BACKGROUND: Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The core of standard of care for newly diagnosed GBM was established in 2005 and includes maximum feasible surgical resection followed by radiation and temozolomide, with subsequent temozolomide with or without tumor-treating fields. Unfortunately, nearly all patients experience a recurrence. Bevacizumab (BV) is a commonly used second-line agent for such recurrences, but it has not been shown to impact overall survival, and short-term response is variable. METHODS: We collected MRI perfusion and diffusion images from 54 subjects with recurrent GBM treated only with radiation and temozolomide. They were subsequently treated with BV. Using machine learning, we created a model to predict short term response (6 months) and overall survival. We set time thresholds to maximize the separation of responders/survivors versus non-responders/short survivors. RESULTS: We were able to segregate 21 (68%) of 31 subjects into unlikely to respond categories based on Progression Free Survival at 6 months (PFS6) criteria. Twenty-two (69%) of 32 subjects could similarly be identified as unlikely to survive long using the machine learning algorithm. CONCLUSION: With the use of machine learning techniques to evaluate imaging features derived from pre- and post-treatment multimodal MRI, it is possible to identify an important fraction of patients who are either highly unlikely to respond, or highly likely to respond. This can be helpful is selecting patients that either should or should not be treated with BV.

• MeSH
• antitumorózní látky alkylující terapeutické užití   MeSH
• bevacizumab terapeutické užití   MeSH
• časové faktory MeSH
• difuzní magnetická rezonance MeSH
• dospělí MeSH
• glioblastom farmakoterapie   patologie   MeSH
• kombinovaná terapie MeSH
• lidé MeSH
• lokální recidiva nádoru farmakoterapie   MeSH
• nádory mozku farmakoterapie   patologie   MeSH
• objem krve v mozku účinky léků   fyziologie   MeSH
• prediktivní hodnota testů * MeSH
• přežití po terapii bez příznaků nemoci MeSH
• protinádorové látky imunologicky aktivní terapeutické užití   MeSH
• radioterapie MeSH
• retrospektivní studie MeSH
• strojové učení MeSH
• temozolomid terapeutické užití   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Early identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry-based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase. METHODS: One hundred thirty-nine (139) ncCT scans of hyperacute ischemic stroke with follow-up magnetic resonance diffusion-weighted (MR-DW) images were collected. The regions of stroke were identified in the MR-DW images, which were spatially aligned to corresponding ncCT images. A state-of-the-art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR-DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co-occurrence and run-length texture-based image features in the regions of ischemic stroke and their contralateral regions. RESULTS: The classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions. CONCLUSIONS: The results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.

• MeSH
• cévní mozková příhoda komplikace   diagnostické zobrazování   MeSH
• ischemie mozku komplikace   MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• počítačová rentgenová tomografie * MeSH
• počítačové zpracování obrazu metody   MeSH
• rozhodovací stromy MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O(6)-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. METHODS: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. RESULTS: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. CONCLUSIONS: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

• MeSH
• DNA modifikační methylasy genetika   MeSH
• enzymy opravy DNA genetika   MeSH
• glioblastom diagnostické zobrazování   genetika   chirurgie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• metylace DNA * MeSH
• mozek diagnostické zobrazování   chirurgie   MeSH
• nádorové biomarkery genetika   MeSH
• nádorové supresorové proteiny genetika   MeSH
• nádory mozku diagnostické zobrazování   genetika   chirurgie   MeSH
• promotorové oblasti (genetika) MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. METHODS: First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations. RESULTS AND DISCUSSION: For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results. CONCLUSIONS: We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.

• MeSH
• algoritmy MeSH
• gliom patologie   chirurgie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• nádory mozku patologie   chirurgie   MeSH
• počítačové zpracování obrazu * MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH