Accuracy For Detection Of Lesions Research Articles

Two-stage lesion detection approach based on dimension-decomposition and 3D context

Lesion detection in Computed Tomography (CT) images is a challenging task in the field of computer-aided diagnosis. An important issue is to locate the area of lesion accurately. As a branch of Convolutional Neural Networks (CNNs), 3D Context-Enhanced (3DCE) frameworks are designed to detect lesions on CT scans. The False Positives (FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals, which slow down the inference time. To solve the above problems, a new method is proposed, a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion detection. Without the restriction of "anchors" on ratios and scales, anchors are decomposed to independent "anchor strings". Anchor segments are dynamically combined in accordance with probability, and anchor strings with different lengths dynamically compose bounding boxes. Experiments show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5.

As an effective tool for colorectal lesion detection, it is still difficult to avoid the phenomenon of missed and false detection when using white-light endoscopy. In order to improve the lesion detection rate of colorectal cancer patients, this paper proposes a real-time lesion diagnosis model (YOLOv5x-CG) based on YOLOv5 improvement. In this diagnostic model, colorectal lesions were subdivided into three categories: micropolyps, adenomas, and cancer. In the course of convolutional network training, Mosaic data enhancement strategy was used to improve the detection rate of small target polyps. At the same time, coordinate attention (CA) mechanism was introduced to take into account channel and location information in the network, so as to realize the effective extraction of three kinds of pathological features. The Ghost module was also used to generate more feature maps through linear processing, which reduces the stress of learning model parameters and speeds up detection. The experimental results show that the lesion diagnosis model proposed in this paper has a more rapid and accurate lesion detection ability, and the AP value of polyps, adenomas, and cancer is 0.923, 0.955, and 0.87, and mAP@50 is 0.916.

Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients.

Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.

Comparison of detection of trauma-related injuries using combined "all-in-one" fused images and conventionally reconstructed images in acute trauma CT.

To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT. In this retrospective study, trauma CT of 66 patients (median age 47years, range 18-96years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection. Reading time of residents was significantly faster using AIO (AIO: 266 ± 72s, CR: 318 ± 113s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d = - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82s, CR: 274 ± 95s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d = - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection. Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT. • Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy.

Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1w, and T2w) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using Siemens 3T (FLAIR: 1 mm isotropic, T1w: 1 mm isotropic, and T2w: 0.34–0.5 × 0.34–0.5 × 3–5 mm) and Hyperfine 64mT (FLAIR: 1.6 × 1.6 × 5 mm, T1w: 1.5 × 1.5 × 5 mm, and T2w: 1.5 × 1.5 × 5 mm) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The average smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T, approaching the spatial resolution of the respective scanner sequences (3T: 1 mm, 64mT: 5 mm slice thickness). Automated lesion volume estimates were highly correlated between 3T and 64mT scans (r = 0.89, p < 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = –19.0–15.9 ml), which over-estimated low lesion volume and under-estimated high volume (r = 0.74, p < 0.001). Visual inspection revealed over-segmentation was driven venous hyperintensities on 64mT T2-FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions > 1.0 ml and all lesions > 1.5 ml being detected. Using multi-acquisition volume averaging, we were able to generate 1.6 mm isotropic images on the 64mT device. Overall, our results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and that automated estimates of total lesion volume correlate with measurements from 3T scans.

Association of Inflammatory Periapical Lesions with Maxillary Sinus Abnormalities: a Retrospective Cone-Beam Computed Tomography Study.

Statement of the Problem: Odontogenic infections such as periapical lesions (PLs) can cause changes in the adjacent tissues. Infection of the maxillary posterior teeth can be easily transmitted to the maxillary sinus and cause changes in the maxillary sinus mucosa. Cone-beam computed tomography (CBCT) has high accuracy and sensitivity for detection of odontogenic lesions and is efficient for maxillary sinus assessment. Purpose: This study aimed to assess the maxillary sinuses for abnormalities such as mucosal thickening, polyps, and periostitis, and evaluate the periapical status of maxillary posterior teeth considering the presence of PLs, their size and distance from the sinus floor by evaluating CBCT images.Materials and Method: This retrospective, cross-sectional study evaluated the CBCT scans of 143 patients, depicting the posterior maxilla with at least one premolar or molar tooth present in this region. Sinus abnormalities (mucosal thickening, sinus polyps, and periostitis) and presence/ absence of PLs, its size, and its distance from the sinus floor were all assessed on CBCT scans. Data were analyzed using the Chi-square test in SPSS version 21 (a= 0.05).Results: PLs were observed in 31.2% of the cases. In presence of PLs, mucosal thickening was noted in 56.8%, sinus polyps in 29.6% and periostitis in 1.3% of the maxillary sinuses. All teeth with a CBCT periapical index (CBCTPAI) score of 5 were associated with sinus abnormalities; there was neither correlation between the sizes of lesions, nor their distance from the sinus floor with sinus abnormalities (p> 0.05).Conclusion: PLs in the posterior maxilla have a direct correlation with the maxillary sinus abnormalities. However, the size or distance of PLs from the sinus floor had no significant effect on the frequency of sinus abnormalities.

Combining CNN with Pathological Information for the Detection of Transmissive Lesions of Jawbones from CBCT Images.

Cone-Beam Computed Tomography (CBCT) imaging modality is used to acquire 3D volumetric image of the human body. CBCT plays a vital role in diagnosing dental diseases, especially cyst or tumour-like lesions. Current computer-aided detection and diagnostic systems have demonstrated diagnostic value in a range of diseases, however, the capability of such a deep learning method on transmissive lesions has not been investigated. In this study, we propose an automatic method for the detection of transmissive lesions of jawbones using CBCT images. We integrated a pre-trained DenseNet with pathological information to reduce the intra-class variation within a patient's images in the 3D volume (stack) that may affect the performance of the model. Our proposed method separates each CBCT stacks into seven intervals based on their disease manifestation. To evaluate the performance of our method, we created a new dataset containing 353 patients' CBCT data. A patient-wise image division strategy was employed to split the training and test sets. The overall lesion detection accuracy of 80.49% was achieved, outperforming the baseline DenseNet result of 77.18%. The result demonstrates the feasibility of our method for detecting transmissive lesions in CBCT images.Clinical relevance - The proposed strategy aims at providing automatic detection of the transmissive lesions of jawbones with the use of CBCT images that can reduce the workload of clinical radiologists, improve their diagnostic efficiency, and meet the preliminary requirement for the diagnosis of this kind of disease when there is a lack of radiologists.

Detection Accuracy and Latency of Colorectal Lesions with Computer-Aided Detection System Based on Low-Bias Evaluation.

We developed a computer-aided detection (CADe) system to detect and localize colorectal lesions by modifying You-Only-Look-Once version 3 (YOLO v3) and evaluated its performance in two different settings. The test dataset was obtained from 20 randomly selected patients who underwent endoscopic resection for 69 colorectal lesions at the Jikei University Hospital between June 2017 and February 2018. First, we evaluated the diagnostic performances using still images randomly and automatically extracted from video recordings of the entire endoscopic procedure at intervals of 5 s, without eliminating poor quality images. Second, the latency of lesion detection by the CADe system from the initial appearance of lesions was investigated by reviewing the videos. A total of 6531 images, including 662 images with a lesion, were studied in the image-based analysis. The AUC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.983, 94.6%, 95.2%, 68.8%, 99.4%, and 95.1%, respectively. The median time for detecting colorectal lesions measured in the lesion-based analysis was 0.67 s. In conclusion, we proved that the originally developed CADe system based on YOLO v3 could accurately and instantaneously detect colorectal lesions using the test dataset obtained from videos, mitigating operator selection biases.

CXCR4-Directed PET/CT with [68Ga]Pentixafor in Central Nervous System Lymphoma: A Comparison with [18F]FDG PET/CT.

This study aimed to evaluate the value of [68Ga]Pentixafor PET/CT for the detection of lesions in central nervous system lymphoma (CNSL) patients before chemotherapy, during treatment and suspected CSNL recurrence, compared with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT). Twenty-six patients with newly or previously diagnosed CNSL who underwent [68Ga]Pentixafor PET/CT were included retrospectively. Histopathological results, magnetic resonance imaging (MRI), and follow-up were used as the standard reference. The accuracy of lesion detection, maximum standardized uptake value (SUVmax) of tumors, and ratio of tumor-to-normal brain (T/N) with [68Ga]Pentixafor PET/CT were calculated and compared to those obtained with [18F]FDG PET/CT. CXCR4 expression was analyzed through immunohistochemistry. Of 26 patients, 18 were newly diagnosed with a total of 23 lesions, 4 had recurrent with 4 lesions, and 4 underwent a mid-term treatment assessment after 4 cycles of chemotherapy (3 achieved complete response (CR), 1 experienced progressive disease (PD) with a total of 8 lesions). Thirty-five lesions were all clearly detected with favorable contrast by [68Ga]Pentixafor PET/CT (accuracy, 100%), consistent with the results of contrast-enhanced magnetic resonance imaging (CE-MRI). The SUVmax of positive lesions in [68Ga]Pentixafor PET/CT was correlated with tumor size (r = 0.555, P = 0.001). In 21 patients, compared with [18F]FDG PET/CT, [68Ga]Pentixafor PET/CT showed a remarkably higher T/N ratio (21.93 ± 10.77 vs 4.29 ± 2.16, P = 0.000) and detected 5 more lesions in the mid-term treatment assessment of patients (P = 0.026). The CXCR4 expression of CNSL lesions was correlated with SUVmax of [68Ga]Pentixafor PET/CT (r = 0.772, P = 0.000). CXCR4-directed PET/CT using [68Ga]Pentixafor, with excellent tumor-to-background contrast, might be a more promising agent for the detection of lesions in CNSL patients than [18F]FDG PET/CT.

More scanning, but not zooming, is associated with diagnostic accuracy in evaluating digital breast pathology slides.

Diagnoses of medical images can invite strikingly diverse strategies for image navigation and visual search. In computed tomography screening for lung nodules, distinct strategies, termed scanning and drilling, relate to both radiologists’ clinical experience and accuracy in lesion detection. Here, we examined associations between search patterns and accuracy for pathologists (N = 92) interpreting a diverse set of breast biopsy images. While changes in depth in volumetric images reveal new structures through movement in the z-plane, in digital pathology changes in depth are associated with increased magnification. Thus, “drilling” in radiology may be more appropriately termed “zooming” in pathology. We monitored eye-movements and navigation through digital pathology slides to derive metrics of how quickly the pathologists moved through XY (scanning) and Z (zooming) space. Prior research on eye-movements in depth has categorized clinicians as either “scanners” or “drillers.” In contrast, we found that there was no reliable association between a clinician's tendency to scan or zoom while examining digital pathology slides. Thus, in the current work we treated scanning and zooming as continuous predictors rather than categorizing as either a “scanner” or “zoomer.” In contrast to prior work in volumetric chest images, we found significant associations between accuracy and scanning rate but not zooming rate. These findings suggest fundamental differences in the relative value of information types and review behaviors across two image formats. Our data suggest that pathologists gather critical information by scanning on a given plane of depth, whereas radiologists drill through depth to interrogate critical features.

Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network

Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a vital role to improve diagnosis, outcome assessment, and treatment planning. The current standard approach of ischemic stroke lesion segmentation is simply thresholding the Computed Tomography Perfusion (CTP) maps, i.e., quantitative feature maps created by summarizing CTP time sequence scans. However, this approach is not precise enough (its Dice similarity score is only around 50%) to be used in practice. Numerous machine learning-based techniques have recently been proposed to improve the accuracy of ischemic stroke lesion segmentation. Although they have achieved remarkable results, they still need to be improved before they can be used in actual practice. This paper presents a novel deep learning-based technique, MutiRes U-Net, for the segmentation of ischemic stroke lesions in CTP maps. MultiRes U-Net is a modified version of the original U-Net that is re-designed to be robust to segment the objects in different scales and unusual appearances. Additionally, in this paper, we propose to enrich the input CTP maps by using their contra-lateral and corresponding Tmax images. We evaluated the proposed method using the ISLES challenge 2018 dataset. As compared to the state-of-the-art methods, the results show an improvement in segmentation task accuracy. The dice similarity score (DSC) was 68%, the Jaccard score was 57.13%, and the mean absolute volume error was 22.62(ml).

