Differentiation of glioblastoma and cerebral metastasis using MR-derived tissue oxygenation and perfusion: a machine learning approach

Purpose: This prospective clinical study was aimed at differentiating glioblastoma and cerebral metastasis, two tumor entities that often show similar radiological features, by means of combined MR oxygenation and perfusion imaging. Their distinction is highly important due to vastly differing therapy algorithms as well as patient outcomes. It was hypothesized that the infiltrative growth pattern of glioblastomas and the lack thereof in brain metastases would make it possible to distinguish the two groups based on their metabolic parameters in and around the contrast-enhancing part of the tumor. Materials and Methods: Fifteen previously untreated patients were recruited, seven of which suffered from glioblastoma (median age: 68 years, range: 54 – 84 years) with the remaining eight showing one or multiple brain metastases (median age 66 years, range: 50 – 78 years). All patients underwent preoperative MRI scans including multi-gradient echo and pseudo-continuous arterial spin labeling sequences. Three regions of interest were segmented in post-processing: contrast-enhancing tumor (CET), contralateral normal-appearing brain tissue (cNAB) and peritumoral non-enhancing T2-weighted fluid-attenuated inversion recovery hyperintense area (NET2). For these regions, oxygen extraction fraction (OEF) and cerebral blood flow (CBF) were estimated, yielding a third parameter: cerebral metabolic rate of oxygen (CMRO2). Two different machine learning-based approaches were employed to calculate OEF: an artificial neural network (ANN) and X-means clustering, both estimating the solution of the quantitative susceptibility mapping and quantitative blood-oxygen-level-dependent model (QSM + qBOLD). ANN results were used for statistical analysis and as features for training a support-vector machine algorithm for binary classification of tumor type. Classification performance was determined with receiver operating characteristic (ROC) analysis. Results: We demonstrated that OEF in CET was significantly lower (p = 0.03) in glioblastomas than metastases, all features (OEF, CBF and CMRO2) were significantly higher (p = 0.01) in CET than NET2 for metastasis patients only, and the ratios of CET/NET2 for CBF (p = 0.04) and CMRO2 (p = 0.01) were significantly higher for metastasis patients than for glioblastoma patients. For glioblastoma patients, OEF was shown to be significantly lower (p = 0.02) in CET than in cNAB. In ROC analysis, the ratios of CMRO2 and CBF in CET divided by NET2 were found to be the best single characteristics for classification with areas under the curve of 0.85 and 0.80, respectively. The best multiparametric classification model was found when training the classifier on two features: OEF in CET and the CMRO2 ratio of CET/NET2. The resulting model had an area under the ROC curve of 0.94 with 93% classification accuracy. Conclusion: The differences in oxygenation and perfusion between glioblastomas and brain metastases support the research hypothesis and allow for robust, non-invasive differential diagnosis of the tumor entity. While classification performance was found to be in line with previous MR-based publications that mainly investigated perfusion metrics such as cerebral blood flow and volume, the voxelwise estimation of CMRO2 presents a major advantage in that it may also yield an insight into the likely response to radiation, antiangiogenic and chemotherapy, especially in glioblastoma. This makes the methods employed in this study promising candidates for implementation into the clinical routine, not least to complement anatomical MRI sequences without the need for additional application of contrast agent. In the long run, they have the potential to add to or even replace brain biopsies due to the good classification accuracy and the absence of typical complications of an invasive procedure. However, further research with larger patient populations is required before the QSM + qBOLD model can find its way into clinical decision making

