QUALIPAED-A retrospective quality control study evaluating pediatric long axial field-of-view low-dose FDG-PET/CT.

Introduction: Pediatric patients have an increased risk of radiation-induced malignancies due to their ongoing development and long remaining life span. Thus, optimization of PET protocols is an important task in pediatric nuclear medicine. Long axial field-of-view (LAFOV) PET/CT has shown a significant increase in sensitivity, which provides an ideal opportunity for reduction of injected tracer activity in the pediatric population. In this study we aim to evaluate the clinical performance of a 2-[18F]FDG-tracer reduction from 3 MBq/kg to 1.5 MBq/kg on the Biograph Vision Quadra LAFOV PET/CT. Materials and methods: The first 50 pediatric patients referred for clinical whole-body PET/CT with 1.5 MBq/kg 2-[18F]FDG, were included. A standard pediatric protocol was applied. Five reconstructions were created with various time, filter and iteration settings. Image noise was computed as coefficient-of-variance (COV = SD/mean standardized-uptake-value) calculated from a spherical 20-50 mm (diameter) liver volume-of-interest. Sets of reconstructions were reviewed by one nuclear medicine physicians, who reported image lesions on a pre-defined list of sites. Paired comparison analysis was performed with significance at PB < 0.05 (Bonferroni corrected). Results: All reconstructions, except one, achieved a COVmean (0.08-0.15) equal to or lower than current clinical acceptable values (COVref ≤ 0.15). Image noise significantly improved with increasing acquisition time, lowering iterations (i) from 6i to 4i (both with five subsets) and when applying a 2 mm Gauss filter (PB < 0.001). Significant difference in lesion detection was seen from 150s to 300s and from 150s to 600s (PB = 0.006-0.007). 99% of all lesions rated as malignant could be found on the 150s reconstruction, while 100% was found on the 300s, when compared to the 600s reconstruction. Conclusion: Injected activity and scan time can be reduced to 1.5 MBq/kg 2-[18F]FDG with 5 min acquisition time on LAFOV PET/CT, while maintaining clinical performance in the pediatric population. These results can help limit radiation exposure to patients and personnel as well as shorten total scan time, which can help increase patient comfort, lessen the need for sedation and provide individually tailored scans.

Off-Label Use of Naltrexone in Pica and Other Compulsive Behaviors: A Report of Two Cases.

Pica is known to the medical community as an eating disorder in which individuals may ingest non-food items due to a nutritional deficiency and cause unintentional physical harm to themselves. This article discusses the cases of children with pica in addition to other comorbidities such as trichotillomania, depression, autism, and anxiety. Both patients were trialed on typical first-line treatments to address pica symptoms, including antidepressants, psychotherapy, and neurology consults, which were ineffective in treating pica symptoms. The introduction of naltrexone resulted in significant improvements, including decreased pica symptoms and improvements in depression, anxiety, and overall behaviors. These effects of naltrexone were further bolstered by the effects that occurred when both patients discontinued naltrexone for some time.

Unilateral Spinal Anesthesia in Hip Fracture Surgery for Geriatric Patients With High Cardiovascular Risk due to Aortic Stenosis is Safe and Effective.

Introduction: Aortic stenosis is a cause of mortality or morbidity. It complicates the selection and management of anesthetic procedures. The aim of this study was to evaluate the efficacy, hemodynamic effects and postoperative outcome of unilateral spinal anesthesia in geriatric patients with hip fractures with moderate or severe aortic stenosis. Material and Method: A retrospective observational study was conducted on geriatric high-risk patients with cardiac conditions who underwent surgery for hip fractures under unilateral spinal anesthesia with low-dose hyperbaric bupivacaine. The study period spanned from January 2018 to December 2021. The inclusion criteria were individuals with moderate to severe aortic stenosis, as defined by the American Heart Association Criteria. Data on demographic information, cardiac pathologies, hemodynamic data, data on motor and sensory block, perioperative complications, and mortality rates at 30th and 180th days were collected. Results: Mortality rates at the 30th day and 180th day were 8.9% (n:4) and 24.4% (n:11), respectively. T6 level was predominantly obtained level of anesthesia (44.4%). Motor and sensory block formation times averaged 7.6 and 4.8 minutes, respectively. Surgical procedures were performed mostly within 1 hour (66.7%), and complications were rare (11.1% hypotension). Initial analgesic effect showed a rapid resolution, with 64.4% of patients requiring analgesic within the first hour postoperatively. Conclusion: In elderly patients with moderate to severe aortic stenosis scheduled for hip fracture surgery, we posit that unilateral spinal anesthesia with ultra-low dose is safe and effective option.

A new visual State Space Model for low-dose CT denoising.

BACKGROUND: Low-dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long-range modeling capabilities, while Transformers are hindered by high computational complexity. PURPOSE: In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long-range dependencies with linear computational complexity for LDCT denoising. METHODS: In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder-Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth-wise convolution, max-pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max-pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient-sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance. RESULTS: The performance of our proposed ViMEDnet is compared to five state-of-the-art LDCT denoising methods, including an iterative algorithm, two CNN-based methods, and two Transformer-based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low-contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet. CONCLUSIONS: The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options.

Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.

In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plethora of LDCT denoising methods have been proposed. Among them, deep learning (DL) approaches have emerged as the most effective, due to their robust feature extraction capabilities. Yet, the prevalent use of supervised training paradigms is often impractical due to the challenges in acquiring low-dose and normal-dose CT pairs in clinical settings. Consequently, unsupervised and self-supervised deep learning methods have been introduced for LDCT denoising, showing considerable potential for clinical applications. These methods' efficacy hinges on training strategies. Notably, there appears to be no comprehensive reviews of these strategies. Our review aims to address this gap, offering insights and guidance for researchers and practitioners. Based on training strategies, we categorize the LDCT methods into six groups: (i) cycle consistency-based, (ii) score matching-based, (iii) statistical characteristics of noise-based, (iv) similarity-based, (v) LDCT synthesis model-based, and (vi) hybrid methods. For each category, we delve into the theoretical underpinnings, training strategies, strengths, and limitations. In addition, we also summarize the open source codes of the reviewed methods. Finally, the review concludes with a discussion on open issues and future research directions.

Real-Space Tilting Method for Atomic Resolution STEM Imaging of Nanocrystalline Materials.

Atomic-resolution scanning transmission electron microscopy (STEM) characterization requires precise tilting of the specimen to a high symmetric zone axis, which is usually processed in reciprocal space by following the diffraction patterns. However, for small-sized nanocrystalline materials, their diffraction patterns are often too faint to guide the tilting process. Here, a simple and effective tilting method is developed based on the diffraction contrast change of the shadow image in the Ronchigram. The misorientation angle of the specimen can be calculated and tilted to the zone axis based on the position of the shadow image with lowest intensity. This method requires no prior knowledge of the sample and the maximum misorientation angle that can be corrected is >±6.9° with sub-mrad accuracy. It operates in real space, without recording the diffraction patterns of the specimens, making it particularly effective for nanocrystalline materials. Combined with the scripting to control the microscope, the sample can be automatically tilted to the zone axis under low dose conditions (<0.17 e- Å- 2 s-1), facilitating the imaging of beam sensitive materials such as zeolites or metal-organic frameworks. This automated tilting method can significantly contribute to the atomic-scale characterization of the nanocrystalline materials by STEM imaging.

Anti-infectivity efficacy and pharmacokinetics of WHO recommended single low-dose primaquine in children with acute Plasmodium falciparum in Burkina Faso: study protocol.

