDNA gene, and 45% of these mutations were pathogenic. Mutations were scattered across the mtDNA genome, and the commonest nonsynonymous mutations were located at complex I and IV of the mitochondrial respiratory chain. These findings may have implication for future research to determine the mitochondrial energetics and its downstream metabolomics on gliomas.

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy

In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high- and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene. Thus, using selective attention technique to develop the CNNs model, helps to maximize the extraction of relevant features from MRI images. We have also addressed two more issues: class-imbalance, and the spatial relationship among image Patches. To address the first issue, we propose two steps: an equal sampling of images Patches and an experimental analysis of the effect of weighted cross-entropy loss function on the segmentation results. In addition, to overcome the second issue, we have studied the effect of Overlapping Patches against Adjacent Patches where the Overlapping Patches show better segmentation results due to the introduction of the global context as well as the local features of the image Patches compared to the conventionnel Adjacent Patches. Our experiment results are reported on BRATS-2018 dataset where our End-to-End Deep Learning model achieved state-of-the-art performance. The median Dice score of our fully automatic segmentation model is 0.90, 0.83, 0.83 for the whole tumor, tumor core, and enhancing tumor respectively compared to the Dice score of radiologist, that is in the range 74% - 85%. Moreover, our proposed CNNs model is not only computationally efficient at inference time, but it could segment the whole brain on average 12 seconds. Finally, the proposed Deep Learning model provides an accurate and reliable segmentation result, and that makes it suitable for adopting in research and as a part of different clinical settings.

Usefulness of Histogram-Profile Analysis in Ring-Enhancing Intracranial Lesions

BACKGROUND

Several intracranial pathologies present as a ring-enhancing lesion on conventional magnetic resonance imaging (MRI), creating diagnostic difficulty. We studied the characteristics of the anatomical border of gadolinium enhancement on T1-weighted imaging (WI) and hypointensity on T2WI to employ a simple technique of histogram-profile analysis of MRI for differentiation of various ring-enhancing intracranial lesions.

METHODS

After approval from the institutional review board, preoperative MRI (T2WI, postcontrast T1WI) scans were analyzed retrospectively in 18 patients with histologically confirmed brain abscess, 66 glioblastomas, 46 brain-metastases, and 16 tumefactive multiple sclerosis (MS). T2WI and postcontrast T1WI were overlapped, and histogram-profile analysis was performed with in-house image-fusion software. The pattern of differential-peaks in histogram-profile was assessed visually. Kaplan-Meier survival analysis incorporating histogram-profile patterns was performed in patients with glioblastoma.

RESULTS

The histogram-profile study revealed 4 distinct patterns. Pattern 1 showed no differential T2-hypointensity trough, pattern 2 had T2-hypointensity trough inside, whereas pattern 3 had T2-hypointensity trough overlapping the enhanced margin. Pattern 4 had T2-hypointensity trough immediately external to the enhanced margin. Pattern 1 was specific for tumefactive MS (93.3%), whereas pattern 4 was specific for glioblastoma (40.7%). Pattern 4 glioblastoma was subdivided into rim (T2-hypointensity ≥50% of circumference of contrast-enhanced tumor) and arc (T2-hypointensity <50% of circumference of contrast-enhanced tumor). Pattern 4 glioblastoma was further subdivided into group A (edema: T2-hyperintensity ≥50% of circumference of contrast-enhanced tumor) and group B (less edema: T2-hyperintensity <50% of circumference of contrast-enhanced tumor). Patients with pattern 3 glioblastoma (37.6%) had better survival compared with others (P = 0.0341) and pattern 4B had decreased survival compared with pattern 4A (P = 0.0001) and others (P = 0.0003).

CONCLUSIONS

Tumefactive MS and a subset of glioblastomas show specific patterns in histogram-profile analysis. The difference in anatomical border also determines difference in survival in glioblastoma. Histogram-profile analysis is a simple and efficient technique to differentiate these pathologies.

Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma

BACKGROUND

Accurate glioma grading plays an important role in patient treatment.

PURPOSE

To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM).

STUDY TYPE

Retrospective.

POPULATION

In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007.

FIELD STRENGTH/SEQUENCE

3.0T MRI/ T₁ WI, T₂ fluid-attenuated inversion recovery, contrast enhanced T₁ , arterial spinal labeling, diffusion-weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm² ), and dynamic contrast-enhanced.

ASSESSMENT

Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF-SVM) combined with attribute selection using SVM-recursive feature elimination (SVM-RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10-fold crossvalidation. The model performance was further tested using the remaining 20% data.

STATISTICAL TESTS

Ten-fold crossvalidation was used to validate the model performance.

RESULTS

Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM-RFE was able to reduce attribute redundancy as well as improve RBF-SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray-level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM-RFE to provide comparable classifying efficacy.

DATA CONCLUSION

When using image textures-based SVM classification of gliomas, the GLSZM model in combination with gray-level 64 and attribute selection may be an optimized solution.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263-1274.

Development and validation of a new MRI simulation technique that can reliably estimate optimal in vivo scanning parameters in a glioblastoma murine model

BACKGROUND

Magnetic Resonance Imaging (MRI) relies on optimal scanning parameters to achieve maximal signal-to-noise ratio (SNR) and high contrast-to-noise ratio (CNR) between tissues resulting in high quality images. The optimization of such parameters is often laborious, time consuming, and user-dependent, making harmonization of imaging parameters a difficult task. In this report, we aim to develop and validate a computer simulation technique that can reliably provide "optimal in vivo scanning parameters" ready to be used for in vivo evaluation of disease models.

METHODS

A glioblastoma murine model was investigated using several MRI imaging methods. Such MRI methods underwent a simulated and an in vivo scanning parameter optimization in pre- and post-contrast conditions that involved the investigation of tumor, brain parenchyma and cerebrospinal fluid (CSF) CNR values in addition to the time relaxation values of the related tissues. The CNR tissues information were analyzed and the derived scanning parameters compared in order to validate the simulated methodology as a reliable technique for "optimal in vivo scanning parameters" estimation.

RESULTS

The CNRs and the related scanning parameters were better correlated when spin-echo-based sequences were used rather than the gradient-echo-based sequences due to augmented inhomogeneity artifacts affecting the latter methods. "Optimal in vivo scanning parameters" were generated successfully by the simulations after initial scanning parameter adjustments that conformed to some of the parameters derived from the in vivo experiment.

CONCLUSION

Scanning parameter optimization using the computer simulation was shown to be a valid surrogate to the in vivo approach in a glioblastoma murine model yielding in a better delineation and differentiation of the tumor from the contralateral hemisphere. In addition to drastically reducing the time invested in choosing optimal scanning parameters when compared to an in vivo approach, this simulation program could also be used to harmonize MRI acquisition parameters across scanners from different vendors.

Health care use before a diagnosis of primary intracranial tumor: a Danish nationwide register study

Introduction

Detailed knowledge of prediagnostic health care use among patients with primary intracranial tumors is sparse. We aimed to investigate the health care use among adults during the 2 years preceding a diagnosis of a benign or malignant primary intracranial tumor in Denmark.

Methods

We conducted a population-based matched cohort study using historical data from Danish nationwide registries, including all patients aged 30–90 years diagnosed with a first-time primary intracranial tumor from January 1, 2009 to December 31, 2014, and with no prior cancer diagnosis (n=5,926). For each patient, ten comparison subjects were identified using density sampling. We analyzed differences in the frequency and timing of health care use within general practice, physiotherapy, radiology, ear –nose –throat, ophthalmology, neurology, and psychiatry. Odds ratios of having multiple contacts were calculated using a conditional logistical regression model. Monthly incidence rate ratios were estimated using a negative binomial regression model.

Results

Of all patients, 62% had a benign tumor. Patients with benign tumors were more likely to have multiple consultations with health care providers in the period 2–12 months prior to diagnosis and to have increased rates of consultations 1–24 months prior to diagnosis, depending on health service.