Multiparametric Imaging and MR Image Texture Analysis in Brain Tumors

Discrimination of tumor from radiation injured (RI) tissues and differentiation of tumor types using noninvasive imaging is essential for guiding surgical and radiotherapy treatments are some of the challenges that clinicians face in the course of treatment of brain tumors. The first objective in this thesis was to develop a method to discriminate between glioblastoma tumor recurrences and radiation injury using multiparametric characterization of the tissue incorporating conventional magnetic resonance imaging signal intensities and diffusion tensor imaging parameters. Our results show significant correlations in the RI that was missing in the tumor regions. These correlations may aid in differentiating between tumor recurrence and RI. The second objective of was to investigate whether texture based image analysis of routine MR images would provide quantitative information that could be used to differentiate between glioblastoma and metastasis. Our results demonstrate that first-order texture feature of standard deviation and second-order texture features of entropy, inertia, homogeneity, and energy show significant differences between the two groups. The third objective was to investigate whether quantitative measurements of tumor size and appearance on MRI scans acquired prior to helical tomotherapy (HT) type whole brain radiotherapy with simultaneous infield boost treatment could be used to differentiate responder and non-responder patient groups. Our results demonstrated that smaller size lesions may respond better to this type of radiation therapy. Measures of appearance provided limited added value over measures of size for response prediction. Quantitative measurements of rim enhancement and core necrosis performed separately did not provide additional predictive value

Advanced Neuroimaging in Brain Tumors : Diffusion, Spectroscopy, Perfusion and Permeability MR imaging for the evaluation of tumor characterization and surgical treatment planning.

Advanced Neuroimaging in Brain Tumors: Diffusion, Spectroscopy, Perfusion and Permeability for the evaluation of tumor characterization and surgical treatment planning. The current standard of neuroimaging for brain tumor evaluation is anatomy-based MRI. Unfortunately, MRI does not fully reflect the complicated biology of infiltrative glioma, and has a limited capacity to differentiate a high-grade glioma (HGG) from a single brain metastasis. Grading of gliomas is important for the determination of appropriate treatment strategies and in the assessment of prognosis. It is clinically important to distinguish HGG from a single brain metastasis, because medical staging, surgical planning, and therapeutic decisions are different for each tumor type. The basis for this thesis was 208 patients admitted at Oslo University Hospital-Ullevål with the diagnosis of brain tumor between 2006 and 2010. The aim of this thesis was to evaluate in terms of diagnostic examination performance in the clinical decision-making process the use of advanced MRI techniques, namely, diffusion-weighted imaging (DWI), magnetic resonance spectroscopic imaging (MRSI), and T2*-weighted first pass dynamic susceptibility contrast-enhanced perfusion MRI (DSC MRI) in the diagnosis and preoperative planning of brain tumors, with focus in the grading and characterization of gliomas, as well as in the assessment of the peri-enhancing region aiming to demonstrate tumor-infiltration and tumor-free edema. In this thesis, we have demonstrated that MRSI and DSC MRI can be helpful to discriminate HGG from solitary metastases, supporting the hypothesis that these advanced MRI techniques can detect infiltration of tumor cells in the peri-enhancing region. We have demonstrated that combining DWI and MRSI increases the accuracy in the determination of glioma grade. We identified differences among all glial tumor grades for the parameters cerebral blood volume (rCBV) and microvascular leakage (MVL) derived from DSC MRI. Our correlation analysis indicate that MVL, rCBV, and cerebral blood flow (rCBF) may be related to different aspects of tumor angiogeneseis

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice

Innovations in Metastatic Brain Tumor Treatment

Metastatic brain tumors (MBTs) are the most common intracranial tumor and occur in up to 40% of patients with certain cancer diagnoses. The most common and frequent primary locations are cancers originating from the lung, breast, kidney, gastrointestinal tract or skin, and also may arising from any part of the body. Treatment for brain metastasis management includes surgery, whole brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), and chemotherapy. Standard treatment for MBTs includes surgery and SRS which offer the best outcomes, while the WBRT is still an important treatment option for patients who cannot tolerate surgery and SRS or patients with multiple brain metastases. Newer approaches such as immunotherapy and molecularly targeted therapy (e.g., small molecules and monoclonal antibodies) are currently being evaluated for the treatment of MBTs. In this chapter, we will review current available treatments for MBTs and discuss treatments that are undergoing active investigation