BACKGROUND: Primaquine (PQ) has activity against mature P. falciparum gametocytes and proven transmission blocking efficacy (TBE) between humans and mosquitoes. WHO formerly recommended a single transmission blocking dose of 0.75 mg/kg but this was little used. Then in 2012, faced with the emergence of artemisinin-resistant P. falciparum (ARPf) in SE Asia, the WHO recommended a lower dose of 0.25 mg/kg to be added to artemisinin-based combination therapy in falciparum-infected patients in low transmission areas. This dose was considered safe in glucose-6-phosphate dehydrogenase deficiency (G6PDd) and not requiring G6PD testing. Subsequent single low-dose primaquine (SLDPQ) studies have demonstrated safety in different G6PD variants. Dosing remains challenging in children under the age of 5 because of the paucity of PQ pharmacokinetic (PK) data. We plan to assess the anti-infectivity efficacy of SLDPQ using an allometrically scaled, weight-based regimen, with a target dose of 0.25 mg/kg, in children with acute uncomplicated falciparum malaria. METHODS: This study is an open label, randomised 1:1, phase IIb study to assess TBE, tolerability, pharmacokinetics and acceptability of artesunate pyronaridine (ASPYR) administered alone or combined with SLDPQ in 56 Burkinabe children aged ≥ 6 months- < 5 years, with uncomplicated P. falciparum and a haemoglobin (Hb) concentration of ≥ 5 g/dL. We will assess TBE, using direct membrane feeding assays (DMFA), and further investigate PQ pharmacokinetics, adverse events, Hb dynamics, G6PD, sickle cells, thalassaemia and cytochrome 2D6 (CYP2D6) status, acceptability of flavoured PQ [CAST-ClinSearch Acceptability Score Test®], and the population's knowledge, attitude and practices on malaria. EXPECTED RESULTS AND DISCUSSION: We expect children to accept tablets, confirm the TBE and gametocytocidal effects of SLDPQ and then construct a PK infectivity model (including age, sex, baseline Hb, G6PD and CYP2D6 status) to define the dose response TBE relationship that may lead to fine tuning our SLDPQ regimen. Our study will complement others that have examined factors associated with Hb dynamics and PQ PK. It will provide much needed, high-quality evidence of SLDPQ in sick African children and provide reassurance that SLDPQ should be used as a strategy against emerging ARPf in Africa. TRIAL REGISTRATION: ISRCTN16297951. Registered on September 26, 2021.

"Because that is the right thing to do": A focus group study of Australian expert perspectives on offering smoking cessation support in lung cancer screening.

INTRODUCTION: Lung cancer screening (LCS) trials, targeting people with smoking history, have demonstrated reduced mortality. How to optimally embed evidence-based smoking cessation support in LCS, including in Australia, needs to be better understood. We sought experts' perspectives to identify potential barriers and effective implementation strategies. METHODS: Perceptions of providing smoking cessation support in LCS were elicited in 24 focus groups and three individual interviews with clinicians, cancer screening program managers/policymakers, and researchers during 2021. We conducted framework analysis and mapped key topics to the updated Consolidated Framework for Implementation Research. RESULTS: Experts (N=84 participants) strongly supported capitalising on an "opportune time" for smoking cessation and new LCS participant contact opportunities throughout the screening and assessment pathway. Many advocated for adapting existing cessation resources to the LCS setting and providing support without participant costs. Experts generally considered referral alone to established programs (e.g., telephone Quitline) as insufficient, but likely helpful in follow-up, and dedicated cessation specialist roles as essential. Broader cessation messaging (via mass media/community channels) was also suggested to reinforce individualised support. Experts described inherent alignment, and an ethical responsibility, to deliver smoking cessation as a core LCS component. It was suggested that LCS-eligible participants' varied experiences of stigma, health literacy, and motivation, be considered in cessation supports. Primary care support and individualised interventions were suggested to facilitate implementation. CONCLUSIONS: Experts considered smoking cessation support essential in LCS. The expert-identified and multi-level implementation strategies described here can directly inform smoking cessation-specific planning for Australia's forthcoming National LCS Program. IMPLICATIONS: The international literature includes few examples considering how best to provide smoking cessation support within a lung cancer screening (LCS) program in advance of program commencement. Our analysis, using the updated Consolidated Framework for Implementation Research, is one of the first to explore experts' perspectives within this context. Experts identified multiple implementation barriers to providing smoking cessation support within and outside of an Australian LCS program, including key work infrastructure barriers, and advocated for providing tailored interventions within this program. Our foundational work in a new targeted screening program's pre-implementation phase will allow international comparisons to be made.