Recommendations for a More Organized and Effective Approach to the Early Detection of Pancreatic Cancer From the PRECEDE (Pancreatic Cancer Early Detection) Consortium

Recommendations for a More Organized and Effective Approach to the Early Detection of Pancreatic Cancer From the PRECEDE (Pancreatic Cancer Early Detection) Consortium

Detection accuracy and latency of colorectal lesions with com-puter-aided detection system based on low-bias evaluation

Detection accuracy and latency of colorectal lesions with com-puter-aided detection system based on low-bias evaluation

Colorectal Endoscopic Submucosal Dissection: An Update on Best Practice.

Endoscopic submucosal dissection (ESD) is a method of en-bloc resection of neoplastic colorectal lesions which is less invasive compared to surgical resection. Lesion stratification, architecture recognition and estimation of depth of invasion are crucial for patient selection. Expert endoscopists have integrated a variety of classification systems including Paris, lateral spreading tumor (LST), narrow band imaging (NBI), international colorectal endoscopic (NICE) and Japanese NBI expert team (JNET) in their day-to-day practice to enhance lesion detection accuracy. Major societies recommend ESD for LST-non granular (NG), Kudo-VI type, large depressed and protruded colonic lesions with shallow submucosal invasion. Chance of submucosal invasion enhances with increased depth as well as tumor location and size. In comparison to endoscopic mucosal resection (EMR), ESD has a lowerl recurrence rate and higher curative resection rate, making it superior for larger colonic lesions management. Major complications such as bleeding and perforation could be seen in up to 11% and 16% of patients, respectively. In major Western countries, performing ESD is challenging due to limited number of expert providers, lack of insurance coverage, and unique patient characteristics such as higher BMI and higher percentage of previously manipulated lesions.

A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy.

Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI.

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI. We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix. Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790. We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists.

The Role Of High Resolution Ultrasonography In Evaluation Of Post Traumatic Peripheral Nerve Injury

Background: With the development of scientific technology, applications of high-resolution ultrasound have rapidly developed in biomedical fields. High-resolution sonography clearly depicts peripheral nerve size spaces occupying lesions and anatomic variants along the entire length of the normative nerve Aim; Assessment of efficacy of ultrasound in evaluation of degree of traumatic nerve injury methods This prospective study was carried out during the period from June 2019 to April 2020 and included a hundred participants. All patients were referred to the radiology department of Benha university from the emergency department and outpatient clinic of surgery department of Benha university hospital. Results: diagnosis and characterization of turmeric peripheral nerve injury using ultrasound Conclusion; US can be used to facilitate the preoperative diagnosis and planning of the optimal treatment strategy as it can permit tracing the injured nerve at the lesion site and the neuroma’s extension. Finally, US has high accuracy for detection, diagnosis, assessment and treatment planning of traumatic peripheral nerve lesions.

Preoperative Staging in Breast Cancer: Intraindividual Comparison of Unenhanced MRI Combined With Digital Breast Tomosynthesis and Dynamic Contrast Enhanced-MRI.

ObjectivesTo evaluate the accuracy in lesion detection and size assessment of Unenhanced Magnetic Resonance Imaging combined with Digital Breast Tomosynthesis (UE-MRI+DBT) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), in women with known breast cancer.MethodsA retrospective analysis was performed on 84 patients with histological diagnosis of breast cancer, who underwent MRI on a 3T scanner and DBT over 2018-2019, in our Institution. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, reviewed DCE-MRI and UE-MRI (including DWI and T2-w) + DBT images in separate reading sections, unaware of the final histological examination. DCE-MRI and UE-MRI+DBT sensitivity, positive predictive value (PPV) and accuracy were calculated, using histology as the gold standard. Spearman correlation and regression analyses were performed to evaluate lesion size agreement between DCE-MRI vs Histology, UE-MRI+DBT vs Histology, and DCE-MRI vs UE-MRI+DBT. Inter-reader agreement was evaluated using Cohen’s κ coefficient. McNemar test was used to identify differences in terms of detection rate between the two methodological approaches. Spearman’s correlation analysis was also performed to evaluate the correlation between ADC values and histological features.Results109 lesions were confirmed on histological examination. DCE-MRI showed high sensitivity (100% Reader 1, 98% Reader 2), good PPV (89% Reader 1, 90% Reader 2) and accuracy (90% for both readers). UE-MRI+DBT showed 97% sensitivity, 91% PPV and 92% accuracy, for both readers. Lesion size Spearman coefficient were 0.94 (Reader 1) and 0.91 (Reader 2) for DCE-MRI vs Histology; 0.91 (Reader 1) and 0.90 (Reader 2) for UE-MRI+DBT vs Histology (p-value <0.001). DCE-MRI vs UE-MRI+DBT regression coefficient was 0.96 for Reader 1 and 0.94 for Reader 2. Inter-reader agreement was 0.79 for DCE-MRI and 0.94 for UE-MRI+DBT. McNemar test did not show a statistically significant difference between DCE-MRI and UE-MRI+DBT (McNemar test p-value >0.05). Spearman analyses showed an inverse correlation between ADC values and histological grade (p-value <0.001).ConclusionsDCE-MRI was the most sensitive imaging technique in breast cancer preoperative staging. However, UE-MRI+DBT demonstrated good sensitivity and accuracy in lesion detection and tumor size assessment. Thus, UE-MRI could be a valid alternative when patients have already performed DBT.