Quantitative MR Image Analysis - a Useful Tool in Differentiating Glioblastoma from Solitary Brain Metastasis

Cilj: Prikaz glioblastoma i metastaza na konvencionalnom MRI je često jako sličan, ali se terapijski pristup i prognoza bitno razlikuju. Čak i primenom naprednih MR tehnika, u nekim slučajevima dijagnoza ostaje nejasna. Glavni cilj disertacije bio je da utvrdi da li fraktalna ili teksturna, ili obe kvantitativne analize MR slike mogu doprineti diferencijaciji glioblastoma od solitarne metastaze mozga. Metod: Studija je sprovedena na ukupno 96 pacijenata sa dokazanim dijagnozama glioblastoma (50 pacijenata), odnosno solitarne metastaze (46 pacijenata). Izdvojene su slike sa najinformativnijim prikazom lezije (jedan isti presek u tri različite sekvence: CET1, T2 i SWI), a zatim je učinjena njihova kompjuterska analiza, primenom fraktalne metode brojanja kvadrata i teksturne metode bazirane na matrici zajedničke pojave istog nivoa sive boje (GLCM). Rezultati: Analizom sive skale celog tumora i binarne slike unutrašnjosti tumora sa T2 sekvence dobijen je najveći broj parametara koji značajno razlikuju dve vrste tumora (drugi ugaoni moment SASM, inverzni moment razlike SIDM, kontrast SCON, korelacija SCOR, diferencijalna fraktalna dimenzija DDIFF, odnosno binarna fraktalna dimenzija unutrašnjosti DBIN2, normirana fraktalna dimenzija DNORM, lakunarnost Ʌ2), dok su se druge dve sekvence (CET1 i SWI) pokazale manje pogodnim za kvantifikaciju. Kombinacijom parametara povećala se tačnost testiranja (AUC 0,838±0,041, senzitivnost 78% i specifičnost 76% za kombinaciju SASM i SIDM sa CET1 i T2 + SASM sa SWI + DBIN2 i DNORM sa T2). Zaključak: Kvantifikacija MR slike može doprineti diferencijalno dijagnostičkoj odluci između glioblastoma i solitarne metastaze mozga i potencijalno može postati deo svakodnevne radiološke prakse.Purpose: Presentation of glioblastomas and metastases on conventional MRI is quite similar, however treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of dissertation was to determine whether fractal, texture, or both quantitative MR image analysis could aid in differentiating glioblastoma from solitary brain metastasis. Method: Study embraced 96 patients with proven diagnosis of glioblastoma (50 patients), respectively solitary metastasis (46 patients). Images with the most representative lesion (one same slice on three different sequences: CET1, T2 and SWI) were selected, and computer analysis was done by fractal box-counting and texture gray level co-occurrence matrix (GLCM) methods. Results: Gray scale analysis of whole tumor and binary image analysis of tumor´s inner structures, both derived on T2 sequence, obtained the most significantly different parameters between two types of tumors (angular second moment SASM, inverse difference moment SIDM, contrast SCON, correlation SCOR, differential box dimension DDIFF, respectively binary box dimension DBIN2, normalized box dimension DNORM, lacunarity Ʌ2), while the other two sequences (CET1 and SWI) showed less suitable for quantification. The combinations of parameters yielded better results (AUC-0.838±0.041, sensitivity 78% and specificity 76% for next combination SASM and SIDM from CET1 and T2 + SASM from SWI + DBIN2 and DNORM from T2). Conclusions: MR image quantification may aid in differentiation between glioblastoma and solitary brain metastasis, and potentially could become a part of daily radiology practice

Proton Magnetic Resonance Spectroscopy of Intracranial Lesions: Assessment of differences between Tumours and Tumour like Lesions and Its applicability in Brain Lesion Characterization

