Tomography Modalities Research Articles

Is Artificial Intelligence the Future of Radiology? Accuracy of ChatGPT in Radiologic Diagnosis of Upper Extremity Bony Pathology.

Artificial intelligence (AI) is a promising tool to aid in diagnostic accuracy and patient communication. Prior literature has shown that ChatGPT answers medical questions and can accurately diagnose surgical conditions. The purpose of this study was to determine the accuracy of ChatGPT 4.0 in evaluating radiologic imaging of common orthopedic upper extremity bony pathologies, including identifying the imaging modality and diagnostic accuracy. Diagnostic imaging was sourced from an open-source radiology database for 6 common upper extremity bony pathologies: distal radius fracture (DRF), metacarpal fracture (MFX), carpometacarpal osteoarthritis (CMC), humerus fracture (HFX), scaphoid fracture (SFX), and scaphoid nonunion (SN). X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) modalities were included. Fifty images were randomly selected from each pathology where possible. Images were uploaded to ChatGPT 4.0 and queried for imaging modality, laterality, and diagnosis. Each image query was completed in a new ChatGPT search tab. Multinomial linear regression was used to identify variations in ChatGPT's diagnostic accuracy across imaging modalities and medical conditions. Overall, ChatGPT provided a diagnosis for 52% of images, with accuracy ranging from 0% to 55%. Diagnostic accuracy was significantly lower for SFX and MFX relative to HFX. ChatGPT was significantly less likely to provide a diagnosis for MRI relative to CT. Diagnostic accuracy ranged from 0% to 40% with regard to imaging modality (x-ray, CT, MRI) though this difference was not statistically significant. ChatGPT's accuracy varied significantly between conditions and imaging modalities, though its iterative learning capabilities suggest potential for future diagnostic utility within hand surgery.

SYRMEP beamline: state of the art, upgrades and future prospects

SYRMEP is the hard X-ray imaging beamline of Elettra synchrotron offering X-ray full-field techniques, micro-computed tomography (microCT) and phase-contrast modality in the energy range 10–40 keV. The beamline operates in a multidisciplinary research context spanning from biomedical applications to botany, from zoology to food technology and cultural heritage, from materials engineering to geology and earth science. Thanks to the flexibility of SYRMEP setup, in situ experiments can be performed as well, novel imaging methods can be developed and implemented in a synergical manner with interested users and collaborators. SYRMEP peculiar wide beam together with the long sample-to-detector distance enables multiscale phase-contrast studies with optimized contrast and spatial resolution on rather large specimens, such as human lung phantoms. This is particularly relevant in view of future clinical lung imaging foreseen in the framework of Elettra 2.0 program. Here, the current beamline features and recent upgrades are illustrated, an overview of the imaging methods routinely offered to SYRMEP users’ community is presented, and the outlook for the new beamline SYRMEP-Life Science (SYRMEP-LS) is reported.

Customizable Lyophilized Agent for Radiotherapy Imaging and TherapY (CLARITY).

Smart radiotherapy biomaterials (SRBs) include seed and liquid biomaterials designed to be employed as fiducial markers during radiotherapy while also delivering therapeutic drug payloads to enhance treatment outcomes. In this study, we investigate a novel Customizable Lyophilized Agent for Radiotherapy Imaging and TherapY (CLARITY) biomaterial, which can be loaded with immunoadjuvants (anti-CD40 monoclonal antibody or Caflanone (FBL-03G)) at the point of care. The CLARITY biomaterial was investigated in an animal model of pancreatic cancer using C57BL6 mice. Mice were imaged before and at different points of time post-treatment to evaluate the potential of CLARITY biomaterial to provide imaging contrast similar to fiducials. This study also used cadavers to assess CLARITY's potential to provide imaging contrast in humans. Results showed imaging contrast from computed tomography (CT) and magnetic resonance imaging (MRI) modalities for up to 30 days post-treatment, demonstrating potential for use as fiducials. A significant increase in survival (***, p = 0.0006) was observed for mice treated with CLARITY biomaterial loaded with immunoadjuvant for up to 10 weeks post-treatment compared to those without treatment. These initial results demonstrate the potential of CLARITY biomaterial to serve as a smart multifunctional radiotherapy biomaterial and provide the impetus for further development and optimization as a point-of-care technology for combination radiotherapy and immunotherapy.

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images

ObjectiveAccurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade. MethodsBetween March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). ResultsIn total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0–0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA. ConclusionThe application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice. Advances in knowledgePrevious studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research.