Narrative review of epilepsy: getting the most out of your neuroimaging.

Neuroimaging represents an important step in the evaluation of pediatric epilepsy. The crucial role of brain imaging in the diagnosis, follow-up and presurgical assessment of patients with epilepsy is noted and has to be familiar to all neuroradiologists and trainees approaching pediatric brain imaging. Morphological qualitative imaging shows the majority of cerebral lesions/alterations underlying focal epilepsy and can highlight some features which are useful in the differential diagnosis of the different types of epilepsy. Recent advances in MRI acquisitions including diffusion-weighted imaging (DWI), post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection during the last decades. Functional MRI (fMRI) can be really useful and helps to identify cortical eloquent areas that are essential for language, motor function, and memory, and diffusion tensor imaging (DTI) can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. Also positron emission tomography (PET), single photon emission computed tomography (SPECT), simultaneous electroencephalogram (EEG) and fMRI, and electrical and magnetic source imaging can be used to assess the exact localization of epileptic foci and help in the design of intracranial EEG recording strategies. The main role of these “hybrid” techniques is to obtain quantitative and qualitative informations, a necessary step to evaluate and demonstrate the complex relationship between abnormal structural and functional data and to manage a “patient-tailored” surgical approach in epileptic patients.

Culprit Lesion Detection in Patients Presenting With Non-ST Elevation Acute Coronary Syndrome and Multivessel Disease

Background/purposeIdentification of the culprit lesion in patients with non-ST elevation acute coronary syndrome (NSTE-ACS) allows appropriate coronary revascularization but may be unclear in patients with multivessel coronary disease (MVD). Therefore, we investigated the rate of culprit lesion identification during coronary angiography in NSTE-ACS and multivessel disease. Methods/materialsConsecutive patients presenting with NSTE-ACS and MVD, between January 2012 and December 2016 were evaluated. Coronary angiograms, intravascular imaging, and ECGs were analyzed for culprit lesion identification. Long-term clinical outcomes in terms of major adverse cardiac events (MACE) and mortality were reported in patients with or without culprit identification. ResultsA total of 1107 patients with NSTE-ACS and MVD were included in the analysis, 310 (28.0%) with unstable angina and 797 (72.0%) with non-ST elevation myocardial infarction. The culprit lesion was angiographically identified in 952 (86.0%) patients, while no clear culprit lesion was found in 155 (14.0%) patients. ECG analysis allowed to predict the location of the culprit vessel with low sensitivity (range 28.4%–36.7%) and high specificity (range 90.6%–96.5%). Higher lesion complexity was associated with inability to identify the culprit. Intravascular imaging was applied in 55 patients and helped to identify the culprit lesion in 53 patients (96.4%). There was no difference in all-cause mortality (21.4% vs. 25.8%, p = 0.24) and MACE (39.2% vs. 47.6%, p = 0.07) between the cohorts with or without culprit lesion identification by angiography. ConclusionsThe culprit lesion appeared unclear by coronary angiography in >10% of patients with NSTE-ACS and MVD. Complementary invasive imaging substantially enhanced the diagnostic accuracy of culprit lesion detection.