Conclusion

Patients diagnosed with a benign or a malignant primary intracranial tumor use the health care services differently. Increased health care use is seen within relatively close proximity to the diagnosis for patients with malignant tumors. However, patients with benign tumors have increased health care use from up to 2 years prior to diagnosis; this suggests a window of opportunity for earlier diagnosis.

Precision Medicine in Pediatric Neurooncology: A Review

Central nervous system tumors are the leading cause of cancer related death in children. Despite much progress in the field of pediatric neurooncology, modern combination treatment regimens often result in significant late effects, such as neurocognitive deficits, endocrine dysfunction, secondary malignancies, and a host of other chronic health problems. Precision medicine strategies applied to pediatric neurooncology target specific characteristics of individual patients' tumors to achieve maximal killing of neoplastic cells while minimizing unwanted adverse effects. Here, we review emerging trends and the current literature that have guided the development of new molecularly based classification schemas, promising diagnostic techniques, targeted therapies, and delivery platforms for the treatment of pediatric central nervous system tumors.

Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases

BACKGROUND

Conventional magnetic resonance imaging (MRI) is considered a valuable tool for preoperative diagnosis of intracranial tumors. We assessed its accuracy in the diagnosis of intracranial tumors in usual clinical practice.

MATERIALS AND METHODS

MRI reports of 762 patients who had undergone conventional brain MRI prior to surgery were retrospectively reviewed. A 4-grade scoring system was devised to establish diagnostic agreement. Each tumor type was compared with the corresponding pathological diagnoses by dichotomization. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the overall patient population as well as for each tumor type.

RESULTS

664 cases (87.1%) were tumor-positive, and 98 cases (12.9%) were tumor-negative. The most common tumor types were meningiomas, gliomas, pituitary adenomas and schwannomas. These four types together comprised 74.5% of all cases reviewed. Sensitivity and PPV for the overall population were 72.0-90.7% and 91.9-95.4%, respectively. Diagnostic accuracy differed among tumor types. Meningiomas, pituitary adenomas, schwannomas and cholesteatomas were more likely to be diagnosed correctly (sensitivities were 82.6-96.9%, 86.1-96.7%, 88.9-98.2% and 91.3-100.0%, respectively); while some other types like solitary fibrous tumors (SFTs) seemed difficult to identify. Gliomas tended to be confused with metastases, meningiomas with SFTs, and pituitary adenomas with craniopharyngiomas.

CONCLUSION

The accuracy of conventional MRI for diagnosing intracranial tumors is generally satisfactory but should not be too heavily relied upon, especially for certain tumor types. In cases of discrepancy, neurosurgeons are encouraged to confer with the reporting neuroradiologists to achieve optimal preoperative diagnoses.

Image based modeling of tumor growth

Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients' quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.

Neuroendocrine disorders: pituitary imaging

Significant advances in pituitary imaging have taken place in the past several decades, including the introduction of magnetic resonance imaging (MRI). This imaging modality has vastly improved our ability to detect and characterize sellar masses and more accurately characterize the extent and spread of lesions in and around the sella. Intraoperative MRI may help improve the completeness of resection of sellar masses. Other imaging modalities, including magnetic resonance angiography, computed tomography (CT), and CT angiography, have an important role in specific cases. Interventional methods, including bilateral inferior petrosal sinus sampling, may establish the pituitary origin of corticotropin (ACTH) excess in patients with ACTH-dependent Cushing's syndrome. Pituitary imaging should be obtained in patients with pituitary hormone excess, hypopituitarism, or mass effect in the sella. Despite rapid advances in pituitary imaging, there are several diagnostic challenges remaining. Future research may help improve the radiographic detection of small sellar lesions, such as ACTH-secreting adenomas causing Cushing's disease, accurately characterize the type and extent of sellar pathologies, and provide prognostic information regarding their growth potential.