Cardiac positron emission tomography and other modalities for coronary artery disease assessment: A snapshot from the medicare data

BackgroundPositron emission tomography (PET) is an important tool for assessing coronary artery disease (CAD), but its widespread utilization is limited due to various factors, including limited local champion availability. This study aims to compare the frequency of PET procedures and their interpreters with other common CAD assessment modalities. MethodsUsing Medicare data, we examined the number of cardiac PET procedures billed and compared them with single-photon emission computed tomography (SPECT), coronary computed tomography angiography (CCTA), stress magnetic resonance imaging (MRI), and stress echocardiography. Healthcare Common Procedure Coding System codes were used to identify procedures. We calculated the total number of PET myocardial perfusion imaging (MPI) procedures, the proportion of PET/CT and myocardial blood flow (MBF) assessments, and the median number of studies read per physician. We also analyzed the trends in the use of different CAD assessment modalities between 2018 and 2022. Descriptive statistics summarized the data. ResultsIn 2022, Medicare billed for 212,106 PET MPI scans. SPECT was six times more frequent (1,343,519), whereas stress echocardiography (201,676) and CCTA (118,734) had similar or lower use. Stress MRI (3,932) was least used. Of the PET MPI scans, 46% were PET/CT, and 39% included MBF measurements. Cardiologists interpreted 86% of PET scans, with a median of 58 studies per reader; 23% interpreted ≤25 studies annually. SPECT had a median of 63 studies per reader, and CCTA, stress MRI, and stress echocardiography had medians of 27, 20, and 24, respectively. PET, CT, and MRI use increased from 2018 to 2022, whereas SPECT and stress echocardiography declined. ConclusionIn the Medicare population, radionuclide perfusion imaging (SPECT and PET) remained the preferred method for assessment of CAD, with SPECT being the most frequently used modality and PET being the second most frequently used modality for this application. However, PET/CT and MBF are underutilized, limiting diagnostic and prognostic capabilities. Efforts to enhance education and awareness of PET's advantages and to address barriers to its wider adoption are essential to maximize its clinical benefits and improve patient outcomes.

Adapting the segment anything model for multi-modal retinal anomaly detection and localization

The fusion of optical coherence tomography (OCT) and fundus modality information can provide a comprehensive diagnosis for retinal artery occlusion (RAO) disease, where OCT provides the cross-sectional examination of the fundus image. Given multi-modal retinal images, an anomaly diagnosis model can discriminate RAO without the need for real diseased samples. Despite this, previous studies have only focused on single-modal diagnosis, because of: 1) the lack of paired modality samples; and 2) the significant imaging differences, which make the fusion difficult with small-scale medical data. In this paper, we describe how we first built a multi-modal RAO dataset including both OCT and fundus modalities, which supports both the anomaly detection and localization tasks with pixel-level annotation. Motivated by the powerful generalization ability of the recent visual foundation model known as the Segment Anything Model (SAM), we adapted it for our task considering the small-scale property of retinal samples. Specifically, a modality-shared decoder with task-specific tokens is introduced to make SAM support the multi-modal image setting, which includes a mask token for the anomaly localization task at the pixel level and a fusion token for the anomaly detection task at the case level. Since SAM has little medical knowledge and lacks the learning of the “normal” concept, it is infeasible to localize RAO anomalies in the zero-shot manner. To integrate expert retinal knowledge while keeping the general segmentation knowledge, general anomaly simulation for both modalities and a low-level prompt-tuning strategy are introduced. The experiments conducted in this study show that the adapted model can surpass the state-of-the-art model by a large margin. This study sets the first benchmark for the multi-modal anomaly detection and localization tasks in the medical community. The code is available at https://github.com/Jingtao-Li-CVer/MMRAD.

Imaging Features and Recent Advances in Lymph Node Tuberculosis

AbstractThe burden of tuberculosis in our country persists, with diverse presentations affecting various organ systems, most commonly manifesting as pulmonary tuberculosis followed by nodal tuberculosis. While pulmonary involvement is frequently observed, nodal tuberculosis can present in isolation, commonly affecting cervical lymph nodes. Various imaging modalities play pivotal roles in diagnosis and assessment. Radiographs serve as initial screening modality in thoracic lymphadenopathy. Ultrasound is mostly used in cervical lymphadenopathy. Cases of thoracic and abdominal lymphadenopathy are usually evaluated with computed tomography but magnetic resonance imaging can be a radiation-free alternative. The role of fluorodeoxyglucose-positron emission tomography and newer modalities like shear-wave elastography and contrast-enhanced ultrasound are briefly discussed.

Analyzing computed tomography Modalities for screening pediatric patients for traumatic blunt cerebrovascular injury

BackgroundOptimal screening for BCVI in pediatric trauma patients remains debated. We hypothesized screening with CTAN would decrease the number of duplicate CT scans per patient and increase BCVI detection rate. MethodsLocal BCVI screening institutional protocol changed May 2022 to include Computed Tomography angiography neck (CTAN). We performed a retrospective review of pediatric blunt trauma patients presenting at our Level 1 trauma center between 2019 and 2023. Patients before and after implementation of universal screening were compared for demographic, clinical, radiographic, and outcome data. ResultsSix-hundred-eight patients were included with 368 before and 240 after the protocol change. Screening with CTAN decreased the number of duplicate neck scans (5.7%vs.2.1 ​%,p ​= ​0.03) and increased BCVI detection rate (0.27%v.2.5 ​%,p ​= ​0.01). Of the seven patients diagnosed with BCVI 2019–2023, no patients suffered any stroke-related morbidity. ConclusionUniversal screening for BCVI in pediatric patients with CTAN resulted in fewer scans and an increased BCVI detection rate.

Deep-learning-based Attenuation Correction for 68Ga-DOTATATE Whole-body PET Imaging: A Dual-center Clinical Study.

Attenuation correction is a critical phenomenon in quantitative positron emission tomography (PET) imaging with its own special challenges. However, computed tomography (CT) modality which is used for attenuation correction and anatomical localization increases patient radiation dose. This study was aimed to develop a deep learning model for attenuation correction of whole-body 68Ga-DOTATATE PET images. Non-attenuation-corrected and computed tomography-based attenuation-corrected (CTAC) whole-body 68Ga-DOTATATE PET images of 118 patients from two different imaging centers were used. We implemented a residual deep learning model using the NiftyNet framework. The model was trained four times and evaluated six times using the test data from the centers. The quality of the synthesized PET images was compared with the PET-CTAC images using different evaluation metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean square error (MSE), and root mean square error (RMSE). Quantitative analysis of four network training sessions and six evaluations revealed the highest and lowest PSNR values as (52.86±6.6) and (47.96±5.09), respectively. Similarly, the highest and lowest SSIM values were obtained (0.99±0.003) and (0.97±0.01), respectively. Additionally, the highest and lowest RMSE and MSE values fell within the ranges of (0.0117±0.003), (0.0015±0.000103), and (0.01072±0.002), (0.000121±5.07xe-5), respectively. The study found that using datasets from the same center resulted in the highest PSNR, while using datasets from different centers led to lower PSNR and SSIM values. In addition, scenarios involving datasets from both centers achieved the best SSIM and the lowest MSE and RMSE. Acceptable accuracy of attenuation correction on 68Ga-DOTATATE PET images using a deep learning model could potentially eliminate the need for additional X-ray imaging modalities, thereby imposing a high radiation dose on the patient.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge

Background and purposeTo promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. Materials and methodsThe challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. ResultsWhile 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. ConclusionThis challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.

Bimetallic porphyrin PET radiotracers for Low-Dose MRI contrast enhancement

In medical imaging, concerns over the risks associated with imaging agents have been consistently raised by doctors and patients, and ongoing efforts are being made to cautiously explore safer and more efficient compounds. There is continued interest in combining the well-known imaging modalities of positron emission tomography (PET) and magnetic resonance imaging (MRI). However, there has not been reported a single-molecule probe offering dual-modal imaging without performance degradation. We herein present a potential solution wherein the compound, PGaLGd, incorporates radioactive Ga(III) for PET imaging whilst simultaneously being able to exhibit MRI contrast through a coordinated Gd(III) ion but at low concentrations/doses. Besides elucidating MRI enhancing mechanisms through computational chemistry techniques, our compound has proven to be a successful dual-imaging agent for both PET and MRI in mice. Additionally, we conducted comprehensive in vitro and in vivo studies, assessing biosafety and photodynamic therapeutic potential across various cell lines and organoids. We have placed importance on interlinking structures, coordinated metals, adjacent ligands, and frontier molecular orbitals in our probe design to enhance water relaxation ability and move towards the design of next-generation low-dose imaging agents.

