Accuracy For Detection Of Lesions Research Articles

Predictive Analysis of Breast Cancer from Full-Field Digital Mammography Images using Residual Network

Breast cancer has been a significant contributor to cancer-related mortality, but advancements in early detection through regular mammography and improvements in treatment modalities have contributed to declining mortality rates in several regions. This study presents a novel approach to cancer diagnosis utilizing Full-Field Digital Mammography images through predictive analysis methods. By using predictive analytic techniques and mammography images, this study offers a novel way to cancer detection. The research involves the application of deep learning techniques to extract valuable insights from cancer images captured by mammography devices. The CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) dataset including images from patients with varying types and stages of cancer, is collected and pre-processed to ensure uniformity and quality. Relevant features, including color, texture, and shape characteristics, are extracted, and a rigorous feature selection process is employed to identify discriminative markers. The Residual Network (ResNet) model is selected and trained on the dataset, with a focus on classification accuracy and robust predictive performance. Validation metrics, such as accuracy, IoU (Intersection over Union) score, dice score, and ROC (Receiver Operating Characteristic) curve are employed to evaluate the model’s efficiency. After analysis, the proposed method had the best degree of mass lesion detection accuracy, at 99.24%. This research contributes to the advancement of non-invasive and efficient diagnostic tools, potentially enhancing early detection and intervention in cancer patients. The proposed method not only demonstrates promising results in terms of diagnostic accuracy but also emphasizes interpretability, seamless integration into clinical workflows, and adherence to ethical standards.

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Timing offset calibration for TOF PET using stationary line source scans at multiple positions

Background. Accurate timing offset calibration is crucial for time-of-flight (TOF) positron emission tomography (PET) to mitigate image artifacts and improve quantitative accuracy. However, existing methods are often time-consuming, complex, or costly.Objective. This paper presents a method for TOF PET timing offset calibration that eliminates the need for costly equipment, phantoms, short-half-life sources, and precise source positioning.Approach. We estimate channel timing offsets using stationary scans of a68Ge line source, typically used for routine quality control, at a minimum of three non-coplanar positions, with each position scanned for two minutes. The line source positions are accurately determined by applying a simple algorithm to their reconstructed images, allowing precise calculation of arrival time differences. Channel timing offsets are estimated by solving a least squares problem. This method is assessed through analyses of phantoms and patient images using a RAYSOLUTION DigitMI 930 scanner.Main results. The estimated timing offsets ranged from -500 ps to 500 ps across all channels. Calibration with a minimum of three scanned positions was sufficient to correct these offsets, achieving less than a 1% discrepancy across various metrics of the image quality (IQ) phantom compared to 12 positions. This calibration significantly reduced edge artifacts in TOF reconstruction of both phantoms and patients. Furthermore, the IQ phantom displayed a 14% increase in average contrast recovery, a 61% reduction in average background variability across all spheres, and a 90% reduction in average residual error. Consistent with the phantom results, patient data revealed enhancements in maximum standardized uptake values (SUVmax) from 14% to 55% for lesions measuring 6 mm to 14 mm. The calibration also improved lesion-to-background contrast and eliminated artifacts caused by the spillover effect of the kidneys and bladder.Significance. The proposed method is fast, user-friendly, and cost-effective, effectively improving lesion detection and diagnostic accuracy.

Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.

Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.

Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans

IntroductionCone-beam computed tomography (CBCT) is widely used to detect jaw lesions, although CBCT interpretation is time-consuming and challenging. Artificial intelligence for CBCT segmentation may improve lesion detection accuracy. However, consistent automated lesion detection remains difficult, especially with limited training data. This study aimed to assess the applicability of pretrained transformer-based architectures for semantic segmentation of CBCT volumes when applied to periapical lesion detection. MethodsCBCT volumes (n = 138) were collected and annotated by expert clinicians using 5 labels – "lesion," "restorative material," "bone," "tooth structure," and "background." U-Net (convolutional neural network-based) and Swin-UNETR (transformer-based) models, pretrained (Swin-UNETR-PRETRAIN), and from scratch (Swin-UNETR-SCRATCH), were trained with subsets of the annotated CBCTs. These models were then evaluated for semantic segmentation performance using the Sørensen–Dice coefficient (DICE), lesion detection performance using sensitivity and specificity, and training sample size requirements by comparing models trained with 20, 40, 60, or 103 samples. ResultsTrained with 103 samples, Swin-UNETR-PRETRAIN achieved a DICE of 0.8512 for "lesion," 0.8282 for "restorative materials," 0.9178 for "bone," 0.9029 for "tooth structure," and 0.9901 for "background." “Lesion” DICE was statistically similar between Swin-UNETR-PRETRAIN trained with 103 and 60 images (P > .05), with the latter achieving 1.00 sensitivity and 0.94 specificity in lesion detection. With small training sets, Swin-UNETR-PRETRAIN outperformed Swin-UNETR-SCRATCH in DICE over all labels (P < .001 [n = 20], P < .001 [n = 40]), and U-Net in lesion detection specificity (P = .006 [n = 20], P = .031 [n = 40]). ConclusionsTransformer-based Swin-UNETR architectures allowed for excellent semantic segmentation and periapical lesion detection. Pretrained, it may provide an alternative with smaller training datasets compared to classic U-Net architectures.

Lumbar disc herniation diagnosis using deep learning on MRI

ObjectiveIn recent years, artificial intelligence deep learning has gradually become a research hotspot in diagnosing and treating cervical and lumbar degenerative diseases, represented by cervical spondylosis and lumbar disc herniation. MethodsRetrospective inclusion of 590 patients with lumbar disc herniation, randomly divided into a training group (n = 456) and a testing group (n = 134), labeled with intervertebral disc herniation areas on lumbar MR images (1157 in the training group and 395 in the testing group). Construct a deep learning model based on Unet to segment and detect intervertebral disc herniation areas in sagittal and transverse planes and evaluate the model's lesion detection efficiency and accuracy in lesion segmentation. ResultsThe model has good detection performance on the test group data. The F1 values in the sagittal and transverse positions are 0.971 (95% CI: 0.951–0.987) and 0.903 (95% CI: 0.870–0.935). In the segmentation of prominent areas, the Dice values in the sagittal and transverse planes are 0.774 ± 0.193 and 0.647 ± 0.261 (2D plane), 0.777 ± 0.148 and 0.655 ± 0.180 (3D plane), respectively. ConclusionThe deep learning model based on U-net can accurately identify and segment the intervertebral disc herniation area in lumbar MR images, which helps to assist clinical diagnosis.

RefineDet with Improved Attention Block for Chest X-ray Image Lesion Detection

In recent years, artificial intelligence technology has been widely used to assist radiologists in diagnosing and analyzing medical images. The use of artificial intelligence technology can well assist doctors in the localization of lesions. However, the mainstream target detection models at this stage are difficult to be practically applied in medical systems because of factors such as the use of large backbone networks and high input resolution, which leads to low model accuracy and high consumption of computational resources. In this paper, we propose a fast detection speed and high accuracy of the lung lesion detection network IAB- RefineDet. By improving the channel and spatial attention mechanisms and introducing the improved attention module into RefineDet, the lesion detection accuracy is dramatically improved without significantly increasing the number of parameters. We conduct extensive experiments on VinDr-CXR, the world's largest publicly available chest radiograph detection dataset, and comparative experiments with existing mainstream target detection models. The experimental results show that IAB-RefineDet achieves a mAP of 16.23%, and the lesion detection performance is significantly better than mainstream deep learning models.