Preclinical imaging and translational animal models of cancer for accelerated clinical implementation of nanotechnologies and macromolecular agents

The majority of animal models of cancer have performed poorly in terms of predicting clinical performance of new therapeutics, which are most often first evaluated in patients with advanced, metastatic disease. The development and use of metastatic models of cancer may enhance clinical translatability of preclinical studies focused on the development of nanotechnology-based drug delivery systems and macromolecular therapeutics, potentially accelerating their clinical implementation. It is recognized that the development and use of such models are not without challenge. Preclinical imaging tools offer a solution by allowing temporal and spatial characterization of metastatic lesions. This paper provides a review of imaging methods applicable for evaluation of novel therapeutics in clinically relevant models of advanced cancer. An overview of currently utilized models of oncology in small animals is followed by image-based development and characterization of visceral metastatic cancer models. Examples of imaging tools employed for metastatic lesion detection, evaluation of anti-tumor and anti-metastatic potential and biodistribution of novel therapies, as well as the co-development and/or use of imageable surrogates of response, are also discussed. While the focus is on development of macromolecular and nanotechnology-based therapeutics, examples with small molecules are included in some cases to illustrate concepts and approaches that can be applied in the assessment of nanotechnologies or macromolecules.

Prognostic Value of Conventional Magnetic Resonance Imaging for Adult Patients with Brain Tumors

PURPOSE

Magnetic resonance imaging (MRI) is the pivotal diagnostic step in patients with brain tumors, and is performed before histological diagnosis is available. We hypothesized that conventional MRI is as accurate as tumor histology in differentiating malignant from benign clinical course.

METHODS

Two neuroradiologists blinded to any clinical information evaluated the first diagnostic MRI of 244 brain tumor patients before any treatment, using a self-developed standardized list of image criteria and prospectively determined world health organization (WHO) tumor grade and tumor entity. All patients were examined with at least T1- and T2-weighted spin echo sequences before and after contrast injection on 1 and 1.5-T MRI scanners. Following the patients prospectively for 8-13 years after diagnosis, we were able to use nonsurvival at 5 years as a criterion for malignity and reference for the prognostic accuracy of both MRI and tumor tissue histology.

RESULTS

The accuracy for predicting nonsurvival at 5 years was 91% (95% confidence interval (CI): 87-94%) for MRI and 92% (95% CI: 88-95%) for histology. The Kaplan-Meier survival curves of patients with benign and malignant brain tumors as diagnosed by MRI or histology differed significantly (p < 0.001). Histology confirmed benignity or malignity in 201 patients (82%, 95% CI: 77-87%). Sources of misdiagnosis were metastases diagnosed as astrocytoma WHO IV, atypical meningiomas, and low-grade astrocytoma with malignant transformation.

CONCLUSION

MRI appears as accurate as histology in predicting survival at 5 years after diagnosis. Histological diagnosis may be more specific, however, and is needed to assess the tumor's specific biology.

Awake surgery between art and science. Part I: clinical and operative settings

Awake surgery requires coordinated teamwork and communication between the surgeon and the anesthesiologist, as he monitors the patient, the neuroradiologist as he interprets the images for intraoperative confirmation, and the neuropsychologist and neurophysiologist as they evaluate in real-time the patient's responses to commands and questions. To improve comparison across published studies on clinical assessment and operative settings in awake surgery, we reviewed the literature, focusing on methodological differences and aims. In complex, interdisciplinary medical care, such differences can affect the outcome and the cost-benefit ratio of the treatment. Standardization of intraoperative mapping and related controversies will be discussed in Part II.

Neurosurgery and prognosis in patients with radiation-induced brain injury after nasopharyngeal carcinoma radiotherapy: a follow-up study

Background

Radiotherapy is the standard radical treatment for nasopharyngeal carcinoma (NPC) and may cause radiation-induced brain injury (RI). Treatment for RI remains a challenge. We conducted this study to investigate the indications of neurosurgery, operation time and prognosis of patients with RI after NPC radiotherapy who underwent neurosurgical management.

Methods

This was a follow-up study between January 2005 and July 2011. Fifteen NPC cases of RI who underwent neurosurgery were collected. Brain Magnetic resonance imaging (MRI), surgery and histology were studied. The outcome was assessed by LENT/SOMA scales and modified Rankin scale.

Results

Brain lesion resection (86.7%) was more common than decompressive craniotomy (13.3%). According to LENT/SOMA scale before and six months after surgery, 13 of 15, 12 of 15, 14 of 15, and 14 of 15 cases showed improvement at subjective, objective, management and analytic domains, respectively. 12 of 15 patients showed improvement of modified Rankin scale after surgery. Three patients who underwent emergency surgery showed significant improvement (average score increment of 2, 2.7, 2.7, 3 and 2 at LENT/SOMA scale subjective, objective, management, analytic, and modified Rankin scale, respectively), as compared with 12 cases underwent elective surgery (average score increment of 1, 1, 1.4, 1.8 and 1 at LENT SOMA scale subjective, objective, management, analytic, and modified Rankin scale, respectively).

Conclusions

Neurosurgery, including brain necrotic tissue resection and decompressive craniotomy, improves the prognosis for RI patients, especially for those with indications of emergency surgery.

Optimizing a protocol for (1) H-magnetic resonance spectroscopy of the canine brain at 3T

Intracranial diseases are common in dogs and improved noninvasive diagnostic tests are needed. Magnetic resonance (MR) spectroscopy is a technique used in conjunction with conventional MR imaging to characterize focal and diffuse pathology, especially in the brain. As with conventional MR imaging, there are numerous technical factors that must be considered to optimize image quality. This study was performed to develop an MR spectroscopy protocol for routine use in dogs undergoing MR imaging of the brain. Fifteen canine cadavers were used for protocol development. Technical factors evaluated included use of single-voxel or multivoxel acquisitions, manual placement of saturation bands, echo time (TE), phase- and frequency-encoding matrix size, radiofrequency coil, and placement of the volume of interest relative to the calvaria. Spectrum quality was found to be best when utilizing a multivoxel acquisition with the volume of interest placed entirely within the brain parenchyma without use of manually placed saturation bands, TE = 144 ms, and a quadrature extremity radiofrequency coil. An 18 × 18 phase- and frequency-encoding matrix size also proved optimal for image quality, specificity of voxel placement, and imaging time.

MRI perfusion in determining pseudoprogression in patients with glioblastoma

We examine the role of dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion in differentiating pseudoprogression from progression in 20 consecutive patients with treated glioblastoma. MRI perfusion was performed, and relative cerebral blood volume (rCBV), relative peak height (rPH), and percent signal recovery (PSR) were measured. Pseudoprogression demonstrated lower median rCBV (P=.009) and rPH (P<.001), and higher PSR (P=.039) than progression. DSC MRI perfusion successfully identified pseudoprogression in patients who did not require a change in treatment despite radiographic worsening following chemoradiotherapy.

Boron neutron capture therapy (BNCT) in Finland: technological and physical prospects after 20 years of experiences

Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy method developed to treat patients with certain malignant tumours. To date, over 300 treatments have been carried out at the Finnish BNCT facility in various on-going and past clinical trials. In this technical review, we discuss our research work in the field of medical physics to form the groundwork for the Finnish BNCT patient treatments, as well as the possibilities to further develop and optimize the method in the future. Accordingly, the following aspects are described: neutron sources, beam dosimetry, treatment planning, boron imaging and determination, and finally the possibilities to detect the efficacy and effects of BNCT on patients.

The use of neuroimaging to guide the histologic diagnosis of central nervous system lesions

Recent advances in neuroimaging techniques, particularly in magnetic resonance imaging, have led to substantially improved spatial anatomic resolution such that subtle or small central nervous system lesions, which could go undetected on gross examination of brain sections, are now readily identified on imaging. Although neuroimaging is generally considered the surrogate of gross neuropathology, it is still not a substitute for tissue diagnosis. Rather, it can be a valuable tool for the surgical pathologist in the process of formulating a differential diagnosis based on location and imaging features, as well as in identifying radiologic/pathologic discordance, such as the possible undersampling of a heterogenous glioma, which could lead to underestimation of the tumor grade. The following review focuses on the application of neuroimaging techniques, mainly magnetic resonance imaging, to the histologic diagnosis of central nervous system lesions, and the correlation of imaging features of infiltrative gliomas with histologic findings pertinent to tumor grading. The use of advanced functional magnetic resonance methods, specifically diffusion-weighted imaging, perfusion-weighted imaging, and magnetic resonance spectroscopy is also discussed, as well as the common pitfalls in imaging interpretation.