Cost-Effectiveness of Lung Cancer Screening with Low-Dose Computed Tomography: Comparing Hungarian Screening Protocols with the US NLST.

We aimed to directly compare the cost-effectiveness of Hungarian (following the NELSON trial) and NLST screening protocols, two trials influencing lung-cancer-screening implementation internationally. A decision-analytic model analyzing the cost-effectiveness of Hungarian protocols was manipulated to reflect the protocols of the NLST, while maintaining features specific to the Hungarian healthcare setting. In the Hungarian protocol, there are three possible outcomes to the initial round of screening, positive, negative, and indeterminate, indicating an uncertain degree of suspicion for lung cancer. This protocol differs from the NLST, in which the only possible screening outcomes are positive or negative, with no indeterminate option. The NLST pathway for smokers aged 55-74 resulted in a EUR 43 increase in the total average lifetime costs compared to the Hungarian screening pathway and resulted in a lifetime gain of 0.006 QALYs. The incremental costs and QALYs yielded an ICER of 7875 EUR/QALY. Our results demonstrate that assigning any suspicious LDCT screen as a positive result (NLST protocol) rather than indeterminate (Hungarian protocol) can reduce patient uncertainty and yield a slight QALY gain that is worth the additional use of resources according to Hungary's willingness-to-pay threshold. A stratified analysis by age was also conducted, revealing decreasing cost-effectiveness when screening older cohorts. Our study provides insight into the cost-effectiveness, advantages, and disadvantages of various LDCT screening protocols for lung cancer and can assist other countries as they implement their screening programs.

Effects of Continuous Prenatal Low Dose Rate Irradiation on Neurobehavior, Hippocampal Cellularity, Messenger RNA and MicroRNA Expression on B6C3F1 Mice.

Epidemiological, experimental, and ecological data have indicated the controversial effect of in utero chronic low dose rate (<6 mGy/h) with accumulative low (≤100 mGy) or high (>100 mGy) dose radiation exposure. Our main goal of this study was to examine if different low dose rates of chronic pre- and/or post-natal radiation exposure with accumulative high doses could induce hippocampal cellular, mRNA, and miRNA changes leading to neuropsychiatric disorders. The comprehensive mouse phenotypic traits, organ weight, pathological, and blood mRNA and miRNA changes were also studied. Using different approaches including SmithKline, Harwell, Imperial College, Royal Hospital, Phenotype Assessment (SHIRPA), neurobehavioral tests, pathological examination, immunohistochemistry, mRNA and miRNA sequencing, and real-time quantitative polymerase chain reaction (qRT-PCR) validation, we found that in prenatally irradiated (100 mGy/d for 18 days with an accumulative dose of 1.8 Gy) 1-year-old mice, no cellular changes, including immature neurons in the subgranular zone, mature neurons and glial cells in the hilus of the dentate gyrus and development of cognitive impairment, neuropsychiatric disorders, occurred. However, a significant reduction in body weight and mass index (BMI) was indicated by the SHIRPA test. A reduced exploratory behavior was shown by an open field test. Organ weights showed significant reductions in the testes, kidneys, heart, liver and epididymides with no abnormal pathology. mRNA and miRNA sequencing and qRT-PCR validation revealed the upregulation of Rubcnl and Abhd14b, and downregulation of Hspa1b, P4ha1, and Banp genes in both the hippocampus and blood of mice prenatally irradiated with 100 mGy/d. Meanwhile, downregulation of miR-448-3p and miR1298-5p in the hippocampus, miR-320-3p, miR-423-5p, miR-486b-5p, miR-486b-3p, miR-423-3p, miR-652-3p, miR-324-3p, miR-181b-5p, miR-let-7b, and miR-6904-5p in the blood was induced. The target scan revealed that Rubcnl is one of the miR-181b-5p targets in the blood. We, therefore, concluded that prenatal chronic irradiation with a low dose rate of 100 mGy/d and accumulative dose of 1.8 Gy or below might not induce significant adverse health effects on the offspring. Further study of different low dose rate radiation exposures with accumulative high doses may provide threshold doses for authorities or regulators to set new radiation safety guidelines to replace those extrapolated from acute high dose/dose rate irradiation to reduce unnecessary emergency evacuation or spending once a nuclear accident or leakage occurs.

Ultraslow low-dose thrombolytic therapy in patients with acute mechanical prosthetic heart valve thrombosis - A prospective cohort study.

BACKGROUND: While surgery still remains the gold standard treatment for mechanical prosthetic valve thrombosis (MPVT) by many guidelines, the ultraslow low-dose thrombolytic regimen has been reported as a promising alternative. METHODS: In this prospective single-center cohort, patients with acute MPVT were treated with an ultraslow low-dose thrombolytic regimen consisting of 25 mg infusion of recombinant tissue-type plasminogen activator (rtPA) over 25 h. The regimen could be repeated in case of failure until resolution/occurrence of adverse events or a maximum cumulative dose of 150 mg. The primary outcome was the complete MPVT resolution rate; other outcomes included first-dose success rate, major bleeding, thromboembolic events, mortality, and total thrombolytic dose/duration. RESULTS: Between April 2018 to January 2024, 135 episodes of acute MPVT were treated with an ultraslow low-dose thrombolytic regimen in 118 patients. In 118/135 (87.4 %) episodes, right-sided prosthetic valve was involved. Complete success was achieved in 88.1 % of cases, with 39.5 % responding after the first dose. The median total dose was 50 mg over a median of 30 h. Only one fatal intracranial hemorrhage occurred (0.7 %), with no other bleeding or thromboembolic complications. CONCLUSION: The ultraslow low-dose thrombolytic regimen appears to exhibit high efficacy and acceptable safety in treating acute MPVT. Further large clinical trials are essential for validating these preliminary findings.

Benchmarking deep learning-based low-dose CT image denoising algorithms.

BACKGROUND: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms. PURPOSE: Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results. METHODS: In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardized setup. RESULTS: Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal at best. CONCLUSIONS: This study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate their algorithms.

Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.

This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice.

Low-dose apatinib optimizes the vascular normalization and enhances the antitumor effect of PD-1 inhibitor in gastric cancer.

Background: Apatinib is a tyrosine kinase inhibitor that has shown potential in combination with immune checkpoint inhibitors (ICIs) in gastric cancer (GC); however, its role in GC is unclear. This research aims to investigate the effect of low-dose apatinib in GC, and analyze the mechanisms of its underlying action. Methods: A mouse model of GC was established, and the experimental mice were divided into different groups for different treatment: group NS (normal saline), group A (low-dose apatinib 50 mg/kg), group B (high-dose apatinib 200 mg/kg), group C [programmed cell death protein 1 (PD-1) inhibitor monotherapy], and group D (PD-1 inhibitor combined with low-dose apatinib). After 14 days of treatment, the tumor and blood samples were collected from all mice for histological and cytokine detection. Results: Compared with the control group, mice in the low-dose apatinib group showed smaller tumor volumes and slower growth. CD31/α-smooth muscle actin (α-SMA) double staining revealed significantly higher coverage of perivascular cells in the low-dose apatinib group by contrast to the control and high-dose apatinib groups, suggesting that low-dose apatinib may alleviate hypoxia. Compared to the high-dose apatinib group, the expression of hypoxia inducible factor 1 alpha (HIF1α) significantly decreased in the low-dose apatinib group. Hematoxylin and eosin (HE) staining results showed a higher proportion of necrotic tumor tissues in the group of mice treated with low-dose apatinib combined with PD-1 inhibitor than in other groups. In addition, this combined treatment significantly reduced the expression of NG2 and HIF1α in mouse tumor tissues, indicating a more normalized vascular density, and also increased the proportion of CD8+ T cells. Conclusions: Low-dose apatinib enhances the antitumor effect of PD-1 inhibitor by normalizing tumor-related blood vessels, alleviating intratumor hypoxia and altering immunosuppressive microenvironment (IM).

Knowledge and beliefs about lung cancer screening among Black individuals at high risk: a qualitative approach.

Background: Despite its efficacy in reducing lung cancer (LC)-specific mortality by 20%, screening with low-dose computed tomography (LDCT) in eligible groups remains low (5-16%). Black individuals are more commonly affected by LC than other racial/ethnic groups in the United States (U.S.) but less likely to undergo LC screening (LCS). Our study aimed to explore the knowledge and beliefs of Black individuals at high risk regarding LCS. Methods: Black individuals (n=17) who met the 2021 United States Preventive Services Task Force (USPSTF) LCS eligibility criteria were recruited in upstate New York. In-depth semi-structured interviews were conducted, audio recorded, and transcribed to explore knowledge and beliefs that could influence the uptake of LCS. A qualitative thematic analysis method was used to identify and analyze themes within the data. Results: We identified principal themes about LC and LCS. Although most participants reported that smoking was the major risk factor for LC, some participants placed more emphasis on other factors as the major risk factors for LC and de-emphasized the role of smoking. Most participants were not aware that screening for LC existed. Several barriers and facilitators for LCS were identified. Conclusions: Awareness about LCS among Black individuals is low. Addressing barriers may help increase LCS rates among Black individuals, ultimately reducing their LC mortality. The findings from our study have important implications in designing more effective interventions involving community health workers and healthcare clinicians to increase LCS uptake among Black individuals at high risk.

Emphysema and lung cancer risk.

Background: With increasing significance of lung cancer screening programs, it is essential to determine the group of participants, who would benefit the most from screening. In our study, we aimed to establish the correlation between lung emphysema and lung cancer risk. Methods: The study design was cross-sectional. Low-dose computed tomography (LDCT) scans of 896 subjects from MOLTEST-BIS lung cancer screening program, including 100 subjects with detected lung cancer, were visually evaluated for the presence, type and severity of emphysema. Quantitative emphysema evaluation was performed with Siemens syngo.via Pulmo 3D application. Results: Visually detected presence of centrilobular emphysema (CLE) correlated with male gender (P=0.02), age (P<0.001) and pack-years of smoking (P=0.004), as well as with quantitative assessment of Emphysema Index (EI) (P=0.008), and with emphysema clusters of given size (Clas 1-4) Clas 1, Clas 3 and Clas 4 (P<0.001). Visually assessed severity grade of emphysema correlated with age (P<0.001), pack-years of smoking history (P=0.002) and EI (P<0.001). There was a correlation between lung cancer occurrence and pack-years (P<0.001), age (P<0.001), and presence of CLE (P<0.001) but no correlation with gender (P=0.88) and EI (P=0.32) was found. In the logistic regression model pack-years, age, qualitative severity of CLE and Clas 1 were significant factors correlated with lung cancer occurrence (P<0.001). Conclusions: Qualitative and quantitative emphysema evaluation correlate with each other. Both, presence and severity of CLE correlate with higher incidence of lung cancer. Severity of visually assessed emphysema, age and pack-years of smoking are significant predictors of lung cancer occurrence.

Ultra-fast Digital DPC Yielding High Spatio-temporal Resolution for Low-Dose Phase Characterization.

In the scanning transmission electron microscope, both phase imaging of beam-sensitive materials and characterization of a material's functional properties using in situ experiments are becoming more widely available. As the practicable scan speed of 4D-STEM detectors improves, so too does the temporal resolution achievable for both differential phase contrast (DPC) and ptychography. However, the read-out burden of pixelated detectors, and the size of the gigabyte to terabyte sized data sets, remain a challenge for both temporal resolution and their practical adoption. In this work, we combine ultra-fast scan coils and detector signal digitization to show that a high-fidelity DPC phase reconstruction can be achieved from an annular segmented detector. Unlike conventional analog data phase reconstructions from digitized DPC-segment images yield reliable data, even at the fastest scan speeds. Finally, dose fractionation by fast scanning and multi-framing allows for postprocess binning of frame streams to balance signal-to-noise ratio and temporal resolution for low-dose phase imaging for in situ experiments.

The added value of an AI-based body composition analysis in a lung cancer screening population: preliminary results.

BACKGROUND AND AIMS: Body composition has been linked with clinical and prognostic outcomes in patients with cancer and cardiovascular diseases. Body composition analysis in lung cancer screening (LCS) is very limited. This study aimed at assessing the association of subcutaneous fat volume (SFV) and subcutaneous fat density (SFD), measured on chest ultra-low dose computed tomography (ultra-LDCT) images by a fully automated artificial intelligence (AI)-based software, with clinical and anthropometric characteristics in a LCS population. METHODS AND RESULTS: Demographic, clinical, and dietary data were obtained from the written questionnaire completed by each participant at the first visit, when anthropometric measurements, blood sample collection and chest ultra-LDCT were performed. Images were analyzed for automated 3D segmentation of subcutaneous fat and muscle. The analysis included 938 volunteers (372 females); men with a smoking history of ≥40 pack-years had higher SFV (p = 0.0009), while former smokers had lower SFD (p = 0.0019). In female participants, SFV and SFD differed significantly according to age. SFV increased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.0001), whereas SFD decreased with rising BMI, waist circumference, waist-hip ratio, and CRP levels ≥2 mg/L (p < 0.001) in both sexes. SFV was associated with glycemia and triglycerides levels (p = 0.0067 and p=<0.0001 in males, p = 0.0074 and p < 0.0001 in females, respectively), while SFD with triglycerides levels (p < 0.0001). CONCLUSION: We observed different associations of SFV and SFD with age and smoking history between men and women, whereas the association with anthropometric data, CRP, glycemia and triglycerides levels was similar in the two sexes.

Inter-observer consistency on subsolid nodule follow-up recommendation based on National Comprehensive Cancer Network (NCCN) guidelines in low-dose computed tomography (LDCT) lung cancer screening.

Background: Follow-up management of pulmonary nodules is a crucial component of lung cancer screening. Consistency in follow-up recommendations is essential for effective lung cancer screening. This study aimed to assess inter-observer agreement on National Comprehensive Cancer Network (NCCN) guideline-based follow-up recommendation for subsolid nodules from low-dose computed tomography (LDCT) screening. Methods: A retrospective collection of LDCT reports from 2014 to 2017 for lung cancer screening was conducted using the Radiology Information System and keyword searches, focusing on subsolid nodules. A total of 110 LDCT cases containing subsolid nodules were identified. Two senior radiologists provided standardized follow-up recommendation. Follow-up recommendation was categorized into four groups (0-, 3-, 6-, and 12-month). To ensure overall balance and representativeness of the follow-up categories, 60 scans from 60 participants were included (distribution ratio 1:1:2:2). Cases were categorised into follow-up recommendation groups by five observers following NCCN guidelines. Fleiss' kappa statistic was used to evaluate inter-observer agreement. Results: Overall accuracy rate for follow-up recommendation among five observers was 72.3%. Chest radiologists' overall agreement was significantly higher than radiology residents (P<0.01). The overall agreement among the five observers was moderate, with a Fleiss' kappa of 0.437. For all paired readers, the mean Cohen's kappa value was 0.603, with 95% confidence interval (CI) from 0.489 to 0.716. Chest radiologists demonstrated substantial agreement, evidenced by a Cohen's kappa of 0.655 (95% CI: 0.503-0.807). In contrast, the mean Cohen's kappa among radiology residents was 0.533 (95% CI: 0.501-0.565). The majority of cases with discrepancies, accounting for 73.5%, were associated with the same risk-dominant nodules. A higher proportion of part-solid nodule was a risk factor for discrepancies. Of the 600 paired readings, major discrepancies and substantial discrepancies were observed in 27.5% and 4.8% (29/600) of the cases. Conclusions: In subsolid nodules, category evaluation of observer follow-up recommendation based on NCCN guidelines achieved moderate consistency. Disagreements were mainly caused by measurement and type disagreements of identical risk-dominant nodules. Part-solid nodule was a contributor for discrepancies in follow-up recommendation. Major and substantial management discrepancies were 27.5% and 4.8% in the paired evaluations.

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .