A Unique Case of Eight Fetuses in a Single Fetu.

Fetus in fetu is a rare congenital anomaly in which a malformed parasitic twin is found within the body of a living child or adult. In this case report, a 1-day-old child presented with a large firm abdominal mass on the left side of the upper abdomen. Imaging studies misdiagnosed the mass as an intraperitoneal benign dermoid cyst displacing the bowel loops and internal viscera. A surgical resection was performed on 21 days of life, and pathology confirmed eight fetuses inside the cyst.

Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.

Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; -2000, 2000) in Bland-Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.

Greater burden of white matter lesions and silent infarcts ipsilateral to carotid stenosis.

OBJECTIVES: Carotid stenosis may cause silent cerebrovascular disease (CVD) through atheroembolism and hypoperfusion. If so, revascularization may slow progression of silent CVD. We aimed to compare the presence and severity of silent CVD to the degree of carotid bifurcation stenosis by cerebral hemisphere. MATERIALS AND METHODS: Patients age ≥40 years with carotid stenosis >50% by carotid ultrasound who underwent MRI brain from 2011-2015 at Mayo Clinic were included. Severity of carotid stenosis was classified by carotid duplex ultrasound as 50-69% (moderate), 70-99% (severe), or occluded. White matter lesion (WML) volume was quantified using an automated deep-learning algorithm applied to axial T2 FLAIR images. Differences in WML volume and prevalent silent infarcts were compared across hemispheres and severity of carotid stenosis. RESULTS: Of the 183 patients, mean age was 71±10 years, and 39.3% were female. Moderate stenosis was present in 35.5%, severe stenosis in 46.5% and occlusion in 18.0%. Patients with carotid stenosis had greater WML volume ipsilateral to the side of carotid stenosis than the contralateral side (mean difference, 0.42±0.21cc, p=0.046). Higher degrees of stenosis were associated with greater hemispheric difference in WML volume (moderate vs. severe; 0.16±0.27cc vs 0.74±0.31cc, p=0.009). Prevalence of silent infarct was 23.5% and was greater on the side of carotid stenosis than the contralateral side (hemispheric difference 8.8%±3.2%, p=0.006). Higher degrees of stenosis were associated with higher burden of silent infarcts (moderate vs severe, 10.8% vs 31.8%; p=0.002). CONCLUSIONS: WML and silent infarcts were greater on the side of severe carotid stenosis.

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions.

Purpose: Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "pre-deployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions. Approach: A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization. Results: During model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing ( AUC ≥ 0.98 and ≥ 0.99 , respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRI-unsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement. Conclusions: Our LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future real-world experiences.

Transradial versus transfemoral arterial access in DEB-TACE for hepatocellular carcinoma.

Objectives: Transradial access has become increasingly popular in body interventional procedures but has not been ubiquitously adapted. This retrospective study compares the efficacy of this approach versus transfemoral access in hepatocellular carcinoma (HCC) patients who underwent drug-eluting bead transarterial chemoembolization (DEB-TACE). Materials and Methods: A total of 130 HCC patients underwent 146 DEB-TACE procedures within our institution from June 2015 to May 2020. About 90 and 56 procedures were logged for the transradial and transfemoral cohorts, respectively. Peak skin dose, fluoroscopy time, administered contrast volume, total procedure time, and equipment cost data for each procedure were reviewed to evaluate for statistical differences between the two groups. Results: All 146 cases were technically successful without major complications or access failures in either group. No statistical differences were present between the two access groups in regards to peak skin dose or fluoroscopy time. Transradial access recorded a significantly higher contrast volume (P < 0.05), and a significantly longer procedural time than transfemoral access (P < 0.01). However, transradial access also displayed a significantly lower procedural equipment cost (P < 0.01) between the two groups. Conclusion: Transradial DEB-TACE has similar trends to transfemoral DEB-TACE in several pertinent radiation parameters and is also significantly more cost-efficacious. The results of this investigation suggest the consideration of transradial access whenever viable as an alternative to transfemoral access in the DEB-TACE treatment of HCC patients.

Transradial versus transfemoral arterial access in the uterine artery embolization of fibroids.

Purpose: Transradial arterial access has become more popular in body interventional procedures but has not been ubiquitously adapted. This retrospective study assesses the efficacy of this approach in uterine artery embolization. Aim of the study was to compare transradial to transfemoral arterial access in patients undergoing uterine artery embolization for the treatment of fibroids. Material and methods: A total of 172 patients underwent uterine artery embolization procedures at our institute from October 2014 to June 2020. Of these, 76 patients had their operations performed via transfemoral access while 96 underwent transradial access. The peak radiation dose, fluoroscopy time, procedure time, total contrast volume, and equipment cost for each procedure were all reviewed to evaluate for statistical differences between the 2 groups. Results: All cases were technically successful without major complications. The average peak skin dose was 2281 mGy,with no statistical difference between the transradial or transfemoral cohorts. Average fluoroscopy time was 25 minutes, also with no statistical difference between the subsets. Mean procedure time was 100 min, and mean contrast volume usage was 138 mL with no statistical differences. Similarly, the average equipment cost was $2204, with no significant differences found between transradial and transfemoral access. Conclusions: With respect to many pertinent radiation parameters, transradial access was evaluated as being an equally efficacious alternative to transfemoral access in uterine artery embolization procedures. The results of this study suggest that transradial access should be considered more often, whenever viable, as an option in the uterine artery embolization treatment of fibroids.

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use.

Coronary computed tomography angiography (CCTA) evaluation of chest pain patients in an emergency department (ED) is considered appropriate. While a "negative" CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an artificial intelligence (AI) algorithm and workflow for assisting qualified interpreting physicians in CCTA screening for total absence of coronary atherosclerosis. The two-phase approach consisted of (1) phase 1-development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection, and (2) phase 2-simulated clinical Trialing of developed algorithm on a per-case (entire coronary artery tree) basis in a more "real-world" study population (n = 100 with 28% disease prevalence) from an ED chest pain series. This allowed pre-deployment evaluation of the AI-based CCTA screening application which provides vessel-by-vessel graphic display of algorithm inference results integrated into a clinically capable viewer. Algorithm performance evaluation used area under the receiver operating characteristic curve (AUC-ROC); confusion matrices reflected ground truth vs AI determinations. The vessel-based algorithm demonstrated strong performance with AUC-ROC = 0.96. In both phase 1 and phase 2, independent of disease prevalence differences, negative predictive values at the case level were very high at 95%. The rate of completion of the algorithm workflow process (96% with inference results in 55-80 s) in phase 2 depended on adequate image quality. There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest pain presentations.

Abdominal Computed Tomography Angiography and Venography in Evaluation of Hemorrhagic and Thrombotic lesions in Hospitalized COVID-19 patients.

PURPOSE: To report imaging findings at computed tomography angiography (CTA) and venography (CTV) of the abdomen and pelvis in evaluation of hemorrhagic and thrombotic lesions in hospitalized patients with COVID-19. METHODS: In this retrospective observational study, patients admitted to a single tertiary care center from April 1 to July 20, 2020, who tested positive for SARS-CoV-2 and developed acute abdominal pain or decreasing hemoglobin levels over the course of hospitalization were included. Abdominal CTA/CTV imaging studies performed in these patients were reviewed, and acute hemorrhagic or thromboembolic findings were recorded. RESULTS: A total of 40 patients (mean age, 59.7 years; 20 men, 20 women) were evaluated. Twenty-five patients (62.5%) required intensive care unit (ICU) admission and 15 patients (37.5%) were treated in the medical ward. Hemorrhagic complications were detected in 19 patients (47.5%), the most common was intramuscular hematoma diagnosed in 17 patients; It involved the iliopsoas compartment unilaterally in 10 patients, bilaterally in 2 patients and the rectus sheath in 5 cases. Pelvic extraperitoneal hemorrhage was found in 3 patients, and mesenteric hematoma in one patient. Thromboembolic events were diagnosed in 8 patients (20%) including; arterial thrombosis (n = 2), venous thrombosis (n = 2), splenic infarct (n = 1), bowel ischemia (n = 1) and multiple sites of thromboembolism (n = 2). CONCLUSION: Our study highlights that both hemorrhagic and thromboembolic complications can be seen in hospitalized patients with COVID-19. It is important that radiologists maintain a high index of suspicion for early diagnosis of these complications.