Multi-parametric analysis and registration of brain tumors: constructing statistical atlases and diagnostic tools of predictive value

We discuss computer-based image analysis algorithms of multi-parametric MRI of brain tumors, aiming to assist in early diagnosis of infiltrating brain tumors, and to construct statistical atlases summarizing population-based characteristics of brain tumors. These methods combine machine learning, deformable registration, multi-parametric segmentation, and biophysical modeling of brain tumors.

Analytical methods for brain targeted delivery system in vivo: perspectives on imaging modalities and microdialysis

Since the introduction of microdialysis in 1974, the semi-invasive analytical method has grown exponentially. Microdialysis is one of the most potential analysis technologies of pharmacological drug delivery to the brain. In recent decades, analysis of chemicals targeting the brain has led to many improvements. It seems likely that fluorescence imaging was limited to ex vivo and in vitro applications with the exception of several intravital microscopy and photographic imaging approaches. X-ray computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) have been commonly utilized for visualization of distribution and therapeutic effects of drugs. The efficient analytical methods for studies of brain-targeting delivery system is a major challenge in detecting the disposition as well as the variances of the factors that regulate the substances delivery into the brain. In this review, we highlight some of the ongoing trends in imaging modalities and the most recent developments in the field of microdialysis of live animals and present insights into exploiting brain disease for therapeutic and diagnostics purpose.

Isolated diffusion restriction precedes the development of enhancing tumor in a subset of patients with glioblastoma

BACKGROUND AND PURPOSE

Most response criteria for patients with glioblastoma rely on increases in the contrast enhancing abnormality to determine tumor progression. Our aim was to determine retrospectively in patients with glioblastoma whether diffusion restriction can predict the development of new enhancing mass lesions.

MATERIALS AND METHODS

We reviewed the brain MR imaging scans (including DWI and ADC maps) of 208 patients with glioblastoma. Patients with restricted diffusion in or adjacent to the tumor were identified, with further analysis only performed on those patients with low-ADC lesions without enhancement. These patients were followed to determine if new concordant enhancement developed at the site of the low-ADC lesion. A Wilcoxon signed rank test, competing risk analysis, and Kaplan-Meier curves were used to compare the mean drop in ADC values, assess enhancement-free survival, and determine overall survival, respectively.

RESULTS

In 67 of the 208 patients (32.2%), visibly detectable restricted diffusion was seen during treatment. The study cohort was formed by the 27 patients with low-ADC lesions and no corresponding enhancement. Twenty-three (85.2%) patients developed gadolinium-enhancing tumor at the site of restricted diffusion a median of 3.0 months later (95% CI, 2.6-4.1 months). The mean decrease in ADC was 22.9% from baseline (P < .001). The 3-month enhancement-free survival probability was 0.481 (95% CI, 0.288-0.675). The 12-month overall survival probability was 0.521 (95% CI, 0.345-0.788). Restricted diffusion predicted enhancement regardless of antiangiogenic therapy with bevacizumab.

CONCLUSIONS

In a subset of patients with glioblastoma, development of a new focus of restricted diffusion during treatment may precede the development of new enhancing tumor.

Imaging of brain tumors: functional magnetic resonance imaging and diffusion tensor imaging

The eloquent brain can be identified using functional MR (fMR) imaging for the gray matter and diffusion tensor (DT) imaging for the white matter. fMR imaging and DT imaging are especially important for patients with tumors near the important motor and language centers of the brain, where the normal anatomic references may be distorted by the tumor and associated edema. This article explains fMR imaging and DT imaging techniques and illustrates their clinical applications and limitations.

