Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

OBJECTIVES: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. METHODS: In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient. RESULTS: In the 259 eligible men (median 64 [IQR 61-72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis. CONCLUSIONS: U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance. KEY POINTS: • U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial.

Background Relevance of antiangiogenic treatment with bevacizumab in patients with glioblastoma is controversial because progression-free survival benefit did not translate into an overall survival (OS) benefit in randomized phase III trials. Purpose To perform longitudinal characterization of intratumoral angiogenesis and oxygenation by using dynamic susceptibility contrast agent-enhanced (DSC) MRI and evaluate its potential for predicting outcome from administration of bevacizumab. Materials and Methods In this secondary analysis of the prospective randomized phase II/III European Organization for Research and Treatment of Cancer 26101 trial conducted between October 2011 and December 2015 in 596 patients with first recurrence of glioblastoma, the subset of patients with availability of anatomic MRI and DSC MRI at baseline and first follow-up was analyzed. Patients were allocated into those administered bevacizumab (hereafter, the BEV group; either bevacizumab monotherapy or bevacizumab with lomustine) and those not administered bevacizumab (hereafter, the non-BEV group with lomustine monotherapy). Contrast-enhanced tumor volume, noncontrast-enhanced T2 fluid-attenuated inversion recovery (FLAIR) signal abnormality volume, Gaussian-normalized relative cerebral blood volume (nrCBV), Gaussian-normalized relative blood flow (nrCBF), and tumor metabolic rate of oxygen (nTMRO2) was quantified. The predictive ability of these imaging parameters was assessed with multivariable Cox regression and formal interaction testing. Results A total of 254 of 596 patients were evaluated (mean age, 57 years ± 11; 155 men; 161 in the BEV group and 93 in non-BEV group). Progression-free survival was longer in the BEV group (3.7 months; 95% confidence interval [CI]: 3.0, 4.2) compared with the non-BEV group (2.5 months; 95% CI: 1.5, 2.9; P = .01), whereas OS was not different (P = .15). The nrCBV decreased for the BEV group (-16.3%; interquartile range [IQR], -39.5% to 12.0%; P = .01), but not for the non-BEV group (1.2%; IQR, -17.9% to 23.3%; P = .19) between baseline and first follow-up. An identical pattern was observed for both nrCBF and nTMRO2 values. Contrast-enhanced tumor and noncontrast-enhanced T2 FLAIR signal abnormality volumes decreased for the BEV group (-66% [IQR, -83% to -35%] and -33% [IQR, -71% to -5%], respectively; P < .001 for both), whereas they increased for the non-BEV group (30% [IQR, -17% to 98%], P = .001; and 10% [IQR, -13% to 82%], P = .02, respectively) between baseline and first follow-up. None of the assessed MRI parameters were predictive for OS in the BEV group. Conclusion Bevacizumab treatment decreased tumor volumes, angiogenesis, and oxygenation, thereby reflecting its effectiveness for extending progression-free survival; however, these parameters were not predictive of overall survival (OS), which highlighted the challenges of identifying patients that derive an OS benefit from bevacizumab. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Dillon in this issue.

Impact of slice thickness on clinical utility of automated Alberta Stroke Program Early Computed Tomography Scores.

OBJECTIVES: The clinical utility of electronically derived ASPECTS (e-ASPECTS) to quantify signs of acute ischemic infarction could be demonstrated in multiple studies. Here, we aim to clinically validate the impact of CT slice thickness (ST) on the performance of e-ASPECTS software. METHODS: A consecutive series of n = 258 patients (06/2016 and 01/2019) with middle cerebral artery occlusion and subsequent treatment with mechanical thrombectomy was analyzed. The e-ASPECTS score and acute infarct volumes were calculated from baseline non-contrast CT with a software using 1-mm slice thickness (ST) (defined as ground truth) and axial reconstructions with 2-10-mm ST and correlated with baseline stroke severity (NIHSS) as well as clinical outcome (mRS) using logistic regressions. RESULTS: In comparison with the ground truth, significant differences were seen in e-ASPECTS scores with ST > 6 mm (p ≤ 0.031) and infarct volumes with ST > 4 mm (p ≤ 0.001). There was a significant correlation of lower e-ASPECTS and higher acute infarct volumes with increasing baseline NIHSS values for all ST (p ≤ 0.001, respectively), with values derived from 1 mm yielding the highest correlation for both parameters (rho, - 0.38 and 0.31, respectively). Similarly, lower e-ASPECTS and higher acute infarct volumes from all ST were significantly associated with poor outcome after 90 days (p ≤ 0.05, respectively) with values derived from 1-mm ST yielding the highest effects for both parameters (OR, 0.69 [95% CI 0.50-0.88] and 1.27 [95% CI 1.10-1.50], respectively). CONCLUSIONS: The e-ASPECTS software generates robust values for e-ASPECTS and acute infarct volumes when using ST ≤ 4 mm with ST = 1 mm yielding the best performance for predicting baseline stroke severity and clinical outcome after 90 days. KEY POINTS: • Clinical utility of automatically derived ASPECTS from computed tomography scans was shown in patients with acute ischemic stroke and treatment with mechanical thrombectomy. • Thin slices (= 1 mm) had the highest clinical utility in comparison with thicker slices (2-10 mm) by having the strongest correlation with baseline stroke severity and independent effects on clinical outcome after 90 days. • Automatically calculated acute infarct volumes possess clinical utility beyond ASPECTS and should be considered in future studies.

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. METHODS: A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. RESULTS: The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. CONCLUSIONS: Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. KEY POINTS: • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING: Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

Automated brain extraction of multisequence MRI using artificial neural networks.

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80% of the data) through cross validation and subsequently externally validated on the test set (20% of the data). U-Net-derived sPC probability maps were calibrated by matching sextant-based cross-validation performance to clinical performance of Prostate Imaging Reporting and Data System (PI-RADS). Performance of PI-RADS and U-Net were compared by using sensitivities, specificities, predictive values, and Dice coefficient. Results A total of 312 men (median age, 64 years; interquartile range [IQR], 58-71 years) were evaluated. The training set consisted of 250 men (median age, 64 years; IQR, 58-71 years) and the test set of 62 men (median age, 64 years; IQR, 60-69 years). In the test set, PI-RADS cutoffs greater than or equal to 3 versus cutoffs greater than or equal to 4 on a per-patient basis had sensitivity of 96% (25 of 26) versus 88% (23 of 26) at specificity of 22% (eight of 36) versus 50% (18 of 36). U-Net at probability thresholds of greater than or equal to 0.22 versus greater than or equal to 0.33 had sensitivity of 96% (25 of 26) versus 92% (24 of 26) (both P > .99) with specificity of 31% (11 of 36) versus 47% (17 of 36) (both P > .99), not statistically different from PI-RADS. Dice coefficients were 0.89 for prostate and 0.35 for MRI lesion segmentation. In the test set, coincidence of PI-RADS greater than or equal to 4 with U-Net lesions improved the positive predictive value from 48% (28 of 58) to 67% (24 of 36) for U-Net probability thresholds greater than or equal to 0.33 (P = .01), while the negative predictive value remained unchanged (83% [25 of 30] vs 83% [43 of 52]; P > .99). Conclusion U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Padhani and Turkbey in this issue.

Risk factors of intracranial hemorrhage after mechanical thrombectomy of anterior circulation ischemic stroke.

PURPOSE: Intracranial hemorrhage (ICH) is a potentially severe complication after mechanical thrombectomy (MT). Here, we investigated risk factors for the occurrence of any and symptomatic ICH after MT due to large-vessel occlusion of the anterior circulation. METHODS: Consecutive patients with acute ischemic anterior circulation stroke with large-vessel occlusion undergoing MT were analyzed. ICH was categorized according to the Heidelberg Bleeding Classification. Forty-three procedural and clinical parameters were analyzed using univariate tests and multivariate logistic regressions. RESULTS: Of 612 patients, any ICH was detected in 195 (31.9%), while 27 (4.4%) developed a symptomatic ICH. Infarct size > 1/3 of vascular territory in control imaging (OR 2.18, 95% CI 1.45-3.21), higher serum glucose levels (OR 1.23 for change of 15 units mg/dL, 95% CI 1.10-1.39), and higher thrombectomy maneuver count (OR 1.21, 95% CI 1.11-1.32) were significantly associated with a higher risk of developing any ICH compared to no ICH. Wake-up strokes (OR 3.99, 95% CI 1.38-11.60), transfer from an external clinic (OR 3.04, 95% CI 1.24-7.48), and higher serum glucose levels (OR 1.22 for change of 15 units mg/dL, 95% CI 1.05-1.42) were revealed as independent risk factors for development of symptomatic ICH compared to no symptomatic ICH. Patients with no infarct demarcation (OR 0.10, 95% CI 0.01-0.80) and complete recanalization (OR 0.57, 95% CI 0.37-0.86) showed a lower risk of developing any ICH. CONCLUSION: Wake-up strokes and patients who are treated within a drip-and-ship concept are especially vulnerable for symptomatic ICH, while complete recanalization, contrary to subtotal recanalization only, was revealed as a protective factor against ICH.

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88% (53 of 60) and specificity of 50% (79 of 159). Quantitative measurement of the mean ADC (cut-off 732 mm2/sec) significantly reduced false-positive (FP) lesions from 80 to 60 (specificity 62% [99 of 159]) and false-negative (FN) lesions from seven to six (sensitivity 90% [54 of 60]) (P = .048). Radiologist interpretation had a per-patient sensitivity of 89% (40 of 45) and specificity of 43% (38 of 88). Quantitative measurement of the mean ADC reduced the number of patients with FP lesions from 50 to 43 (specificity 51% [45 of 88]) and the number of patients with FN lesions from five to three (sensitivity 93% [42 of 45]) (P = .496). Comparison of the area under the ROC curve (AUC) for the mean ADC (AUCglobal = 0.84; AUCzone-specific ≤ 0.87) vs the RML (AUCglobal = 0.88, P = .176; AUCzone-specific ≤ 0.89, P ≥ .493) showed no significantly different performance. Conclusion Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment. © RSNA, 2018 Online supplemental material is available for this article.

Radiomics, Metabolic, and Molecular MRI for Brain Tumors.

Magnetic resonance imaging plays a key role in diagnosis and treatment monitoring of brain tumors. Novel imaging techniques that specifically interrogate aspects of underlying tumor biology and biochemical pathways have great potential in neuro-oncology. This review focuses on the emerging role of 2-hydroxyglutarate-targeted magnetic resonance spectroscopy, as well as radiomics and radiogenomics in establishing diagnosis for isocitrate dehydrogenase mutant gliomas, and for monitoring treatment response and predicting prognosis of this group of brain tumor patients.

No Signal Intensity Increase in the Dentate Nucleus on Unenhanced T1-weighted MR Images after More than 20 Serial Injections of Macrocyclic Gadolinium-based Contrast Agents.

Purpose To determine the effect of more than 20 serial injections of macrocyclic gadolinium-based contrast agents (GBCAs) on the signal intensity (SI) of the dentate nucleus (DN) on unenhanced T1-weighted magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 33 patients who underwent at least 20 consecutive MR imaging examinations (plus an additional MR imaging for reference) with the exclusive use of macrocyclic GBCAs gadoterate meglumine and gadobutrol were analyzed. SI ratio differences were calculated for DN-to-pons and DN-to-middle cerebellar peduncle (MCP) ratios by subtracting the SI ratio at the first MR imaging examination from the SI ratio at the last MR imaging examination. One-sample t tests were used to examine if the SI ratio differences differed from 0, and Bayes factors were calculated to quantify the strength of evidence for each test. Results Patients underwent a mean of 23.03 ± (standard deviation) 4.20 GBCA administrations (mean accumulated dose, 491.21 mL ± 87.04 of a 0.5 M GBCA solution) with an average of 12.09 weeks ± 2.16 between every administration. Both ratio differences did not differ significantly from 0 (DN-to-pons ratio: -0.0032 ± 0.0154, P = .248; DN-to-MCP ratio: -0.0011 ± 0.0093, P = .521), and one-sided Bayes factors provided substantial to strong evidence against an SI ratio increase (Bayes factor for DN-to-pons ratio = 0.09 and that for DN-to-MCP ratio = 0.12). Conclusion The study indicates that 20 or more serial injections of macrocyclic GBCAs administered with on average 3 months between each injection are not associated with an SI increase in the DN. © RSNA, 2016.

Pediatric Brain: No Increased Signal Intensity in the Dentate Nucleus on Unenhanced T1-weighted MR Images after Consecutive Exposure to a Macrocyclic Gadolinium-based Contrast Agent.

Purpose To determine the effect of at least five serial injections of the macrocyclic gadolinium-based contrast agent (GBCA) gadoterate meglumine on the signal intensity (SI) of the dentate nucleus (DN) of the pediatric brain on nonenhanced T1-weighted magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 41 pediatric patients (age range, 3-17 years) who were imaged in at least five consecutive 1.5-T MR examinations with the exclusive use of gadoterate meglumine (plus a final additional nonenhanced MR imaging examination) were evaluated. SI ratio differences between the first and last MR examination were calculated for DN-to-pons and DN-to-middle cerebellar peduncle (MCP) ratios in a region-of-interest-based analysis, and one-sample t tests were used to examine if the SI ratio differences differed from 0. Bayes factors were calculated to quantify the strength of evidence for each test. Results Patients underwent a mean of 8.6 ± 3.9 GBCA administrations (mean accumulated dose, 32.07 mmol ± 17.62, with an average of 16.7 weeks ± 7.9 between every administration). Both ratio differences did not differ significantly from 0 (DN-to-pons ratio: -0.0012 ± 0.0101, P = .436; DN-to-MCP ratio: 0.0007 ± 0.0088, P = .604), and one-sided Bayes factors provided substantial evidence against an SI ratio increase (0.10 for DN-to-pons ratio; 0.27 for DN-to-MCP ratio). Conclusion No increase of the SI in the DN was found after a mean of 8.6 serial injections of the macrocyclic GBCA gadoterate meglumine in pediatric patients, confirming previous studies that did not find this effect after serial injections of macrocyclic GBCAs in adults. © RSNA, 2017.

Downfield-NOE-suppressed amide-CEST-MRI at 7 Tesla provides a unique contrast in human glioblastoma.

PURPOSE: The chemical exchange saturation transfer (CEST) effect observed in brain tissue in vivo at the frequency offset 3.5 ppm downfield of water was assigned to amide protons of the protein backbone. Obeying a base-catalyzed exchange process such an amide-CEST effect would correlate with intracellular pH and protein concentration, correlations that are highly interesting for cancer diagnosis. However, recent experiments suggested that, besides the known aliphatic relayed-nuclear Overhauser effect (rNOE) upfield of water, an additional downfield rNOE is apparent in vivo resonating as well around +3.5 ppm. In this study, we present further evidence for the underlying downfield-rNOE signal, and we propose a first method that suppresses the downfield-rNOE contribution to the amide-CEST contrast. Thus, an isolated amide-CEST effect depending mainly on amide proton concentration and pH is generated. METHODS: The isolation of the exchange mediated amide proton effect was investigated in protein model-solutions and tissue lysates and successfully applied to in vivo CEST images of 11 glioblastoma patients. RESULTS: Comparison with gadolinium contrast enhancing longitudinal relaxation time-weighted images revealed that the downfield-rNOE-suppressed amide-CEST contrast forms a unique contrast that delineates tumor regions and show remarkable overlap with the gadolinium contrast enhancement. CONCLUSION: Thus, suppression of the downfield rNOE contribution might be the important step to yield the amide proton CEST contrast originally aimed at. Magn Reson Med 77:196-208, 2017. © 2016 Wiley Periodicals, Inc.

Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.

PURPOSE: To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS: From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC. RESULTS: The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI. CONCLUSION: In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.

Feasibility, Risk Profile and Diagnostic Yield of Stereotactic Biopsy in Children and Young Adults with Brain Lesions.

Objective To evaluate the feasibility, safety, and diagnostic yield of stereotactic biopsy (SB) in children and adolescents with cerebral lesions. Methods We performed a systematic review of the literature and a retrospective analysis of all pediatric and adolescent patients who underwent SB for unclear brain lesions at our center. We collected patient and lesion-associated parameters, analysed the rate of procedural complications and diagnostic yield. Results Our institutional series consisted of 285 SBs in 269 children and young adults between 1989 and 2016 (median age, 9 (range 1-18) years). There was no procedure-related mortality. Permanent and transient morbidity was 0.7% and 5.8%, respectively. Lesions were located in brain lobes (26.3%) and in midline structures (73.7%). The diagnostic yield was 97.5% and histology consisted low-grade gliomas (44.2%), high-grade gliomas (15.1%), non-glial tumors (22.8%), and non-neoplastic disease (15.4%). Morbidity was not associated with tumor location, age, histology or intraoperative position of the patient. In order to compare our findings with previous reports, we reviewed 25 studies with 1 109 children and young adults which had underwent SB. The diagnostic yield ranged between 83% and 100%. The reported morbidity and mortality rates range from 0-27% and 0-3.3%, respectively. Conclusions SB in this particular patient population is a safe and a high-yield diagnostic procedure and indicates therefore its importance in the light of personalized medicine with the development of individual molecular treatment strategies.

MR Perfusion-derived Hemodynamic Parametric Response Mapping of Bevacizumab Efficacy in Recurrent Glioblastoma.

PURPOSE: To better understand the effect of bevacizumab therapy on tumor blood flow and oxygenation status in patients with recurrent glioblastoma. MATERIALS AND METHODS: Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number, S-320/2012), and informed consent was waived. A total of 71 patients who received a diagnosis of recurrent glioblastoma underwent conventional anatomic magnetic resonance (MR) imaging and dynamic susceptibility contrast material-enhanced MR imaging at baseline and at the first follow-up examination after initiation of bevacizumab therapy. Parametric response maps (PRMs) were created with multistep (nonlinear) registration of patients' post- to pretreatment images and voxel-wise subtraction between Gaussian-normalized relative cerebral blood volume (nrCBV) and Gaussian-normalized relative cerebral blood flow (nrCBF) maps. Intratumor voxels were stratified as being increased (PRM[+]) or decreased (PRM[-]) if they exceeded a threshold that represented the 95% confidence interval in the normal-appearing brain. Correlation with progression-free and overall survival was performed with Cox proportional hazards models. RESULTS: The risks for disease progression and death significantly increased with (a) higher baseline nrCBV (hazard ratio [HR] = 1.86, P < .01; HR = 1.52, P < .01) and nrCBF (HR = 1.78, P < .01; HR = 1.86, P < .01) values and (b) higher PRM(-) of nrCBV (HR = 1.03, P = .01; HR = 1.02, P = .03) and nrCBF (HR = 1.04, P < .01; HR = 1.03, P < .01), but not with higher PRM(+) of nrCBV and nrCBF, and not for the relative change in mean nrCBV and nrCBF, confirming the superiority of the PRM approach. The magnitude of PRM(-) for both nrCBV and nrCBF significantly increased for higher baseline values (P < .01). CONCLUSION: Pretreatment hemodynamic parameters are the principal determinant of response to bevacizumab therapy in patients with recurrent glioblastoma. Although the magnitude of PRM(-) is a function of the corresponding pretreatment parameter, the finding of higher PRM(-) and a lack of change in PRM(+) in nonresponders to bevacizumab therapy implies that tumors with a high degree of angiogenesis before bevacizumab therapy retain a higher level of angiogenesis during therapy, despite a greater antiangiogenic effect of bevacizumab, such that a reversal of the biologic behavior and relative prognosis of these tumors does not occur.

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.

Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.

Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.

Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. © RSNA, 2016 Online supplemental material is available for this article.

Prognostic value of combined visualization of MR diffusion and perfusion maps in glioblastoma.