Association of Immunosuppression and Viral Load With Subcortical Brain Volume in an International Sample of People Living With HIV.

Importance: Despite more widely accessible combination antiretroviral therapy (cART), HIV-1 infection remains a global public health challenge. Even in treated patients with chronic HIV infection, neurocognitive impairment often persists, affecting quality of life. Identifying the neuroanatomical pathways associated with infection in vivo may delineate the neuropathologic processes underlying these deficits. However, published neuroimaging findings from relatively small, heterogeneous cohorts are inconsistent, limiting the generalizability of the conclusions drawn to date. Objective: To examine structural brain associations with the most commonly collected clinical assessments of HIV burden (CD4+ T-cell count and viral load), which are generalizable across demographically and clinically diverse HIV-infected individuals worldwide. Design, Setting, and Participants: This cross-sectional study established the HIV Working Group within the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) consortium to pool and harmonize data from existing HIV neuroimaging studies. In total, data from 1295 HIV-positive adults were contributed from 13 studies across Africa, Asia, Australia, Europe, and North America. Regional and whole brain segmentations were extracted from data sets as contributing studies joined the consortium on a rolling basis from November 1, 2014, to December 31, 2019. Main Outcomes and Measures: Volume estimates for 8 subcortical brain regions were extracted from T1-weighted magnetic resonance images to identify associations with blood plasma markers of current immunosuppression (CD4+ T-cell counts) or detectable plasma viral load (dVL) in HIV-positive participants. Post hoc sensitivity analyses stratified data by cART status. Results: After quality assurance, data from 1203 HIV-positive individuals (mean [SD] age, 45.7 [11.5] years; 880 [73.2%] male; 897 [74.6%] taking cART) remained. Lower current CD4+ cell counts were associated with smaller hippocampal (mean [SE] ß = 16.66 [4.72] mm3 per 100 cells/mm3; P < .001) and thalamic (mean [SE] ß = 32.24 [8.96] mm3 per 100 cells/mm3; P < .001) volumes and larger ventricles (mean [SE] ß = -391.50 [122.58] mm3 per 100 cells/mm3; P = .001); in participants not taking cART, however, lower current CD4+ cell counts were associated with smaller putamen volumes (mean [SE] ß = 57.34 [18.78] mm3 per 100 cells/mm3; P = .003). A dVL was associated with smaller hippocampal volumes (d = -0.17; P = .005); in participants taking cART, dVL was also associated with smaller amygdala volumes (d = -0.23; P = .004). Conclusions and Relevance: In a large-scale international population of HIV-positive individuals, volumes of structures in the limbic system were consistently associated with current plasma markers. Our findings extend beyond the classically implicated regions of the basal ganglia and may represent a generalizable brain signature of HIV infection in the cART era.

Ventilator associated lung injury in severe COVID-19 pneumonia patients - Case Reports: Ventilator associated lung injury in COVID-19.

Management of severe coronavirus disease 2019 requires advanced respiratory support modalities including invasive mechanical ventilation (IMV), continuous positive airway pressure (C-PAP), and non-invasive ventilation ((NIV). IMV leads to either subtle forms of lung injury (pulmonary edema, lung cysts) or more severe form of lung injury manifested as subcutaneous emphysema, pneumomediastinum, and pneumothorax (herein collectively termed barotrauma). We have described two cases showing the two end of spectrum of ventilator associated lung injury (VALI).

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.

Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network.

We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The system learns the discriminatory features between vessels with and without atherosclerosis. The discriminative features at the final convolutional layer are visualized with a saliency map approach to provide visual clues related to atherosclerosis likelihood and location. We have evaluated the system on a reference dataset representing 247 patients with atherosclerosis and 246 patients free of atherosclerosis. With five fold cross-validation, an Accuracy = 90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.9%, Specificity of 93.6%, and Negative Predictive Value (NPV) = 96.1% are achieved at the artery/branch level with threshold 0.5. The average area under the receiver operating characteristic curve is 0.91. The system indicates a high NPV, which may be potentially useful for assisting interpreting physicians in excluding coronary atherosclerosis in patients with acute chest pain.

Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning.

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA.

A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.