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region-of-interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis (24), meningiomas (4), gliomas World Health Organization grade II (22), gliomas World Health Organization grade III (18), and glioblastomas (34). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high-grade (grades III and IV) from low-grade (grade II) neoplasms. Multiclass classification was also performed via a one-vs-all voting scheme.

Potential role of CT perfusion parameters in the identification of solitary intra-axial brain tumor grading

In this study, neoplastic perfusion abnormalities were investigated by computed tomography perfusion (CTP) scanning in 38 patients with solitary intra-axial brain tumors (19 with high grade gliomas, 7 with low grade gliomas and 12 with brain metastasis). Regional cerebral blood flow (rCBF), cerebral blood volume (rCBV), mean transit time (rMTT) and permeability surface flow (rPSF) levels were measured in two different regions of interest: (1) enhancing or non-enhancing tumor tissue and (2) a mirror area of apparently normal brain tissue located in the contralateral hemisphere. rCBF mean levels were greater in tumoral tissue than in the contralateral area for high-grade gliomas (p < 0.02). rCBV and rPSF mean values were higher in tumoral tissue than in the contralateral area for high-grade gliomas (p < 0.01 and p < 0.05, respectively) and metastasis (p < 0.05 and p < 0.001, respectively). rCBV mean values of tumoral tissue were greater in high-grade than in low-grade gliomas (p < 0.05). rPSF mean levels of tumoral tissue were higher in metastasis than in low-grade gliomas (p < 0.02). These findings indicate that multi-parametric CTP mapping may contribute to differential diagnosis of solitary intra-axial brain tumors.

MRI and MRS of human brain tumors

The purpose of this chapter is to provide an introduction to magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) of human brain tumors, including the primary applications and basic terminology involved. Readers who wish to know more about this broad subject should seek out the referenced books (1. Tofts (2003) Quantitative MRI of the brain. Measuring changes caused by disease. Wiley; Bradley and Stark (1999) 2. Magnetic resonance imaging, 3rd Edition. Mosby Inc; Brown and Semelka (2003) 3. MRI basic principles and applications, 3rd Edition. Wiley-Liss) or reviews (4. Top Magn Reson Imaging 17:127-36, 2006; 5. JMRI 24:709-724, 2006; 6. Am J Neuroradiol 27:1404-1411, 2006).MRI is the most popular means of diagnosing human brain tumors. The inherent difference in the magnetic resonance (MR) properties of water between normal tissues and tumors results in contrast differences on the image that provide the basis for distinguishing tumors from normal tissues. In contrast to MRI, which provides spatial maps or images using water signals of the tissues, proton MRS detects signals of tissue metabolites. MRS can complement MRI because the observed MRS peaks can be linked to inherent differences in biochemical profiles between normal tissues and tumors.The goal of MRI and MRS is to characterize brain tumors, including tumor core, edge, edema, volume, types, and grade. The commonly used brain tumor MRI protocol includes T2-weighted images and T1-weighted images taken both before and after the injection of a contrast agent (typically gadolinium: Gd). The commonly used MRS technique is either point-resolved spectroscopy (PRESS) or stimulated echo acquisition mode (STEAM).

Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images

RATIONALE AND OBJECTIVES

Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. Magnetic resonance imaging (MRI) is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques such as perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multiparametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter.

MATERIALS AND METHODS

Five structural MRI sequences, namely, B0, diffusion-weighted images, fluid-attenuated inversion recovery, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI (ie, fractional anisotropy and apparent diffusion coefficient) are used to create an intensity-based tissue profile. This is incorporated into a nonlinear pattern classification technique to create a multiparametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high-grade neoplasms who have not received any therapy before imaging.

RESULTS

Preliminary results demonstrate that this multiparametric tissue characterization helps to better differentiate among neoplasm, edema, and healthy tissue, and to identify tissue that is likely to progress to neoplasm in the future. This has been validated on expert assessed tissue.

CONCLUSION

This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence.