Background
Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of ...patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI.
Purpose
To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post‐contrast T1‐weighted (T1W) MRI.
Study Type
Retrospective.
Subjects
Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others).
Field Strength/Sequence
Post‐contrast 3D T1W gradient echo images, acquired with 1.5 and 3.0 T MR systems.
Assessment
Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first‐ and second‐order statistical, morphological, wavelet features, and bag‐of‐features. Following dimension reduction, classification was performed using various machine‐learning algorithms including support‐vector machine (SVM), k‐nearest neighbor, decision trees, and ensemble classifiers.
Statistical Tests
For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.
Results
For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively.
Data Conclusion
Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts.
Level of Evidence: 1
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2019;50:519–528.
Background
High‐grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current ...radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging.
Purpose
To use radiomics patch‐based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components.
Study Type
Prospective.
Subjects
In all, 179 MRI scans were obtained from 102 patients: 67 patients with HGG and 35 patients with brain metastases. A subgroup of 15 patients with HGG were scanned before and following administration of bevacizumab.
Field Strength/Sequence
Pre and postcontrast agent T1‐weighted‐imaging (WI), T2WI, FLAIR, diffusion‐tensor‐imaging (DTI), and dynamic‐contrast‐enhanced (DCE)‐MRI at 3T.
Assessment
A total of 225 histograms and gray‐level‐co‐occurrence matrix‐based features were extracted from the nonenhancing lesion area. Tumoral volumes of interest (VOIs) were defined at the peritumoral area in patients with HGG; nontumoral VOIs were defined in patients with brain metastasis. Twenty machine‐learning algorithms including support‐vector‐machine (SVM), k‐nearest neighbor, decision‐trees, and ensemble classifiers were tested. The best classifier was trained on the entire labeled data, and was used to classify the entire data.
Statistical Tests
Dimensional reduction was performed on the 225 features using principal component analysis. Classification results were evaluated based on the sensitivity, specificity, and accuracy of each of the 20 classifiers, first based on a training and testing dataset (80% of the labeled data) in a 5‐fold manner, and next by applying the best classifier to the validation data (the remaining 20% of the labeled data). Results were additionally evaluated by assessing differences in dynamic‐contrast‐enhanced plasma‐volume (vp) and volume‐transfer‐constant (ktrans) values between the two components using Mann–Whitney U‐test/t‐test.
Results
The best classification into tumoral and nontumoral lesion components was obtained using a linear SVM classifier, with average accuracy of 87%, sensitivity 86%, and specificity of 89% (for the training and testing data). Significantly higher vp and ktrans values (P < 0.0001) were detected in the tumoral compared to the nontumoral component. Preliminary classification results in a subgroup of patients treated with bevacizumab demonstrated a reduction mainly in the nontumoral component following administration of bevacizumab, enabling early assessment of disease progression in some patients.
Data Conclusion
A radiomics patch‐based analysis enables classification of the nonenhancing lesion area in patients with HGG. Preliminary results were promising and the proposed method has the potential to assist in clinical decision‐making and to improve therapy response assessment in patients with HGG.
Level of Evidence: 1
Technical Efficacy Stage 4
J. Magn. Reson. Imaging 2018;48:729–736.
T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) ...contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNR.sub.WG ), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Significantly higher CNR.sub.WG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; p<0.0001). Significantly higher total brain GM, and lower cerebrospinal fluid volumes were obtained from MP2RAGE vs. MP-RAGE based on all segmentation methods (p<0.05 in all cases). Whole-brain voxel-wise comparisons revealed higher GM tissue probability in the thalamus, putamen, caudate, lingual gyrus, and precentral gyrus based on MP2RAGE compared with MP-RAGE. Moreover, significantly higher WM probability was observed in the cerebellum, corpus callosum, and frontal-and-temporal regions in MP2RAGE vs. MP-RAGE. Finally, MP2RAGE showed a higher mean percentage of change in total brain GM compared to MP-RAGE. On the other hand, MP-RAGE demonstrated a higher overtime percentage of change in WM and DGM volumes compared to MP2RAGE. Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Placental-related fetal growth restriction, resulting from placental dysfunction, impacts 3–5% of pregnancies and is linked to elevated risk of adverse neurodevelopmental outcomes. In ...response, the fetus employs a mechanism known as brain-sparing, redirecting blood flow to the cerebral circuit, for adequate supply to the brain. In this study we aimed to quantitatively evaluate disparities in gyrification and brain volumes among fetal growth restriction, small for gestational age and appropriate-for gestational-age fetuses. Additionally, we compared fetal growth restriction fetuses with and without brain-sparing. The study encompassed 106 fetuses: 35 fetal growth restriction (14 with and 21 without brain-sparing), 8 small for gestational age, and 63 appropriate for gestational age. Gyrification, supratentorial, and infratentorial brain volumes were automatically computed from T2-weighted magnetic resonance images, following semi-automatic brain segmentation. Fetal growth restriction fetuses exhibited significantly reduced gyrification and brain volumes compared to appropriate for gestational age (P < 0.001). Small for gestational age fetuses displayed significantly reduced gyrification (P = 0.038) and smaller supratentorial volume (P < 0.001) compared to appropriate for gestational age. Moreover, fetal growth restriction fetuses with BS demonstrated reduced gyrification compared to those without BS (P = 0.04), with no significant differences observed in brain volumes. These findings demonstrate that brain development is affected in fetuses with fetal growth restriction, more severely than in small for gestational age, and support the concept that vasodilatation of the fetal middle cerebral artery reflects more severe hypoxemia, affecting brain development.
The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for ...quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls.
We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria.
In control fetuses, all parameters changed significantly with gestational age (
< .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (
≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (
≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (
= 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (
= 33), with an area under the curve of 0.84 and a recall of 0.62.
This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.
Purpose
Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. ...The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts.
Methods
Preoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images,
T
2
-weighted images (WI) and post-contrast
T
1
WI
. Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis.
Results
Radiomic analysis differentiated tumors with 1p/19q intact (
n
=
21
; astrocytomas) from those with 1p/19q codeleted (
n
=
26
; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity
=
92%, specificity
=
83% and accuracy
=
87%, and with area under the curve
=
0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted (
46.2
±
30.0
vs.
30.8
±
16.8
cc, respectively;
p
=
0.03
) and predominantly located to the left insula (
p
=
0.04
).
Conclusion
The proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.
Fetal ventriculomegaly is one of the most frequently diagnosed abnormalities detected prenatally. The finding of additional subtle abnormalities can facilitate accurate prognoses, which may range ...from normal outcomes to significant neurodevelopmental sequelae. Pathogenesis and imaging patterns of ventriculomegaly and hydrocephalus in the fetus based on the pattern-recognition approach using fetal MRI are reviewed in this paper. This radiological approach may shed light on clinical course prediction and therapeutic efficacy of hydrocephalus in the fetus.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF ...inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.
Purpose: White-matter tract segmentation in patients with brain pathology can guide surgical planning and can be used for tissue integrity assessment. Recently, TractSeg was proposed for automatic ...tract segmentation in healthy subjects. The aim of this study was to assess the use of TractSeg for corticospinal-tract (CST) segmentation in a large cohort of patients with brain pathology and to evaluate its consistency in repeated measurements. Methods: A total of 649 diffusion-tensor-imaging scans were included, of them: 625 patients and 24 scans from 12 healthy controls (scanned twice for consistency assessment). Manual CST labeling was performed in all cases, and by 2 raters for the healthy subjects. Segmentation results were evaluated based on the Dice score. In order to evaluate consistency in repeated measurements, volume, Fractional Anisotropy (FA), and Mean Diffusivity (MD) values were extracted and correlated for the manual versus automatic methods. Results: For the automatic CST segmentation Dice scores of 0.63 and 0.64 for the training and testing datasets were obtained. Higher consistency between measurements was detected for the automatic segmentation, with between measurements correlations of volume = 0.92/0.65, MD = 0.94/0.75 for the automatic versus manual segmentation. Conclusions: The TractSeg method enables automatic CST segmentation in patients with brain pathology. Superior measurements consistency was detected for the automatic in comparison to manual fiber segmentation, which indicates an advantage when using this method for clinical and longitudinal studies.
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