ENHANCED DETECTION OF CANCER LESIONS USING CONVOLUTIONAL NEURAL NETWORKS AND FEATURE FUSION

Breast cancer remains a significant global health concern, with early detection crucial for effective treatment and improved patient outcomes. Traditional methods of detecting breast cancer lesions, such as mammography and ultrasound, often rely on subjective interpretation and may lack sensitivity. Convolutional Neural Networks (CNNs) have shown promise in medical imaging analysis due to their ability to automatically extract features from images and classify abnormalities. However, improving the detection accuracy of breast cancer lesions using CNNs remains a challenge. Existing CNN-based approaches for breast cancer lesion detection may suffer from limited sensitivity and specificity, leading to missed diagnoses or false positives. Additionally, extracting discriminative features from medical images with varying resolutions and noise levels presents a significant challenge. In this study, we propose an enhanced detection framework for breast cancer lesions using CNNs and feature fusion. Our method incorporates multiple CNN architectures, including DenseNet, ResNet, and Inception, to capture diverse image features. Furthermore, we employ feature fusion techniques to integrate complementary information from different CNN models. By combining features at multiple levels, our approach aims to improve the robustness and discriminative power of the detection model. Experimental results on a large dataset of breast cancer images demonstrate the effectiveness of our proposed method. The proposed framework achieves a sensitivity of 0.95 and a specificity of 0.92, outperforming state-of-the-art methods by a significant margin. Moreover, the proposed method exhibits an area under the receiver operating characteristic curve (AUC) of 0.97, indicating its superior discriminative ability in distinguishing between malignant and benign lesions. The computational efficiency of the proposed approach is also shows, with an average inference time of 0.2 seconds per image.

A SWOT ANALYSIS OF BREAST CANCER DIAGNOSIS IN DIGITAL MAMMOGRAPHY USING DEEP CONVOLUTIONAL NEURAL NETWORK

Breast cancer is a significant global health challenge that affects both men and women, leading to cause-specific deaths. Current early screening interventions, such as digital mammography (DM), are susceptible to high false-positives and false-negatives. This paper explores the potential of convolutional neural network (CNN), a form of artificial intelligence (AI), to support screening mammography with the aim to enhance accuracy in lesion detection, image classification and diagnostic prediction. Because the adoption of AI in cancer diagnosis is still in its infancy, the objective of this paper is to provide insight into the benefits and limitations of deep learning-based approaches to detect and diagnose cancer. An analysis of the implementation of CNN in AI-screening mammography models was conducted, using the SWOT strategic analysis tool. Internal strengths that improve the predictive accuracy of CNN include transfer learning and data augmentation, whereas the internal weaknesses include a lack of data standardisation and reproducibility. External opportunities consist of increased sensitivity in differentiating between microcalcifications and non-tumorous structures, improved predictive diagnosis and reduced workload. Nevertheless, integration within clinical settings must also consider the external threats of breaching patient privacy, automation biases and the role of clinical judgement.

Breast Cancer and Lesion Detection using Machine Learning : A Review

Breast Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—D cancer remains one of the most common cancers among women globally, necessitating early detection to improve prognosis and survival rates. Recent advancements in machine learning (ML) have shown promise in enhancing the accuracy and efficiency of breast cancer and lesion detection. This review paper discusses the current methodologies, challenges, and future directions of ML applications in the detection and diagnosis of breast cancer. We analyze various ML algorithms used for analyzing mammograms, ultrasound, and MRI data, evaluating their effectiveness and applicability in clinical settings.

Crow search algorithm with deep transfer learning driven skin lesion detection on dermoscopic images

The early diagnosis of skin cancer is of paramount importance for effective patient treatment. Dermoscopy, a non-surgical technique, utilizes precise equipment to examine the skin and plays a crucial role in identifying specific features and patterns that may indicate the presence of skin carcinoma. In recent times, machine learning (ML) methods have been developed to recognize and classify dermoscopic images as either malignant or benign. Deep learning (DL) systems, including Convolutional Neural Networks (CNNs), as well as various ML models like Random Forest (RF) classifiers and Support Vector Machine (SVM), are employed to extract relevant features from these images. This study introduces the Crow Search Algorithm with Deep Transfer Learning Driven Skin Lesion Detection on Dermoscopic Images (CSADTL-SLD) technique. The CSADTL-SLD method starts with the application of a median filter (MF) to remove noise from the images and utilizes the GoogleNet model for feature extraction. GoogleNet is well-regarded for its capacity to capture intricate and meaningful patterns within the data, which are essential for accurate lesion characterization. Furthermore, the CSADTL-SLD technique applies the Crow Search Algorithm (CSA) for parameter tuning of the GoogleNet model. After feature selection, the system employs the MLP classification model for precise lesion categorization. The comprehensive results of this research demonstrate the superiority of the CSADTL-SLD algorithm, showing significant enhancements in skin lesion detection accuracy and robustness when compared to existing methods. This approach holds promise as an effective solution for automating the detection and classification of skin lesions in dermoscopic images.

