Subtraction Maps Research Articles

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas

PurposeThis study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes.MethodsA total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses.ResultsT2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051).ConclusionT2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Interictal blood-brain barrier dysfunction in piriform cortex of people with epilepsy.

The piriform cortex is considered to be highly epileptogenic. Its resection during epilepsy surgery is a predictor for postoperative seizure freedom in temporal lobe epilepsy. Epilepsy is associated with a dysfunction of the blood-brain barrier. We investigated blood-brain barrier dysfunction in the piriform cortex of people with temporal lobe epilepsy using quantitative T1-relaxometry. Gadolinium-based contrast agent was administered ictally and interictally in 37 individuals before undergoing quantitative T1-relaxometry. Postictal and interictal images were co-registered, and subtraction maps were created as biomarkers for peri-ictal (∆qT1interictal-postictal) and interictal (∆qT1noncontrast-interictal) blood-brain barrier dysfunction. Values were extracted for the piriform cortex, hippocampus, amygdala, and the whole cortex. In temporal lobe epilepsy (n = 14), ∆qT1noncontrast-interictal was significantly higher in the piriform cortex than in the whole cortex (p = 0.02). In extratemporal lobe epilepsy (n = 23), ∆qT1noncontrast-interictal was higher in the hippocampus than in the whole cortex (p = 0.05). Across all individuals (n = 37), duration of epilepsy was correlated with ∆qT1noncontrast-interictal (ß = 0.001, p < 0.001) in all regions, while the association was strongest inthe piriform cortex. Impaired verbal memory was associated with ∆qT1noncontrast-interictal only in the piriform cortex (p = 0.04). ∆qT1interictal-postictal did not show differences in any region. Interictal blood-brain barrier dysfunction occurs in the piriform cortex in temporal lobe epilepsy. This dysfunction is linked to longer disease duration and worse cognitive deficits, emphasizing the central role of the piriform cortex in the epileptogenic network of temporal lobe epilepsy.

Quantification of T2-FLAIR Mismatch in Nonenhancing Diffuse Gliomas Using Digital Subtraction.

The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.

NIMG-62. QUANTITATIVE ASSESSMENT OF T2-FLAIR MISMATCH IN HUMAN NON-ENHANCING GLIOMAS USING SUBTRACTION MAPS OF T2-W AND FLAIR MRI

Abstract INTRODUCTION The T2-FLAIR mismatch (T2FM) sign on anatomical MRI is highly specific for isocitrate dehydrogenase (IDH)-mutant 1p/19q-intact gliomas (astrocytomas) and is characterized by relative hypointense FLAIR signal within the T2 hyperintense lesion. However, studies on T2FM sign have mostly utilized qualitative visual assessment. This study explored quantitative subtraction maps of T2-w and FLAIR MRI in human non-enhancing gliomas to quantify the extent of T2FM and assess T2FM subregion volumetric and diffusion MRI differences. METHODS Seventy-two newly-diagnosed, non-enhancing gliomas with known IDH- and 1p/19q-status from The Cancer Imaging Archive (TCIA) were studied. An institutional cohort (n= 36) was analyzed for validation. T2-FLAIR subtraction maps were generated from voxel-wise subtraction of co-registered, normalized T2-w and FLAIR MRI scans. Tumor volumes of interest (VOIs) were segmented on the T2 hyperintensity excluding macroscopic cysts. Tumor VOIs were split into mismatched and non-mismatched subregions using consistent T2-FLAIR subtraction map thresholds. Tumor subregion volumes and normalized apparent diffusion coefficient (nADC) values were obtained, and tumor T2FM percentage was calculated. RESULTS In the TCIA cohort, a threshold of &amp;gt; 16% T2FM had 92.3% specificity and 34.8% sensitivity and a threshold of &amp;gt; 42% T2FM had 100% specificity and 21.7% sensitivity for classifying IDH-mutant astrocytomas (AUC= 0.704, P= 0.004). IDH-mutant astrocytomas with &amp;gt; 16% T2FM had higher nADC in mismatched compared to non-mismatched subregions (mean difference= 0.51; P&amp;lt; 0.0001). Using the same T2FM thresholds, these findings were validated in the institutional cohort for glioma classification (AUC= 0.918, P&amp;lt; 0.0001; &amp;gt; 16% T2FM: 100% specificity, 66.7% sensitivity, PPV= 100%, NPV= 65.2%; &amp;gt; 42% T2FM: 100% specificity, 23.8% sensitivity, PPV= 100%, NPV= 48.3%) and subregion nADC differences (mean difference= 0.56, P&amp;lt; 0.0001). CONCLUSIONS T2-FLAIR subtraction maps may be a useful, automated tool for classifying non-enhancing gliomas and exploring T2FM subregion differences. Elevated nADC in mismatched subregions may reflect microcystic changes.

Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma.

Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy. N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival. Optimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth. Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

Depth of radiographic response (DpR) and time to tumor regrowth (TTG) to predict overall survival following anti-VEGF therapy in recurrent glioblastoma.

2063 Background: Anti-angiogenic therapies, anti-VEGF agents, cause high radiographic response rates due to reduction in vascular permeability. However, we theorized the degree of tumor shrinkage combined with the duration of response might be a more meaningful measure of tumor control and may better reflect real patient benefit. In the current study, we investigate the association between model-derived parameters describing enhancing tumor volumetric dynamics and overall survival (OS) in recurrent glioblastoma treated with anti-VEGF therapy from multiple clinical trials. Methods: N=134 patients treated with bevacizumab +/- irinotecan (BRAIN Trial; NCT00345163) and N=121 patients treated with cabozantinib (XL184-201; NCT00704288) were used to identify potential imaging biomarkers, and N=63 patients in the bevacizumab control arm in a phase III trial (GLOBE Trial; NCT02511405) were used as a validation dataset. Enhancing tumor volumes were estimated using T1 subtraction maps, and a biexponential model was used to model tumor response ( d) and regrowth ( g) rates, as well as time to tumor regrowth ( TTG), the inflection point where the tumor stops shrinking and starts to regrow, and the depth of response ( DpR) after initiation of anti-VEGF therapy for each patient. Log-rank, univariate, and multivariable Cox regression were used to quantify the relationship of these parameters and OS. Results: Optimized thresholds based on maximum hazard ratio occurred at d =0.11 months-1 ( P=5x10-9 training; P=0.0082 validation); g=0.07 months-1 ( P=5x10-20 training; P&lt;0.0001 validation); TTG of 3.8 months ( P=6x10-17training; P&lt;0.0001 validation); and DpR of 11.3% ( P=0.0026 training; P=0.0177 validation). Univariate and multivariable Cox regression using these thresholds and controlling for age and baseline tumor volume confirmed that d, g, TTG, and DpR were all significant and independent predictors of OS in both phase II training and phase III validation datasets ( P&lt;0.01). Additionally, patients exhibiting combinations of a high DpR and/or long TTG showed differential median OS, while patients with favorable diffusion MR phenotypes exhibited a longer OS and significantly longer TTG. Conclusions: Volumetric response and regrowth rates, TTG, and DpR using a biexponential model are significant and independent predictors of OS in recurrent glioblastoma treated with anti-VEGF therapy. Clinical trial information: NCT00345163 ; NCT00704288 ; NCT02511405 .

Lesion probability map in cerebral vein thrombosis due to Behçet's disease.

Behçet's disease (BD) is a multisystemic inflammatory disease. Cerebral venous sinus thrombosis (CVST) is the second most common form of neuro-BD after parenchymal central nervous system involvement. The purpose of this study was to construct flow-void probability maps of patients with CVST with and without BD to visually illustrate the impacted cerebral venous sinuses, to compare the subgroups of patients, and investigate the effect of thrombus localization on clinical findings. Seventeen patients with a diagnosis of BD-related CVST (CVST-BD) and 23 patients with a diagnosis of CVST related to other etiologies (CVST-O) were included. We collected data including gender, age at onset of BD and CVST, presenting symptoms, neurological findings, and the etiology. High-resolution magnetic resonance venographies obtained during CVST were used to mark and digitalize thrombosed areas. Thrombus probability and subtraction maps were created to reveal the differences between the subgroups. Remarkably, all patients with CVST-BD had thrombosis in the transverse sinus (TS). However, TS was affected in 73.9% of the CVST-O patients (17/17 in CVST-BD vs 17/23 in CVST-O, P=.03). Thrombosis developed mostly in the superior sagittal sinus (SSS) and TS in the CVST-O group (11/23, 47.8% and 17/23, 73.9%, respectively). The frequency of SSS thrombosis tended to be higher in the CVST-O (47.8% vs 23.5%, P=.19). Venous infarction and hemorrhage were less common in patients with CVST-BD. The only clinical symptom in most of the CSVT patients with BD was headache due to elevated intracranial pressure. TS thrombosis was more common in patients with BD.