PURPOSE: To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging. MATERIALS AND METHODS: GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and three-dimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading. RESULTS: For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3-21 seconds per study]). A total of 294 coronary CT angiographic studies with 1843 arteries and branches were annotated for atherosclerotic plaque over 23 days (15.2 studies [80.1 vessels] per day; median speed: 6.08 minutes per study [interquartile range, 2.8-10.6 minutes per study] and 73 seconds per vessel [interquartile range, 20.9-155 seconds per vessel]). CONCLUSION: GUI-component compatibility with common image analysis tools facilitates radiologist engagement in image data curation, including image annotation, supporting AI application development and evolution for medical imaging. When complemented by other GUI elements, a continuous integrated workflow supporting formation of an agile deep neural network life cycle results.Supplemental material is available for this article.© RSNA, 2019.

ENIGMA MILITARY BRAIN INJURY: A COORDINATED META-ANALYSIS OF DIFFUSION MRI FROM MULTIPLE COHORTS.

Traumatic brain injury (TBI) is a significant cause of morbidity in military Veterans and Service Members. While most individuals recover fully from mild injuries within weeks, some continue to experience symptoms including headaches, disrupted sleep, and other cognitive, behavioral or physical symptoms. Diffusion magnetic resonance imaging (dMRI) shows promise in identifying areas of structural disruption and predicting outcomes. Although some studies suggest widespread structural disruption after brain injury, dMRI studies of military brain injury have yielded mixed results so far, perhaps due to the subtlety of mild injury, individual differences in injury location, severity and mechanism, and comorbidity with other disorders such as post-traumatic stress disorder (PTSD), depression, and substance abuse. We present preliminary dMRI results from the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) military brain injury working group. We found higher fractional anisotropy (FA) in participants with a history of TBI. Understanding the injury and recovery process, along with factors that influence these, will lead to improved diagnosis and treatment.

Geospatial assessment of tourism impact on land environment of Dehradun, Uttarakhand, India.

India's tourism industry has emerged as a leading industry with a potential to grow further in the next few decades. Dehradun, one of the famous tourist places in India located in the state of Uttarakhand, attracts tourist from all over the country and abroad. The surge in tourist number paved the way for new infrastructure projects like roads, buildings, and hotels, which in turn affects the topography of the mountainous region. In this study, remote sensing and GIS techniques have been used to assess the impact of tourism on the land environment of Dehradun. Satellite images of the years 1972, 2000, and 2016 were analyzed using object-based image analysis (OBIA) to derive land use and land cover (LULC) and ASTER-DEM (Digital Elevation Model) was used to determine the topography of the study area. LULC classification includes built-up, vegetation, forest, scrub, agriculture, plantation, and water body. The slope of the region was categorized as gentle, moderate, strong, extreme, steep, and very steep. To assess the sprawl of built-up on high terrain land, built-up class of LULC was overlaid on slope classes. The overlay analysis reveals that due to increase in tourism, the land use in terms of the built-up area has been extended from gentle slope to very steep slope. The haphazard construction on the extreme, steep, and very steep slope is prone to landslide and other natural disasters. For this, landslide susceptibility maps have also been generated using multicriteria evaluation (MCE) techniques to prevent haphazard construction and to assist in further planning of Dehradun City. This study suggests that a proper developmental plan of the city is essential which follows the principles of optimum use of land and sustainable tourism.

Brain cortical structural differences between non-central nervous system cancer patients treated with and without chemotherapy compared to non-cancer controls: a cross-sectional pilot MRI study using clinically-indicated scans.

