Radiomic Texture Feature Descriptor to Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal MRI

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature

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

Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging

Cardiovascular diseases constitute the leading global cause of morbidity and mortality. Magnetic resonance imaging (MRI) has become the gold standard technique for the assessment of patients with myocardial infarction. However, limitations still exist thus new alternatives are open to investigation. Texture analysis is a technique that aims to quantify the texture of the images that are not always perceptible by the human eye. It has been successfully applied in medical imaging but applications to cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply texture analysis in conventional CMR images for the assessment of patients with myocardial infarction, as an alternative to current methods. Three applications of texture analysis and machine learning techniques were studied: i) Detection of infarcted myocardium in late gadolinium enhancement (LGE) CMR. Segmentation of the infarcted myocardium is routinely performed using image intensity thresholds. The inclusion of texture features to aid the segmentation was analyzed obtaining overall good results. The method was developed using 10 LGE CMR datasets and tested on a separate dataset comprising 5 cases that were acquired with a completely different scanner than that used for training. Therefore, this preliminary study showed the transferability of texture analysis which is important for clinical applicability. ii) Differentiation of acute and chronic myocardial infarction using LGE CMR and standard pre-contrast cine CMR. In this study, two different feature selection techniques and six different machine learning classifiers were studied and compared. The best classification was achieved using a polynomial SVM obtaining an overall AUC of 0.87 ± 0.06 in LGE CMR. Interestingly, results on cine CMR in which infarctions are visually imperceptible in most cases were also good (AUC = 0.83 ± 0.08). iii) Detection of infarcted non-viable segments in cine CMR. This study was motivated by the findings of the previous one. It demonstrated that texture analysis can be used to distinguish non-viable, viable and remote segments using standard pre-contrast cine CMR solely. This was the most relevant contribution of this thesis as it can be used as hypothesis for future work aiming to accurately delineate the infarcted myocardium as a gadolinium-free alternative that will have potential advantages. The three proposed applications were successfully performed obtaining promising results. In conclusion, texture analysis can be successfully applied to conventional CMR images and provides a potential quantitative alternative to existing methods