Emerging trends in Diagnosis and Treatment of Brain Tumor

Brain tumors are rare but have high mortality rate among children and young adults. The purpose of this report is to portray the situation of imaging strategies and advancements for distinguishing reaction of cerebrum tumors to remedy within the placing of multicenter medical trials. Inside as of now utilized advances, usage of institutionalized image procurement and the usage of volumetric appraisals and subtraction maps are likely going to decorate tumor notion, depiction, and dimension. Throughout the subsequent couple of years, new innovations, for instance, 23Na MRI and CEST imaging improvements may be investigated for their usage in growing the ability to quantitatively photo tumor response in order to provide remedies in a scientific trial placing. The combination of poor visualization and absence of remedial choices urge the need to enhance clinical results for patients experiencing CNS malignancies

IRlab - Platform for thermal video analysis in evaluation of peripheral thermal behavior and blood perfusion

Background and objectivesDynamic thermal imaging in medicine has several advantages in comparison to static thermal image analysis and has potential as a novel patient assessment method e.g. in the area of vascular surgery. Since dynamic thermal imaging has become in the scope of research only during the last decade, the computational available analysis methods are often lacking or not existing. Most of the published software is not available to the research community or are behind a paywall. IRlab provides an easy-to-use dynamic thermal video processing and analysis platform, freely accessible to researchers. MethodsIRlab is programmed in Matlab R2020b. Computational tools for dynamic analysis are divided into spatio-temporal and spectral methods, where spatio-temporal methods consist of region of interest delineation tools, thermal modulation analysis, standard thermal measures such as median, maximum, minimum and deviation values, and subtraction and gamma maps. Spectral methods include spectral band power, spectral flow, and wavelet analysis tools. Preliminary data of a single healthy subject was analyzed with the program as a sample run. ResultsIRlab provides a platform for lower limb thermal image and video analysis with a clear workflow and variety of processing and analysis tools for time and frequency space analysis. The whole source code for IRlab is freely available for the research community under the General public license. ConclusionsIRlab is a versatile tool for dynamic thermal image and video processing. Freeware and open-source programs for medical thermal imaging are severely lacking, thus as a completely open-source project IRlab offers a unique platform for researchers within the field of medical thermal imaging.

NIMG-30. PATHOLOGICAL VALIDATION OF CONTRAST ENHANCEMENT AT AUTOPSY BETWEEN PATIENTS TREATED AND UNTREATED WITH CHEMOTHERAPY AND RADIATION

Abstract Tumor heterogeneity in glioblastoma complicates delineation of active tumor using standard MR imaging. Pseudo-progression following treatment with chemotherapy and radiation (chemoRT) further complicates how tumors appear. T1-weighted subtraction maps (T1S) have been used to better identify subtly enhancing regions containing infiltrative tumor. This study examines the differences in tumor appearance between patients treated with chemoRT compared to a cohort opting out at autopsy, to understand how chemoRT changes contrast enhancement on MRI. Ten patients diagnosed with glioblastoma were recruited for whole brain donation for this study. Three patients received no treatment (chemoRT-), and seven received a combination of chemoRT and additional treatments (chemoRT+), including but not limited to bevacizumab (Bev) and tumor treating fields (TTF). Large tissue samples were taken at autopsy from whole brain samples sliced axially to align with the last clinical MRI using patient-specific 3D-printed slicing jigs. All tissue samples were hematoxylin and eosin (HE) stained and digitized at 40X resolution (27 total samples). The whole slide images (WSI) were annotated to outline regions containing necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor. T1S were created for each patient by subtracting intensity normalized T1-weighted images from T1 post-contrast images (T1C). The annotated WSIs were aligned and resampled into MRI space using a custom software. A mixed effect model was used to compare the T1S intensity between chemoRT +/- cohorts within each pathological annotation, incorporating a random effect for subject. Mean T1S intensity was greater in untreated subjects when compared to chemoRT-subjects within each pathological annotation, including necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor (p&amp;lt; 0.001). We show that chemoRT reduces the contrast enhancement in all aspects of pathologically validated tumor compartments, including infiltrative tumor. Future research is needed to examine if other patterns are evident in additional MR sequences.

Measurement of treatment dependent glioblastoma cell density in T1- weighted contrast enhancement at autopsy.

e14035 Background: With an average overall survival of 12-18 months, glioblastoma has a particularly grim diagnosis. Standard treatment of glioblastoma, following detection on MRI, is surgical resection followed by radiation therapy and chemotherapy and is monitored through MR imaging. Glioblastoma has a unique heterogenous nature that complicates visualization of subtly enhancing tumor. This study used autopsy tissue samples taken from glioblastoma patients with varying treatment, to examine the effects of treatment on cell density within regions of contrast enhancement, using T1-weighted subtraction maps (T1S) from the last MR images to death. Methods: Eight patients diagnosed with glioblastoma at autopsy were recruited for this study. Two patients had no treatment and six received a combination of chemo-radiation and other treatments, including but not limited to bevacizumab (Bev) and TTFields therapy. At autopsy, whole brain samples were sliced axially aligned to the patient’s final MRI to death. Time between last MRI and death ranged from 4-27 days (mean 16 days). Overall survival (OS) ranged from 4-538 days (mean 307 days). Large tissue samples were taken from regions of suspected tumor or treatment effect, for a total of 18 tissue samples. Tissue samples were processed, H&amp;E stained, and digitized at 40X resolution (Huron Slide Scanner). Cell density (cells/mm2) was calculated using digital histology. T1S were created for each patient by subtracting intensity normalized T1 weighted images from T1 post contrast images (T1C). Digital histology was aligned and resampled into MRI space using manual control point registration. Mixed effect models were used to compare differences in cell density across contrast enhancement (T1SE vs. NE) as well as across treatment groups (treatment vs. no treatment). Results: Cellularity was compared across regions of T1S enhancement (T1SE) and non-enhancement (NE) within manually selected regions of interest. Cell density compared between regions of T1SE and NE was not different (p=0.219). Total cell density was increased in patients who had received treatment compared to no treatment in both regions of T1SE and NE (p=0.014). Conclusions: Overall, cell density was increased in patients who had received treatment after diagnosis of glioblastoma. Additional research is needed to examine the extent of treatment’s effect on cellularity of glioblastoma. This work begins to characterize the use of T1S in evaluating glioblastoma tumor burden in patients with varying treatment histories.[Table: see text]

Subtraction Maps Derived from Longitudinal Magnetic Resonance Imaging in Patients with Glioma Facilitate Early Detection of Tumor Progression.

Simple SummaryTumor recurrence is common among World Health Organization (WHO) grade II to IV gliomas. Magnetic resonance imaging (MRI) including fluid attenuated inversion recovery (FLAIR) sequences is key to detection of recurrence. This study used subtraction maps (SMs) derived from serial FLAIR imaging in 100 patients with glioma to facilitate detection of progressive disease, compared to conventional (CONV) visual reading. Reading of SMs revealed FLAIR signal increases in a larger proportion of patients and with higher diagnostic confidence according to evaluations of two readers. Correspondingly, an improved sensitivity (99.9% vs. 73.3%) was observed for SM reading when compared to CONV reading.Progression of glioma is frequently characterized by increases or enhanced spread of a hyperintensity in fluid attenuated inversion recovery (FLAIR) sequences. However, changes in FLAIR signal over time can be subtle, and conventional (CONV) visual reading is time-consuming. The purpose of this monocentric, retrospective study was to compare CONV reading to reading of subtraction maps (SMs) for serial FLAIR imaging. FLAIR datasets of cranial 3-Tesla magnetic resonance imaging (MRI), acquired at two different time points (mean inter-scan interval: 5.4 ± 1.9 months), were considered per patient in a consecutive series of 100 patients (mean age: 49.0 ± 13.7 years) diagnosed with glioma (19 glioma World Health Organization [WHO] grade I and II, 81 glioma WHO grade III and IV). Two readers (R1 and R2) performed CONV and SM reading by assessing overall image quality and artifacts, alterations in tumor-associated FLAIR signal over time (stable/unchanged or progressive) including diagnostic confidence (1—very high to 5—very low diagnostic confidence), and time needed for reading. Gold-standard (GS) reading, including all available clinical and imaging information, was performed by a senior reader, revealing progressive FLAIR signal in 61 patients (tumor progression or recurrence in 38 patients, pseudoprogression in 10 patients, and unclear in the remaining 13 patients). SM reading used an officially certified and commercially available algorithm performing semi-automatic coregistration, intensity normalization, and color-coding to generate individual SMs. The approach of SM reading revealed FLAIR signal increases in a larger proportion of patients according to evaluations of both readers (R1: 61 patients/R2: 60 patients identified with FLAIR signal increase vs. R1: 45 patients/R2: 44 patients for CONV reading) with significantly higher diagnostic confidence (R1: 1.29 ± 0.48, R2: 1.26 ± 0.44 vs. R1: 1.73 ± 0.80, R2: 1.82 ± 0.85; p < 0.0001). This resulted in increased sensitivity (99.9% vs. 73.3%) with maintained high specificity (98.1% vs. 98.8%) for SM reading when compared to CONV reading. Furthermore, the time needed for SM reading was significantly lower compared to CONV assessments (p < 0.0001). In conclusion, SM reading may improve diagnostic accuracy and sensitivity while reducing reading time, thus potentially enabling earlier detection of disease progression.