[18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography and conventional imaging modalities in the diagnosis of diabetic foot osteomyelitis: a meta-analysis

This meta-analysis aimed to evaluate the comparative diagnostic efficacy of [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and conventional imaging, MRI, and white blood cell (WBC) scintigraphy in detecting foot osteomyelitis among diabetic patients. An exhaustive search was conducted within the PubMed and Embase databases to identify publications available up until February 2024. Studies were included if they evaluated the diagnostic efficacy of [18F]FDG PET or the comparative diagnostic performance between PET and (MRI or WBC scintigraphy). Two researchers independently assessed the quality of the included studies, utilizing the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool. Nine retrospective or prospective studies involving 605 patients were included in the meta-analysis. For [18F]FDG PET, the overall sensitivity was 0.83(95% CI: 0.69-0.94), while the overall specificity was 0.92(95% CI: 0.86-0.97). In the head-to-head comparison, no significant difference of sensitivity was found between [18F]FDG PET and MRI (0.72 vs. 0.68, P=0.81), as well as between [18F]FDG PET and WBC scintigraphy (0.57 vs. 0.66, P=0.64). In addition, specificity was also found to be no significant difference between [18F]FDG PET and MRI (0.90 vs. 0.82, P=0.27), as well as [18F]FDG PET and WBC scintigraphy (0.81 vs. 0.93, P=0.09). [18F]FDG PET demonstrates similar sensitivity and specificity to MRI and WBC scintigraphy in detecting foot osteomyelitis among diabetic patients. MRI, often cited as a primary choice in guidelines, might be preferred due to its lower cost and lower dose. Further larger sample prospective studies are needed to confirm these findings.

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

AbstractBreast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

MG-TCCA: Tensor Canonical Correlation Analysis across Multiple Groups.

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

A MACHINE LEARNING APPROACH USING STATISTICAL MODELS FOR EARLY DETECTION OF CARDIAC ARREST IN NEWBORN BABIES IN THE CARDIAC INTENSIVE CARE UNIT

A frightening yet common medical emergency is cardiac arrest in infants who have just been delivered. The greatest care and therapy for these infants can only be provided if detected early. Recent studies have aimed to create reliable diagnostic techniques for early diagnosis of neonatal cardiac arrest by finding possible biomarkers and indications of this condition. Echocardiography, computed tomography, and other imaging modalities may aid in the early diagnosis of cardiac arrest. In order to identify cardiac arrest in newborn newborns in the Cardiac Intensive Care Unit (CICU) early on, this project seeks to construct a Cardiac Machine Learning model (CMLM) utilizing statistical methods. Multiple neonate physiological characteristics were used to identify the cardiac arrest occurrences. Predictive models for cardiac arrest were constructed using statistical modeling approaches, including logistic regression and support vector machines. The suggested methodology will be used in the CICU to facilitate the early identification of neonatal cardiac arrest. With a training (Tr) comparison area, the suggested CMLA achieved a delta-p value of 0.912, an FDR value of 0.894, an OR value of 0.076, a prevalence threshold value of 0.859, and a CSI value of 0.842. The suggested CMLA achieved test-specific values of 0.896 for delta-p, 0.878 for FDR, 0.061 for FOR, 0.844 for prevalence threshold, and 0.827 for CSI in a testing (Ts) comparison range. Babies induced cardiac arrest in the neonatal intensive care unit will have a lower risk of death and morbidity as a result.

Towards human-scale magnetic particle imaging: development of the first system with superconductor-based selection coils.

Magnetic Particle Imaging (MPI) is an emerging tomographic modality that allows for precise three-dimensional (3D) mapping of magnetic nanoparticles (MNPs) concentration and distribution. Although significant progress has been made towards improving MPI since its introduction, scaling it up for human applications has proven challenging. High-quality images have been obtained in animal-scale MPI scanners with gradients up to 7 T/m/μ0, however, for MPI systems with bore diameters around 200 mm the gradients generated by electromagnets drop significantly to below 0.5 T/m/μ0. Given the current technological limitations in image reconstruction and the properties of available MNPs, these low gradients inherently impose limitations on improving MPI resolution for higher precision medical imaging. Utilizing superconductors stands out as a promising approach for developing a human-scale MPI system. In this study, we introduce, for the first time, a human-scale amplitude-modulated (AM) MPI system with superconductor-based selection coils. The system achieves an unprecedented magnetic field gradient of up to 2.5 T/m/μ0 within a 200 mm bore diameter, enabling large fields of view of 100 × 130 × 98 mm3 at 2.5 T/m/μ0 for 3D imaging. While obtained spatial resolution is in the order of previous animal-scale AM MPIs, incorporating superconductors for achieving such high gradients in a 200 mm bore diameter marks a major step toward clinical MPI.

Accuracy of Four-Dimensional Computed Tomography and Different Imaging Modalities in Primary Hyperparathyroidism.

Background This study aimed to compare the accuracy of different imaging modalities in the preoperative localization of parathyroid pathology in primary hyperparathyroidism. Methodology This prospective study enrolled 70 patients who were biochemically diagnosed with primary hyperparathyroidism between 2021 and 2022 at our center. Patients underwent scanning using three imaging modalities, namely, Tc99m sestamibi scan (sestamibi), parathyroid ultrasonography, and four-dimensional computed tomography (4DCT). A descriptive analysis was performed to determine and compare the respective localizing sensitivities. Results The most common site of parathyroid adenoma (PA) was the left inferior parathyroid gland, seen in 28 (40%) patients. Three patients had false-positive imaging studies with no parathyroid pathology identified surgically or on histological examination. The median levels of parathyroid hormone decreased significantly (p < 0.001) after the surgery, with a median of 24.3 (1.90-121). Furthermore, 4DCT accomplished a sensitivity of 97.14% for diagnosing the side and 94.03% for overall localization of PA. This sensitivity was superior to the sensitivity of ultrasonography and sestamibi scan to detect the side and quadrant of the adenoma. 4DCT was significantly higher in sensitivity when compared to the combination of ultrasound and sestamibi (p < 0.001). Conclusions 4DCT yielded the highest sensitivity in localizing parathyroid pathology from the imaging modalities studied with the lowest false-negative rate. Using ultrasound with 4DCT could be the most cost-effective combination for detecting primary hyperparathyroidism.

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.

Bochdalek Hernia: A Comprehensive Review of Pathophysiology, Clinical Presentation, Diagnostic Modalities, and Contemporary Management Strategies

Bochdalek hernia, a rare congenital diaphragmatic anomaly, has garnered substantial attention due to its intricate pathophysiological underpinnings and multifaceted clinical implications. This article endeavors to provide an exhaustive analysis of Bochdalek hernia, encompassing its embryological origins, intricate pathophysiology involving the posterolateral diaphragmatic defect, and the subsequent herniation of abdominal contents into the thoracic cavity. A meticulous exploration of the clinical presentation underscores the considerable variation in symptomatology, ranging from asymptomatic cases incidentally detected on imaging, to severe respiratory distress and life-threatening consequences in neonates. The diagnostic armamentarium, including radiographic, ultrasonographic, and tomographic modalities, is scrutinized with a focus on their respective sensitivities and specificities in discerning this anatomical aberration. In light of contemporary advancements, the article elucidates the expanding role of three-dimensional reconstructions and minimally invasive techniques in achieving accurate diagnosis and surgical planning. Furthermore, the management landscape is delineated, encompassing conservative approaches for asymptomatic cases, urgent surgical interventions for acute presentations, and the nuanced perioperative considerations for neonates and pediatric patients. A comprehensive overview of surgical techniques, ranging from traditional open procedures to the evolving realm of robotic-assisted and laparoscopic repairs, is provided, with emphasis on individualized strategies tailored to patient-specific factors. In summation, this article offers a comprehensive and sophisticated exploration of Bochdalek hernia, amalgamating embryology, pathophysiology, clinical presentation, diagnostic modalities, and contemporary management paradigms. It is envisaged that this review will serve as a valuable resource for medical practitioners, surgeons, and researchers, fostering an enriched understanding of this intricate condition and facilitating informed decision-making in its management.

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This study aimed to develop a dual-stream attention neural network to classify individuals with MCI based on multi-modal retinal images. Our approach incorporated a cross-modality fusion technique, a variable scale dense residual model, and a multi-classifier mechanism within the dual-stream network. The model utilized a residual module to extract image features and employed a multi-level feature aggregation method to capture complex context information. Self-attention and cross-attention modules were utilized at each convolutional layer to fuse features from optical coherence tomography (OCT) and fundus modalities, resulting in multiple output losses. The neural network was applied to classify individuals with MCI, Alzheimer's disease, and control participants with normal cognition. Through fine-tuning the pre-trained model, we classified community-dwelling participants into two groups based on cognitive impairment test scores. To identify retinal imaging biomarkers associated with accurate prediction, we used the Gradient-weighted Class Activation Mapping technique. The proposed method achieved high precision rates of 84.96% and 80.90% in classifying MCI and positive test scores for cognitive impairment, respectively. Notably, changes in the optic nerve head on fundus photographs or OCT images among patients with MCI were not used to discriminate patients from the control group. These findings demonstrate the potential of our approach in identifying individuals with MCI and emphasize the significance of retinal imaging for early detection of cognitive impairment.