BACKGROUND: Cognitive deficit associated with cancer and its treatment is called cancer-related cognitive impairment (CRCI). Increases in cancer survival have made understanding the basis of CRCI more important. CRCI neuroimaging studies have traditionally used dedicated research brain MRIs in breast cancer survivors after chemotherapy with small sample sizes; little is known about other non-central nervous system (CNS) cancers after chemotherapy as well as those not exposed to chemotherapy. However, there may be a wealth of unused data from clinically-indicated MRIs that could be used to study CRCI. OBJECTIVE: Evaluate brain cortical structural differences in those with various non-CNS cancers using clinically-indicated MRIs. DESIGN: Cross-sectional. PATIENTS: Adult non-CNS cancer and non-cancer control (C) patients who underwent clinically-indicated MRIs. METHODS: Brain cortical surface area and thickness were measured using 3D T1-weighted images. An age-adjusted linear regression model was used and the Benjamini and Hochberg false discovery rate (FDR) corrected for multiple comparisons. Group comparisons were: cancer cases with chemotherapy (Ch+), cancer cases without chemotherapy (Ch-) and subgroup of lung cancer cases with and without chemotherapy vs C. RESULTS: Sixty-four subjects were analyzed: 22 Ch+, 23 Ch- and 19 C patients. Subgroup analysis of 16 lung cancer (LCa) patients was also performed. Statistically significant decreases in either cortical surface area or thickness were found in multiple regions of interest (ROIs) primarily within the frontal and temporal lobes for all comparisons. Effect sizes were variable with the greatest seen in the left middle temporal surface area ROI (Cohen's d -0.690) in the Ch- vs C group comparison. LIMITATIONS: Several limitations were apparent including a small sample size that precluded adjustment for other covariates. CONCLUSIONS: Our preliminary results suggest that, in addition to breast cancer, other types of non-CNS cancers treated with chemotherapy may result in brain structural abnormalities. Similar findings also appear to occur in those not exposed to chemotherapy. These results also suggest that there is potentially a wealth of untapped clinical MRIs that could be used for future CRCI studies.

Stent retrieval thrombectomy in acute stoke is facilitated by the concurrent use of intracranial aspiration catheters.

BACKGROUND: New device technology has changed the techniques used for revascularization of emergent large vessel occlusion in acute stroke. We report technical results using stent retrievers (SRs) for thrombectomy alone versus SRs used in conjunction with a new group of devices, intracranial aspiration catheters (IACs). Our aim is to demonstrate differences in procedural time and thrombectomy attempts between these two groups. METHODS: A retrospective evaluation was performed of a prospectively maintained database of 97 patients treated at a single institution for anterior circulation stroke with SRs. Patients were divided into two groups, a combination group defined as the SR/IAC group and the SR alone group defined as the SR group. RESULTS: Patients in the SR/IAC group had a mean age of 66 years vs 59 years in the SR group (p=0.008). Mean presenting National Institutes of Health Stroke Scale (NIHSS) scores in the SR/IAC and control groups were 18.7 and 18.2, respectively (p=0.50). Recanalization rates (Thrombolysis In Cerebral Infarction (TICI) 2b or 3) in the SR/IAC and SR groups were 85% (58/68) and 90% (26/29), respectively (p=0.41). Mean time from groin arteriotomy to recanalization was 50±3.6 min (range 19-136) in the SR/IAC group (n=59) and 61±6.6 min (range 28-140) in the SR group (n=27) (p=0.049). The total number of thrombectomy attempts in the SR/IAC and SR groups were 1.9±0.1 (range 1-4) and 2.5±0.6 (range 1-6), respectively (p=0.009). Post-procedural subarachnoid hemorrhage was seen in 15% (10/68) and 10% (3/29) of cases in the SR/IAC and SR groups, respectively (p=0.41). CONCLUSION: When using SRs for intracranial stroke thrombectomy, the concurrent use of IACs is associated with a decrease in procedural time and thrombectomy attempts compared with use of SRs alone.

Spectrum of intracranial incidental findings on pediatric brain magnetic resonance imaging: What clinician should know?

Intracranial incidental findings on magnetic resonance imaging (MRI) of the brain continue to generate interest in healthy control, research, and clinical subjects. However, in clinical practice, the discovery of incidental findings acts as a "distractor". This review is based on existing heterogeneous reports, their clinical implications, and how the results of incidental findings influence clinical management. This draws attention to the followings: (1) the prevalence of clinically significant incidental findings is low; (2) there is a lack of a systematic approach to classification; and discusses (3) how to deal with the detected incidental findings based a proposed common clinical profile. Individualized neurological care requires an active discussion regarding the need for neuroimaging. Clinical significance of incidental findings should be decided based on lesion's neuroradiologic characteristics in the given clinical context. Available evidence suggests that the outcome of an incidentally found "serious lesion in children" is excellent. Future studies of intracranial incidental findings on pediatric brain MRI should be focused on a homogeneous population. The study should address this clinical knowledge based review powered by the statistical analyses.