Phantom study and clinical application of total-body 18F-FDG PET/CT imaging: How to use small voxel imaging better?

BackgroundConventional PET/CT imaging reconstruction is typically performed using voxel size of 3.0–4.0 mm in three axes. It is hypothesized that a smaller voxel sizes could improve the accuracy of small lesion detection. This study aims to explore the advantages and conditions of small voxel imaging on clinical application.MethodsBoth NEMA IQ phantom and 30 patients with an injected dose of 3.7 MBq/kg were scanned using a total-body PET/CT (uEXPLORER). Images were reconstructed using matrices of 192 × 192, 512 × 512, and 1024 × 1024 with scanning duration of 3 min, 5 min, 8 min, and 10 min, respectively.ResultsIn the phantom study, the contrast recovery coefficient reached the maximum in matrix group of 512 × 512, and background variability increased as voxel size decreased. In the clinical study, SUVmax, SD, and TLR increased, while SNR decreased as the voxel size decreased. When the scanning duration increased, SNR increased, while SUVmax, SD, and TLR decreased. The SUVmean was more reluctant to the changes in imaging matrix and scanning duration. The mean subjective scores for all 512 × 512 groups and 1024 × 1024 groups (scanning duration ≥ 8 min) were over three points. One false-positive lesion was found in groups of 512 × 512 with scanning duration of 3 min, 1024 × 1024 with 3 min and 5 min, respectively. Meanwhile, the false-negative lesions found in group of 192 × 192 with duration of 3 min and 5 min, 512 × 512 with 3 min and 1024 × 1024 with 3 min and 5 min were 5, 4, 1, 4, and 1, respectively. The reconstruction time and storage space occupation were significantly increased as the imaging matrix increased.ConclusionsPET/CT imaging with smaller voxel can improve SUVmax and TLR of lesions, which is advantageous for the diagnosis of small or hypometabolic lesions if with sufficient counts. With an 18F-FDG injection dose of 3.7 MBq/kg, uEXPLORER PET/CT imaging using matrix of 512 × 512 with 5 min or 1024 × 1024 with 8 min can meet the image requirements for clinical use.

Improving small sample medical image segmentation using CBAM: Insights from two datasets

Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images.

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.

A polymeric 1H/19F dual-modal MRI contrast agent with a snowman-like Janus nanostructure.

Magnetic resonance imaging (MRI) has emerged as a pivotal tool in contemporary medical diagnostics, offering non-invasive and high-resolution visualization of internal structures. Contrast agents are essential for enhancing MRI resolution, accurate lesion detection, and early pathology identification. While gadolinium-based contrast agents are widely used in clinics, safety concerns have prompted exploration of metal-free alternatives, including fluorine and nitroxide radical-based MRI contrast agents. Fluorine-containing compounds exhibit excellent MRI capabilities, with 19F MRI providing enhanced resolution and quantitative assessment. Nitroxide radicals, such as PROXYL and TEMPO, offer paramagnetic properties for MRI contrast. Despite their versatility, nitroxide radicals suffer from lower relaxivity values (r1) compared to gadolinium. Dual-modal imaging, combining 1H and 19F MRI, has gained prominence for its comprehensive insights into biological processes and disease states. However, existing dual-modal agents predominantly utilize gadolinium-organic ligands without incorporating nitroxide radicals. Here, we introduce a novel dual-modal MRI contrast agent (J-CA) featuring a Janus asymmetric nanostructure synthesized via seeded emulsion polymerization and post-modification. J-CA demonstrates excellent in vitro and in vivo performance in both 19F and 1H MRI, with a T2 relaxation time of 5 ms and an r1 value of 0.31 mM-1 s-1, ensuring dual-modal imaging capability. Moreover, J-CA exhibits superior biocompatibility and organ targeting, making it a promising candidate for precise lesion imaging and disease diagnosis. This work introduces a new avenue for metal-free dual-modal MRI, addressing safety concerns associated with traditional contrast agents.

Potential Usefulness a Coronal View using an Automated Breast Ultrasound System in Detecting Breast Lesions.

An automated breast ultrasound system (ABUS) combined with screening mammography has increased cancer detection rates; however, supplemental ABUS use has increased recall rates. In this study, we aimed to identify an accurate and efficient method of ABUS interpretation and evaluate the potential usefulness of its coronal view versus the conventional transverse view. This retrospective observer study included comprised 114 ABUS cases (40 normal, 35 benign, 39 malignant). Ten physicians from multiple institutions interpreted the anonymized coronal and transverse views independently. The observers scored their confidence in the lesion detection for each case using a continuous scale and recorded reading times for each coronal and transverse view interpretation. Free-response receiver operating characteristic analysis was employed to compare detection accuracies between views; a paired t-test was used to compare the average reading times. Detection accuracy did not differ significantly between the coronal and transverse views (figure of merit=0.740 and 0.745, respectively; p = 0.72). However, the average reading time for the coronal view was significantly shorter than that for the transverse view (149.7 vs. 200.3 seconds per case, p = 0.003). The coronal view obtained with the ABUS was useful for interpretation and associated with significantly shorter reading times compared with the conventional transverse view while maintaining breast lesion detection accuracy.

SC-UneXt: Nested UNeXt Architecture based on Medical Image Segmentation

UNet and its various variants are commonly used methods in medical image segmentation tasks; however, many network parameters, complex calculations, and slow usage are problems that need to be overcome. These problems hinder the specific application of fast image segmentation in real-time tasks. At the same time, the lesion area has problems such as small size, irregular shape, and blurred edges, which makes the network feature extraction difficult and the segmentation accuracy needs to be improved. At the same time, medical image segmentation provides a variety of effective methods for the accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, so integrating rich multi-scale features can improve segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are crucial to segmentation results. To this end, we propose an image segmentation method (SC -UNe X t) based on edge feature extraction and multi-scale feature fusion of convolutional multi-layer perceptron (MLP). The network is a deeply supervised encoder-decoder network, in which the encoder and decoder pass through a series of nested, multiple jump paths to reduce the semantic gap between the feature maps of the encoder and decoder sub-networks.; Multi - scale feature fusion is introduced based on the UNe Finally, we evaluate our model approach on the LIDC dataset public dataset. Experiments have proven the effectiveness of this method. Our model's similarity coefficient and intersection ratio reached 86.44% and 90.86% respectively. Compared with UNet and UNe X t, the network proposed in this article has improved in accuracy, intersection ratio of real values and predicted values, similarity coefficient, and segmentation effect.

Chromocolposcopy with fluorescein sodium for detection of premalignant and malignant lesions of the cervix: A cross-sectional study.

The aim of the study was to explore the utility of fluorescein sodium (FNa) as a contrast agent for colposcopy to detect premalignant and malignant lesions of cervix. The primary objective was to determine and compare the percentage detection of premalignant and malignant lesions of FNa and acetic acid (AA) positive areas. This study included 120 screen positive women who underwent colposcopy using both 3% AA and FNa (0.06%). Observations for FNa staining were made under blue filter and directed biopsies were taken from acetowhite and fluorescent green areas. Benign lesions were considered as disease-negative and low grade squamous intraepithelial lesions (LSIL), high grade SIL (HSIL), and invasive cancer were considered as disease-positive. Correlation between histopathology and FNa and AA was determined by Kappa statistics. The mean age was 39.59 ± 10.73 years and median parity was 2. Out of 120 patients, 57 had benign lesions, 18 had LSIL, 33 had HSIL and 12 had invasive carcinomas. Sensitivity was 98.41% versus 64.91% respectively and specificity was 85.71% versus 35.09% respectively with FNa and AA. Diagnostic accuracy of FNa and AA was 82.50% versus 61.60%. There was good agreement between FNa staining and final histopathology and fair agreement between AA application and HPE (κ = 0.643 vs 0.213, P < 0.001). Using FNa as a contrast agent during colposcopy results in greater accuracy for detection of premalignant and malignant lesions of the cervix as compared to AA.