We analyzed whether the combined visualization of decreased apparent diffusion coefficient (ADC) values and increased cerebral blood volume (CBV) in perfusion imaging can identify prognosis-related growth patterns in patients with newly diagnosed glioblastoma. Sixty-five consecutive patients were examined with diffusion and dynamic susceptibility-weighted contrast-enhanced perfusion weighted MRI. ADC and CBV maps were co-registered on the T1-w image and a region of interest (ROI) was manually delineated encompassing the enhancing lesion. Within this ROI pixels with ADC values the 70th percentile (CBVmax) and the intersection of pixels with ADCmin and CBVmax were automatically calculated and visualized. Initially, all tumors with a mean intersection greater than the upper quartile of the normally distributed mean intersection of all patients were subsumed to the first growth pattern termed big intersection (BI). Subsequently, the remaining tumors' growth patterns were categorized depending on the qualitative representation of ADCmin, CBVmax and their intersection. Log-rank test exposed a significantly longer overall survival of BI (n = 16) compared to non-BI group (n = 49) (p = 0.0057). Thirty-one, four and 14 patients of the non-BI group were classified as predominant ADC-, CBV- and mixed growth group, respectively. In a multivariate Cox regression model, the BI-, CBV- and mixed groups had significantly lower adjusted hazard ratios (p-value, α(Bonferroni) < 0.006) when compared to the reference group ADC: 0.29 (0.0027), 0.11 (0.038) and 0.33 (0.0059). Our study provides evidence that the combination of diffusion and perfusion imaging allows visualization of different glioblastoma growth patterns that are associated with prognosis. A possible biological hypothesis for this finding could be the interpretation of the ADCmin fraction as the invasion-front of tumor cells while the CBVmax fraction might represent the vascular rich tumor border that is "trailing behind" the invasion-front in the ADC group.

Factors triggering an additional resection and determining residual tumor volume on intraoperative MRI: analysis from a prospective single-center registry of supratentorial gliomas.

OBJECTIVE: In this analysis, the authors sought to identify variables triggering an additional resection (AR) and determining residual intraoperative tumor volume in 1.5-T intraoperative MRI (iMRI)-guided glioma resections. METHODS: A consecutive case series of 224 supratentorial glioma resections (WHO Grades I-IV) from a prospective iMRI registry (inclusion dates January 2011-April 2013) was examined with univariate and multiple regression models including volumetric data, tumor-related, and surgeon-related factors. The surgeon's expectation of an AR, in response to a questionnaire completed prior to iMRI, was evaluated using contingency analysis. A machine-learning prediction model was applied to consider if anticipation of intraoperative findings permits preoperative identification of ideal iMRI cases. RESULTS: An AR was performed in 70% of cases after iMRI, but did not translate into an accumulated risk for neurological morbidity after surgery (p = 0.77 for deficits in cases with AR vs no AR). New severe persistent deficits occurred in 6.7% of patients. Initial tumor volume determined frequency of ARs and was independently correlated with larger tumor remnants delineated on iMRI (p < 0.0001). Larger iMRI volume was further associated with eloquent location (p = 0.010) and recurrent tumors (p < 0.0001), and with WHO grade (p = 0.0113). Greater surgical experience had no significant influence on the course of surgery. The surgeon's capability of ruling out an AR prior to iMRI turned out to incorporate guesswork (negative predictive value 43.6%). In a prediction model, AR could only be anticipated with 65% accuracy after integration of confounding variables. CONCLUSIONS: Routine use of iMRI in glioma surgery is a safe and reliable method for resection guidance and is characterized by frequent ARs after scanning. Tumor-related factors were identified that influenced the course of surgery and intraoperative decision-making, and iMRI had a common value for surgeons of all experience levels. Commonly, the subjective intraoperative impression of the extent of resection had to be revised after iMRI review, which underscores the manifold potential of iMRI guidance. In combination with the failure to identify ideal iMRI cases preoperatively, this study supports a generous, tumor-oriented rather than surgeon-oriented indication for iMRI in glioma surgery.