The left anterior right temporal (LART) placement for electroconvulsive therapy: A computational modelling study

Electrode placement in electroconvulsive therapy (ECT) has a major impact on treatment efficacy and cognitive side effects. Left Anterior Right Temporal (LART) is a lesser utilised bilateral montage which may produce more optimal clinical outcomes relative to standard bitemporal ECT. In this study we used computational modelling to explore how stimulation effects from LART and two novel variants (LART – F3 and LART – Frontal) compared to the more common bilateral placements of bitemporal and bifrontal ECT. High resolution finite element human head models were generated from MRI scans of three subjects with Major Depressive Disorder. Differences in regional stimulation were examined through parametric tests for regions of interest and subtraction maps. Compared to bitemporal ECT, LART – Original resulted in significantly greater stimulation of the left cingulate gyrus (hypothesised to be associated with treatment efficacy), and relatively reduced stimulation of the bilateral hippocampi (potentially associated with cognitive side effects). No additional clinical benefit was suggested with the novel LART placements compared to the original LART. The original LART placement is a promising montage for further clinical investigation.

Postictal brainstem hypoperfusion and risk factors for sudden unexpected death in epilepsy.

Since the strongest risk factor for sudden unexpected death in epilepsy (SUDEP) is frequent bilateral tonic-clonic seizures (BTCS), our aim was to determine whether postictal hypoperfusion in brainstem respiratory centers (BRCs) is more common following tonic-clonic seizures. We studied 21 patients with focal epilepsies who underwent perfusion imaging with arterial spin labeling MRI. Subtraction maps of cerebral blood flow were obtained from the postictal and baseline scans. We identified 6 regions of interest in the brainstem that contain key BRCs. Patients were considered to have postictal BRC hypoperfusion if any of the 6 regions of interest were significantly hypoperfused. All 6 patients who experienced BTCS during the study had significant clusters of postictal hypoperfusion in BRCs compared to 7 who had focal impaired awareness seizures (7/15). The association between seizure type studied and the presence of BRC hypoperfusion was significant. Duration of epilepsy and frequency of BTCS were not associated with postictal brainstem hypoperfusion despite also being associated with risk for SUDEP. Postictal hypoperfusion in brainstem respiratory centers occurs more often following BTCS than other seizure types, providing a possible explanation for the increased risk of SUDEP in patients who regularly experience BTCS.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

PurposeWe propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.Materials and MethodsFifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsIn all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.ConclusionPre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software.

The diagnostic reading of follow-up low-dose whole-body computed tomography (WBCT) examinations in patients with multiple myeloma (MM) is a demanding process. This study aimed to evaluate the diagnostic accuracy and benefit of a novel software program providing rapid-subtraction maps for bone lesion change detection. Sixty patients (66years ± 10years) receiving 120 WBCT examinations for follow-up evaluation of MM bone disease were identified from our imaging archive. The median follow-up time was 292days (range 200-641days). Subtraction maps were calculated from 2-mm CT images using a nonlinear deformation algorithm. Reading time, correctly assessed lesions, and disease classification were compared to a standard reading software program. De novo clinical reading by a senior radiologist served as the reference standard. Statistics included Wilcoxon rank-sum test, Cohen's kappa coefficient, and calculation of sensitivity, specificity, positive/negative predictive value, and accuracy. Calculation time for subtraction maps was 84s ± 24s. Both readers reported exams faster using subtraction maps (reader A, 438s ± 133s; reader B, 1049s ± 438s) compared to PACS software (reader A, 534s ± 156s; reader B, 1486s ± 587s; p< 0.01). The course of disease was correctly classified by both methods in all patients. Sensitivity for lesion detection in subtraction maps/conventional reading was 92%/80% for reader A and 88%/76% for reader B. Specificity was 98%/100% for reader A and 95%/96% for reader B. A software program for the rapid-subtraction map calculation of follow-up WBCT scans has been successfully tested and seems suited for application in clinical routine. Subtraction maps significantly facilitated reading of WBCTs by reducing reading time and increasing sensitivity. • A novel algorithm has been successfully applied to generate motion-corrected bone subtraction maps of whole-body low-dose CT scans in less than 2min. • Motion-corrected bone subtraction maps significantly facilitate the reading of follow-up whole-body low-dose CT scans in multiple myeloma by reducing reading time and increasing sensitivity.

NIMG-54. DIFFUSION MRI PHENOTYPES PREDICT OVERALL SURVIVAL BENEFIT FROM BEVACIZUMAB IN RECURRENT GLIOBLASTOMA WITH A LARGE TUMOR BURDEN: EVIDENCE FROM CLINICAL PRACTICE AND A MULTICENTER PHASE 3 TRIAL

Abstract We have previously shown that diffusion MR characteristics are a predictive imaging biomarker for survival benefit in recurrent glioblastoma treated with anti-VEGF therapy; however, contemporary clinical use of bevacizumab is often limited to patients with very large tumors and/or after multiple recurrences. We hypothesize that diffusion MR characteristics can be used to predict long-term survival benefit in these patients, which may be beneficial for clinical decision-making. The current study identified 83 recurrent glioblastoma patients from our institution who were treated with bevacizumab over the past 5 years with high quality anatomic and diffusion MRI data. Of these 83 patients, 35 had large contrast enhancing tumors (&gt;20cc or &gt;3.4cm diameter, group average). Additionally, we identified 37 recurrent glioblastoma patients from the bevacizumab treated control arm of a recent multicenter phase III trial (NCT02511405) with high quality data and large enhancing tumors for validation. Pre-treatment tumor volume was quantified using T1 subtraction maps and apparent diffusion coefficient (ADC) histogram analysis was used to phenotype patients as having high (&gt; 1.24 um2/ms) or low (&lt; 1.24 um2/ms) ADCL, the mean value of the lower peak using a double Gaussian mixed model. Median overall survival in patients with large volume recurrent glioblastoma was ~5.7 months. High ADCL was associated with significantly longer overall survival (OS) compared with low ADCL in both single center (P=0.0271, HR=0.486, mOS=5.5 vs. 2.8mo) and multicenter phase III data (P=0.0457, HR=0.507, mOS=7.9 vs. 5.7mo). Accounting for absolute tumor volume and age, both cohorts showed that ADCL was an independent prognostic factor for OS (Cox, P&lt; 0.01). In summary, pre-treatment diffusion MR imaging is an independent predictive biomarker for OS in recurrent glioblastoma with a large tumor burden.

Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study

Background and purposeAdvanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of multi-paramedic MRI. MethodsA total of 138 patients from 5 international centers that underwent MRI prior to surgical resection of intracranial meningiomas were included. Segmentation was performed manually on co-registered multi-parametric MR images using apparent diffusion coefficient (ADC) maps, T1-weighted (T1), post-contrast T1-weighted (T1c), subtraction maps (Sub, T1c – T1), T2-weighted fluid-attenuated inversion recovery (FLAIR) and T2-weighted (T2) images. Feature selection was performed and using cross-validation to separate training from testing data, four machine learning classifiers were scored on combinations of MRI modalities: random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP). ResultsThe best AUC of 0.97 (1.0 and 0.97 for sensitivity and specificity) was observed for the combination of ADC, ADC of the peritumoral edema, T1, T1c, Sub and FLAIR-derived features using only 16 of the 10,914 possible features and XGBoost. ConclusionsMachine learning using radiomics features derived from multi-parametric MRI is capable of high AUC scores with high sensitivity and specificity in classifying meningiomas between low and higher gradings despite heterogeneous protocols across different centers. Feature selection can be performed effectively even when extracting a large amount of data for radiomics fingerprinting.

Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study.