Comparison of conventional MR arthrography and 3D volumetric MR arthrography in detection of cartilage defects accompanying glenoid labrum pathologies.

In this study, we aimed to compare conventional and T1-weighted volumetric magnetic resonance arthrography (MRA) in the diagnosis and grading of glenoid cartilage defects that accompany labral pathologies. A total of 79 patients who were prediagnosed with labrum pathologies based on shoulder magnetic resonance imaging (MRI) had MRA and CTA between December 2021 and May 2022. CTA was regarded as reference standard. CTA images were examined by a radiologist experienced in musculoskeletal radiology, and MRA images were examined by two radiologists independently to determine presence, grade, and localization of any glenoid cartilage defect, if present. Sensitivity, specificity, and accuracy were calculated separately for conventional and T1-weighted volumetric MRA. In addition, at the last stage, two observers examined all MRAs together, and the presence of a cartilage defect was decided by consensus, and the overall sensitivity, specificity, and accuracy were calculated. Cartilage defect was detected on CTAs of 48 (60.75%) cases of among 79 patients with labrum pathology. The sensitivity, specificity, and accuracy of conventional MRA for two examiners were 17-19%, 100-100%, and 49-51%, respectively, while those values were 67-65%, 92-97%, and 84-77%, respectively, for T1-weighted volumetric MRA. Inter-examiner agreement was excellent for diagnosis of cartilage defects on all MRAs. The overall sensitivity, specificity, and accuracy for detection of glenoid cartilage lesions by MRA were 69%, 97%, and 80%, respectively. T1-weighted volumetric MRA seems to demonstrate cartilage defects accompanied with labrum pathologies accurately with high sensitivity, specificity, and excellent inter-examiner agreement.

The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.

Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are valuable tools for diagnosing acute ischemic stroke (AIS). It is essential to obtain high-quality CTP and CTA images in short time. This study aimed to evaluate the image quality and diagnostic performance of brain CTP and CTA images generated from CTP reconstructed by a deep learning image reconstruction (DLIR) algorithm on patients with AIS. The study prospectively enrolled 54 patients with suspected AIS undergoing non-contrast CT and CTP within 24 hours. CTP datasets were reconstructed with three levels of adaptive statistical iterative reconstruction-Veo algorithm [ASIR-V 0% with filtered back projection (FBP), ASIR-V 40%, and ASIR-V 80%] and three levels of DLIR, including low (DLIR-L), medium (DLIR-M), and high (DLIR-H). CTA images were generated using the CTP datasets at the peak arterial phase. Objective parameters including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise reduction rate. Subjective evaluation was assessed according to Abels scoring system. Perfusion parameters and detection accuracy for infarction core lesions were evaluated. The objective and subjective image quality of CTA images were also evaluated. All reconstructions produced similar CT values (P>0.05). With the increase of ASIR-V and DLIR reconstruction strength, image noise decreased, while SNR and CNR increased for CTP images, especially in white matter. DLIR-H, DLIR-M, and ASIR-V80% yielded higher subjective scores than did ASIR-V40% and FBP. DLIR-H provided the highest noise reduction rate and detection accuracy. No significant difference was found in conventional parameters, the volume of infarct core, or ischemic penumbra among the 6 groups (P>0.05). The objective evaluation of reconstructed CTA images was comparable in DLIR-H, DLIR-M, and ASIR-V80% (P>0.05). The subjective scores of the DLIR-H and DLIR-M images were higher than those of the other groups, especially ASIR-V40% and FBP (P<0.05). Compared with FBP and ASIR-V40%, DLIR-H, DLIR-M, and ASIR-V80% improved the overall image quality of CTP and CTA images to varying degrees. Furthermore, DLIR-H and DLIR-M showed the best performance. DLIR-H is the best choice in diagnosing AIS with improved detection accuracy for cerebral infarction. Reconstructing CTA images using CTP datasets could reduce contrast agent and radiation dose.