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Journal of Cardiothoracic Surgery Jul 2024This study evaluated the prevalence and quantity of lymph nodes at particular stations of the mediastinum in patients with lung cancer. These data are important to...
BACKGROUND
This study evaluated the prevalence and quantity of lymph nodes at particular stations of the mediastinum in patients with lung cancer. These data are important to radiologists, pathologists, and thoracic surgeons because they can serve as a benchmark when assessing the completeness of lymph node dissection. However, relevant data in the literature are scarce.
METHODS
Data regarding the number of lymph nodes derived from two randomised trials of bilateral mediastinal lymph node dissection, the BML-1 and BML-2 study, were included in this analysis. Detectable nodes at particular stations of the mediastinum and the number of nodes at these stations were analysed.
RESULTS
The mean number of removed nodes was 28.67 (range, 4-88). Detectable lymph nodes were present at stations 2R, 4R, and 7 in 93%, 98%, and 99% of patients, respectively. Nodes were rarely present at stations 9 L (33%), and 3 (35%). The largest number of nodes was observed at stations 7 and 4R (mean, 5 nodes).
CONCLUSION
The number of mediastinal lymph nodes in patients with lung cancer may be greater than that in healthy individuals. Lymph nodes were observed at stations 2R, 4R, and 7 in more than 90% of patients with lung cancer. The largest number of nodes was observed at stations 4R and 7. Detectable nodes were rarely observed at stations 3 and 9 L.
TRIAL REGISTRATION
ISRCTN 86,637,908.
Topics: Humans; Lung Neoplasms; Mediastinum; Lymph Node Excision; Lymph Nodes; Male; Female; Aged; Middle Aged; Lymphatic Metastasis; Prevalence
PubMed: 38956617
DOI: 10.1186/s13019-024-02928-z -
BMC Medical Imaging Jul 2024To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and... (Comparative Study)
Comparative Study
A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.
PURPOSE
To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities.
MATERIALS AND METHODS
In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded.
RESULTS
The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05).
CONCLUSION
In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.
Topics: Humans; Deep Learning; Male; Retrospective Studies; Female; Middle Aged; Lower Extremity; Aged; Phlebography; Adult; Contrast Media; Algorithms; Venous Thrombosis; Tomography, X-Ray Computed; Radiographic Image Interpretation, Computer-Assisted; Popliteal Vein; Varicose Veins; Vena Cava, Inferior; Femoral Vein; Radiation Dosage; Computed Tomography Angiography; Aged, 80 and over; Radiographic Image Enhancement
PubMed: 38956583
DOI: 10.1186/s12880-024-01342-0 -
BMC Medical Imaging Jul 2024The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from... (Comparative Study)
Comparative Study
BACKGROUND
The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR.
MATERIALS
This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods.
RESULTS
The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR.
CONCLUSIONS
In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.
Topics: Humans; Retrospective Studies; Female; Computed Tomography Angiography; Aged; Male; Endovascular Procedures; Artifacts; Middle Aged; Aortic Aneurysm, Abdominal; Deep Learning; Radiographic Image Interpretation, Computer-Assisted; Stents; Endovascular Aneurysm Repair
PubMed: 38956470
DOI: 10.1186/s12880-024-01343-z -
Scientific Reports Jul 2024Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal...
Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.
Topics: Magnetic Resonance Imaging; Brain Neoplasms; Humans; Neural Networks, Computer; Image Processing, Computer-Assisted; Algorithms; Brain
PubMed: 38956224
DOI: 10.1038/s41598-024-65714-w -
Journal of Imaging Informatics in... Jul 2024This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving...
This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving and communication systems (PACS) and comparing it with that of radiologists. Patients whose radiological reports contained the term lung cancer (3111 for training, 124 for validation, and 288 for test) were included in this retrospective study. Based on clinical indication and diagnosis sections of the radiological report (used as input data), they were classified into four groups (used as reference data): group 0 (no lung cancer), group 1 (pretreatment lung cancer present), group 2 (after treatment for lung cancer), and group 3 (planning radiation therapy). Using the training and validation datasets, fine-tuning of the pretrained LLM was conducted ten times. Due to group imbalance, group 2 data were undersampled in the training. The performance of the best-performing model in the validation dataset was assessed in the independent test dataset. For testing purposes, two other radiologists (readers 1 and 2) were also involved in classifying radiological reports. The overall accuracy of the fine-tuned LLM, reader 1, and reader 2 was 0.983, 0.969, and 0.969, respectively. The sensitivity for differentiating group 0/1/2/3 by LLM, reader 1, and reader 2 was 1.000/0.948/0.991/1.000, 0.750/0.879/0.996/1.000, and 1.000/0.931/0.978/1.000, respectively. The time required for classification by LLM, reader 1, and reader 2 was 46s/2539s/1538s, respectively. Fine-tuned LLM effectively extracted patients on pretreatment for lung cancer from PACS with comparable performance to radiologists in a shorter time.
PubMed: 38955964
DOI: 10.1007/s10278-024-01186-8 -
Journal of Imaging Informatics in... Jul 2024Despite the importance of communication, radiology departments often depend on communication tools that were not created for the unique needs of imaging workflows,...
Despite the importance of communication, radiology departments often depend on communication tools that were not created for the unique needs of imaging workflows, leading to frequent radiologist interruptions. The objective of this study was test the hypothesis that a novel asynchronous communication tool for the imaging workflow (RadConnect) reduces the daily average number of synchronous (in-person, telephone) communication requests for radiologists. We conducted a before-after study. Before adoption of RadConnect, technologists used three conventional communication methods to consult radiologists (in-person, telephone, general-purpose enterprise chat (GPEC)). After adoption, participants used RadConnect as a fourth method. Technologists manually recorded every radiologist consult request related to neuro and thorax CT scans in the 40 days before and 40 days after RadConnect adoption. Telephone traffic volume to section beepers was obtained from the hospital telephone system for the same period. The value and usability experiences were collected through an electronic survey and structured interviews. RadConnect adoption resulted in 53% reduction of synchronous (in-person, telephone) consult requests: from 6.1 ± 4.2 per day to 2.9 ± 2.9 (P < 0.001). There was 77% decrease (P < 0.001) in telephone volume to the neuro and thorax beepers, while no significant volume change was noted to the abdomen beeper (control group). Survey responses (46% response rate) and interviews confirmed the positive impact of RadConnect on interruptions. RadConnect significantly reduced radiologists' telephone interruptions. Study participants valued the role-based interaction and prioritized worklist overview in the survey and interviews. Findings from this study will contribute to a more focused work environment.
PubMed: 38955962
DOI: 10.1007/s10278-024-01157-z -
Abdominal Radiology (New York) Jul 2024
PubMed: 38955880
DOI: 10.1007/s00261-024-04409-2 -
European Radiology Jul 2024Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging...
Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
OBJECTIVES
Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.
MATERIAL AND METHODS
One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.
RESULTS
Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001).
CONCLUSIONS
Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment.
CLINICAL RELEVANCE STATEMENT
For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure.
KEY POINTS
The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.
PubMed: 38955845
DOI: 10.1007/s00330-024-10818-0 -
International Journal of Surgery Case... Jun 2024Traumatic pelvic fractures are complex injuries often associated with significant morbidity and mortality. Among the complications of pelvic trauma, rupture of the...
INTRODUCTION AND IMPORTANCE
Traumatic pelvic fractures are complex injuries often associated with significant morbidity and mortality. Among the complications of pelvic trauma, rupture of the ovarian vein represents a rare yet potentially life-threatening event. Prompt recognition and appropriate management are essential to mitigate the risk of hemorrhage and associated complications.
CASE PRESENTATION
We present a case of a 70-year-old woman who sustained a traumatic pelvic fracture following a skiing accident, resulting in rupture of the left ovarian vein. The patient came with the ambulance in the emergency room with lower abdominal tenderness, pelvic pain, but no signs of hemorrhagic shock. Imaging studies confirmed the diagnosis of a pelvic fracture with venous leakage of the left ovarian vein.
CLINICAL DISCUSSION
This review synthesizes recent insights into the diagnosis, management, and complications associated with pelvic fractures, with an emphasis on optimizing patient outcomes through a multidisciplinary approach. The analysis incorporates findings from key studies, including those by Wong and Bucknill, Ma Y et al., and Tullington and Blecker, which advocate for the use of advanced diagnostic tools like CT scans and systematic evaluation processes. These studies underline the necessity of precise classification systems such as the Tile classification to guide treatment and predict outcomes.
CONCLUSION
Management of traumatic pelvic fractures with associated vascular injuries requires a multidisciplinary approach involving trauma surgeons, interventional radiologists, and critical care specialists. Early recognition, accurate diagnosis, and timely intervention are paramount in optimizing outcomes and reducing the risk of mortality. This case underscores the importance of prompt intervention and highlights the challenges associated with traumatic pelvic fractures and rupture of the ovarian vein. Further research is warranted to enhance our understanding of optimal management strategies and improve outcomes for patients with these complex injuries.
PubMed: 38954965
DOI: 10.1016/j.ijscr.2024.109894 -
IEEE Journal of Biomedical and Health... Jul 2024Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various...
Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical applications. However, training these models is conditioned on the availability of abundant annotated data. Even if we annotate enough data, MRI images display considerable variability due to factors such as differences among patients, MRI scanners, and imaging protocols. This variability necessitates retraining neural networks for each specific application domain, which, in turn requires manual annotation by expert radiologists for all new domains. To relax the need for persistent data annotation, we develop a method for unsupervised federated domain adaptation using multiple annotated source domains. Our approach enables the transfer of knowledge from several annotated source domains for use in an unannotated target domain. Initially, we ensure that the target domain data shares similar representations with each source domain in a latent embedding space by minimizing the pair-wise distances between the distributions for the target and the source domains. We then employ an ensemble approach to leverage the knowledge obtained from all domains to build an integrated outcome. We perform experiments on two datasets to demonstrate our method is effective. Our implementation code is publicly available: https://github.com/navapatn/Unsupervised -Federated-Domain-Adaptation-for-Image-Segmentation new.
PubMed: 38954567
DOI: 10.1109/JBHI.2024.3422250