Background Dual-energy CT iodine maps are used to detect pulmonary embolism (PE) with CT angiography but require dedicated hardware. Subtraction CT, a software-only solution, results in iodine maps with high contrast-to-noise ratios. Purpose To compare the use of subtraction CT versus dual-energy CT iodine maps to CT angiography for PE detection. Materials and Methods In this prospective study ( https://clinicaltrials.gov , NCT02890706), 274 participants suspected of having PE underwent precontrast CT followed by contrast material-enhanced dual-energy CT angiography between July 2016 and April 2017. Iodine maps from dual-energy CT were derived. Subtraction maps (contrast-enhanced CT minus precontrast CT) were calculated after motion correction. Truth was established by expert consensus. A total of 75 randomly selected participants with and without PE (1:1 ratio) were evaluated by three radiologists and six radiology residents (blinded to final diagnosis) for the presence of PE using three types of CT: CT angiography alone, dual-energy CT, and subtraction CT. The partial area under the receiver operating characteristic curve (AUC) for the clinically relevant specificity region (maximum partial AUC, 0.11) was compared by using multireader multicase variance. A P value less than or equal to .025 was considered indicative of a significant difference due to multiple comparisons. Results There were 35 men and 40 women in the reader study (mean age, 63 years ± 12 [standard deviation]). The pooled sensitivities were not different (P ≥ .31 among techniques) (95% confidence intervals [CIs]: 67%, 89% for CT angiography; 72%, 91% for dual-energy CT; 70%, 91% for subtraction CT). However, pooled specificity was higher for subtraction CT (95% CI: 100%, 100%) than for CT angiography (95% CI: 89%, 97%) or dual-energy CT (95% CI: 89%, 98%) (P < .001). Partial AUCs for the average observer improved equally when adding iodine maps (subtraction CT [0.093] vs CT angiography [0.088], P = .03; dual-energy CT [0.094] vs CT angiography, P = .01; dual-energy CT vs subtraction CT, P = .68). Average reading times were equivalent (range, 97-101 seconds; P ≥ .41) among techniques. Conclusion Subtraction CT iodine maps had greater specificity than CT angiography alone in pulmonary embolism detection. Subtraction CT had comparable diagnostic performance to that of dual-energy CT, without the need for dedicated hardware. © RSNA, 2019 Online supplemental material is available for this article.

Accuracy of Unenhanced MRI in the Detection of New Brain Lesions in Multiple Sclerosis

Background Administration of a gadolinium-based contrast material is widely considered obligatory for follow-up imaging of patients with multiple sclerosis (MS). However, advances in MRI have substantially improved the sensitivity for detecting new or enlarged lesions in MS. Purpose To investigate whether the use of contrast material has an effect on the detection of new or enlarged MS lesions and, consequently, the assessment of interval progression. Materials and Methods In this retrospective study based on a local prospective observational cohort, 507 follow-up MR images obtained in 359 patients with MS (mean age, 38.2 years ± 10.3; 246 women, 113 men) were evaluated. With use of subtraction maps, nonenhanced images (double inversion recovery [DIR], fluid-attenuated inversion recovery [FLAIR]) and contrast material-enhanced (gadoterate meglumine, 0.1 mmol/kg) T1-weighted images were separately assessed for new or enlarged lesions in independent readings by two readers blinded to each other's findings and to clinical information. Primary outcome was the percentage of new or enlarged lesions detected only on contrast-enhanced T1-weighted images and the assessment of interval progression. Interval progression was defined as at least one new or unequivocally enlarged lesion on follow-up MR images. Results Of 507 follow-up images, 264 showed interval progression, with a total of 1992 new or enlarged and 207 contrast-enhancing lesions. Four of these lesions (on three MR images) were retrospectively detected on only the nonenhanced images, corresponding to 1.9% (four of 207) of the enhancing and 0.2% (four of 1992) of all new or enlarged lesions. Nine enhancing lesions were not detected on FLAIR-based subtraction maps (nine of 1442, 0.6%). In none of the 507 images did the contrast-enhanced sequences reveal interval progression that was missed in the readouts of the nonenhanced sequences, with use of either DIR- or FLAIR-based subtraction maps. Interrater agreement was high for all three measures, with intraclass correlation coefficients of 0.91 with FLAIR, 0.94 with DIR, and 0.99 with contrast-enhanced T1-weighted imaging. Conclusion At 3.0 T, use of a gadolinium-based contrast agent at follow-up MRI did not change the diagnosis of interval disease progression in patients with multiple sclerosis. © RSNA, 2019 See also the editorial by Saindane in this issue.