Intracranial lesions are a significant health problem and present several imaging challenges. The role of imaging is no longer limited to merely providing anatomic details of the exact location of the pathology. Advanced Magnetic Resonance Imaging (MRI) techniques allow insight into the chemical makeup of certain compounds within these pathologic lesions. In vivo Proton MR Spectroscopy (1H MRS), a non-invasive technique which provides metabolic information can complement the anatomical changes found in radiological examinations of various intracranial lesions increasing diagnostic specificity. The present investigation was carried out a) To determine if there are specific MRS findings which could help differentiate between neoplastic and non –neoplastic lesions and also to identify the presence or absence of MRS features which could help differentiate one neoplasm from another. b)to determine if MRS findings could help grade gliomas pre operatively. c) To determine if MRS findings could help differentiate irradiated residual/recurrent tumours from radiation necrosis. d) To determine if MRS findings could help differentiate necrotic tumours from infective lesions such as abscesses. CONCLUSION: MR Spectroscopy in addition to appropriate conventional MRI sequences provides useful supplementary information and has a potential to validate treatment strategies. This could influence decision making with respect to prognosis and therapy in patients with intracranial lesions. Current Neuro - Imaging techniques enable use of multiple modalities to enhance the accuracy of non-invasive diagnosis. This study emphasizes the utility and validity of a simple add on technique of MRS over MRI to provide additional information in establishing the possible aetiological diagnosis of intracranial lesions. This study helps radiologists to improve diagnostic accuracy of MRI using the additional modality of MRS. MRS could help in clinical decision making in difficult cases e.g.in distinguishing neoplastic from non-neoplastic lesions. During the last few years there has been an exponential growth in MRS. The phenomenal advances in neuroimaging of the brain are resulting in a paradigm change in the clinician’s approach to diagnosing and managing intracranial conditions. Advances in the ultra precise delineation of anatomical changes in the brain have resulted in increasing precision in separating normal from abnormal brain tissue. However this has not kept pace with identifying the nature of the abnormal brain tissue. For several decades humankind has preferred non-invasive methods in establishing a definite pathological diagnosis. This is particularly true for cerebral lesions. This study appears to indicate, that one is now justified, in carrying out larger studies to confirm the present findings that MRS could be a valuable diagnostic tool in identifying certain types of cerebral pathology. CLINICAL APPLICATIONS & SUGGESTIONS: This study has enabled development of a few guidelines in the use of MR spectroscopy, as an add-on modality, to improve diagnostic accuracy, in certain categories of lesions i.e. neoplasm versus nonneoplasm, high grade tumours versus low grade tumours, high grade tumours versus metastases, recurrent tumours versus radiation necrosis, cystic non-tumoural lesions versus cystic tumours and improved follow up of polyphasic demyelinating conditions i.e. multiple sclerosis. 1. It is desirable that all patients with intra cranial space occupying lesions referred for MRI also undergo MRS, to provide additional information which may help differentiate a neoplasm from nonneoplasms. This could avoid surgical treatment in some instances. 2. MRS can be considered as a suitable, complementary modality to be used when a doubt exists in defining whether a tumour is low or high grade. 3. Solitary metastasis always is difficult to diagnose and may be confused with other high grade tumours. While it is challenging to use single voxel MRS technique to distinguish between the two, MV-MRS technique would be more useful to differentiate metastasis from high grade glioma. 4. In follow up of treated patients of brain tumour, especially after surgery, radiotherapy and/or chemotherapy, the differentiation between recurrent tumour and radiation necrosis can be difficult. Making this distinction is critical for treatment and MRS appears to be a useful tool to distinguish between the two. 5. In analyzing the use of MRS in tuberculous and pyogenic abscess, MRS was found to be useful in distinguishing tuberculous from pyogenic abscesses. 6. In polyphasic demyelinating conditions such as Multiple Sclerosis, MRS plays a valuable role not only in initial diagnosis but also in follow up. Degree of neuronal loss, a prognostic factor, can indirectly be titrated through MRS findings on follow up studies