Tiny pH-Resolved DNA Nanospheres for Cellular Pyroptosis or Apoptosis Regulation.

The design and synthesis of nanomedicines capable of regulating programmed cell death patterns to enhance antitumor efficacy remain significant challenges in cancer therapy. In this study, we developed intelligent DNA nanospheres (NS) capable of distinguishing tiny pH changes between different endosomal compartments to regulate pyroptosis or apoptosis. These NS are self-assembled from two multifunctional DNA modules, enabling tumor targeting, acid-responsive disassembly, and photodynamic therapy (PDT) activation. By modifying the embedded i-motif sequence, the NS can be activated in early endosomes (EE) or lysosomes (Ly), producing singlet oxygen (1O2) at specific locations under laser irradiation. Our results demonstrate that EE-activated PDT induces gasdermin-E-mediated pyroptosis in tumor cells, enhancing antitumor efficacy and reducing systemic toxicity compared to Ly-activated apoptosis. This study offers new insights into the design of endosome-activated nanomedicines, advancing the biomedical applications of targeted cancer therapy.

Opioid-related emergency admissions in people with opioid dependence/use disorder with and without sickle cell disease: An analysis of multi-state insurance claims.

OBJECTIVE: We estimated rates of opioid-related admissions in people with sickle cell disease (SCD) diagnosed with opioid-related disorders. METHOD: We analyzed ten years (1/2006-12/2016) of multi-state claims data from 191,638 people receiving treatment for opioid-related disorders in the U.S. We used multivariable cox regression to estimate the association between admissions for opioid-related adverse events after initiating treatment and SCD status (SCD[n = 320] vs no SCD[n = 191,318]) among people with opioid-related disorders, controlling for sociodemographic variables and comorbidities. In secondary analyses, we excluded events occurring simultaneously as vaso-occlusive crises (VOCs) and computed rates of admissions for non-opioid substance-related events (i.e., alcohol, cannabis). RESULTS: Whereas 287(90 %) of the SCD cohort had >1 all-cause admission, of which 199 were for VOCs, only 78(20 %) experienced an opioid-related adverse event. The SCD cohort experienced higher rates of opioid-related admissions than the non-SCD cohort (aHR = 1.82[95 % CI = 1.51-2.19), a finding that remained robust even after excluding events that occurred at the same time as a VOC. SCD diagnoses were not associated with admissions for non-opioid substance-related events. CONCLUSIONS: Even though clinicians may perceive people with SCD as being at elevated risk for substance use disorders, opioid-related admissions made up only a small fraction of all-cause admissions among people with SCD diagnosed with opioid-related disorders, in contrast to VOCs that comprised the majority of admissions. Opioid-related admissions, while modestly higher among those with SCD than among peers without SCD, were relatively uncommon.

Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease.

Background: Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS). Methods: This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models. Results: LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively. Conclusions: The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.

Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.

Objectives: The purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning. Methods: We retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model. Results: The deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p<0.05). Conclusion: Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.

One-step high-speed thermal-electric aerosol printing of piezoelectric bio-organic films for wirelessly powering bioelectronics.

Piezoelectric biomaterials hold a pivotal role in the progression of bioelectronics and biomedicine, owing to their remarkable electromechanical properties, biocompatibility, and bioresorbability. However, their technological potential is restrained by certain challenges, including precise manipulation of nanobiomolecules, controlling their growth across nano-to-macro hierarchy, and tuning desirable mechanical properties. We report a high-speed thermal-electric driven aerosol (TEA) printing method capable of fabricating piezoelectric biofilms in a singular step. Electrohydrodynamic aerosolizing and in situ electrical poling allow instantaneous tuning of the spatial organization of biomolecular inks. We demonstrate TEA printing of ß-glycine/polyvinylpyrrolidone films, and such films exhibit the piezoelectric voltage coefficient of 190 × 10-3 volt-meters per newton, surpassing that of industry-standard lead zirconate titanate by approximately 10-fold. Furthermore, these films demonstrate nearly two orders of magnitude improvement in mechanical flexibility compared to glycine crystals. We also demonstrate the ultrasonic energy harvesters based on the biofilms, providing the possibility of wirelessly powering bioelectronics.

Effects of frailty on patients with oral and maxillofacial space infection: a retrospective analysis.

OBJECTIVES: To investigate the implications of frailty as a predictive factor for outcomes among patients with oral and maxillofacial space infection. METHODS: A retrospective cohort study was conducted to analyze 348 medical records, gathering data on several key aspects. These included the etiology of infection, the location of inflamed areas, the treatment administered, and the ultimate treatment outcomes. Additionally, the study collected information on the Symptom Severity (SS) score, frailty score, age, gender, the presence of systemic diseases, alcohol consumption, and smoking history. RESULTS: A total of 155 patients were classified as frailty, while 193 patients were classified as non-frailty. We found a significantly different in age, BMI, hospitalization expenses, length of hospital stay, SS, fibrinogen and admission to ICU between the frail group and the non- frail group. CONCLUSIONS: Frailty serves as a valuable predictor of outcomes among patients with oral and maxillofacial space infections. By identifying high-risk patients, frailty can be employed as a clinical tool to guide perioperative care, ultimately optimizing patient outcomes. Notably, frail patients often require more ICU treatment.

Altered Brain Structure in Hemifacial Spasm Patients: A Multimodal Brain Structure Study.

Objective: Hemifacial spasm (HFS) is a clinical neurosurgical disease, which brain structural alterations caused by HFS remain a topic of debate. We evaluated changes in brain microstructure associated with HFS and observed their relevance to clinical characteristics. Methods: We enrolled 72 participants. T1-weighted structural and diffusion tensor images were collected from all participants using 3.0T magnetic resonance equipment. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to identify changes in gray matter volume (GMV) and disruptions in white matter (WM) integrity. The severity of the spasms was graded using the Cohn scale. Results: VBM analysis revealed that the GMV was significantly reduced in the left Thalamus and increased GMV in the right Cerebellum IV-V of the HFS group. TBSS analysis showed that FA in the left superior longitudinal fasciculus (SLF) of the HFS group was significantly increased. GMV in the thalamus showed a negative correlation with disease duration and Cohn grade, while FA in the left SLF had a positive correlation with both the disease duration and Cohn grade. Conclusion: We identified regions with altered GMV in HFS patients. Additionally, we determined that FA in the left SLF might serve as a significant neural indicator of HFS.

Design and synthesis of triazolopyridine derivatives as potent JAK/HDAC dual inhibitors with broad-spectrum antiproliferative activity.

A series of triazolopyridine-based dual JAK/HDAC inhibitors were rationally designed and synthesised by merging different pharmacophores into one molecule. All triazolopyridine derivatives exhibited potent inhibitory activities against both targets and the best compound 4-(((5-(benzo[d][1, 3]dioxol-5-yl)-[1, 2, 4]triazolo[1, 5-a]pyridin-2-yl)amino)methyl)-N-hydroxybenzamide (19) was dug out. 19 was proved to be a pan-HDAC and JAK1/2 dual inhibitor and displayed high cytotoxicity against two cancer cell lines MDA-MB-231 and RPMI-8226 with IC50 values in submicromolar range. Docking simulation revealed that 19 fitted well into the active sites of HDAC and JAK proteins. Moreover, 19 exhibited better metabolic stability in vitro than SAHA. Our study demonstrated that compound 19 was a promising candidate for further preclinical studies.

Wafer-scale integration of two-dimensional perovskite oxides towards motion recognition.

Two-dimensional semiconductors have shown great potential for the development of advanced intelligent optoelectronic systems. Among them, two-dimensional perovskite oxides with compelling optoelectronic performance have been thriving in high-performance photodetection. However, harsh synthesis and defect chemistry severely limit their overall performance and further large-scale heterogeneous integration. Here, we report the wafer-scale integration of highly oriented nanosheets by introducing a charge-assisted oriented assembly film-formation process and confirm its universality and scalability. The shallow-trap dominance induced by structural optimization endows the device with a distinguished performance balance, including high photosensitivity close to that of single nanosheet units and fast response speed. An integrated ultra-flexible 256-pixel device demonstrates the versatility of material-to-substrate integration and conformal imaging functionality. Moreover, the device achieves efficient recognition of multidirectional motion trajectories with an accuracy of over 99.8%. Our work provides prescient insights into the large-area fabrication and utilization of 2D perovskite oxides in advanced optoelectronics.

Modular Assembled Localized Hybridization Chain Reaction for In Situ mRNA Amplified Imaging.

As a nonenzymatic DNA signal amplification technique, localized hybridization chain reaction (LHCR) was designed to improve the limitations in response speed and low sensitivity of conventional free diffusional HCR (hybridization chain reaction). However, it is still confronted with the challenges of complicated DNA scaffolds with low loading capacity and a time-consuming process of diffusion. Herein, we introduced modular assembly of a DNA minimal scaffold for coassembly of DNA hairpins for amplified fluorescence imaging of mRNA in situ. DNA hairpins were spatially bound to two Y-shaped modules to form H-shaped DNA modules, and then multiple H-shaped DNA modules can further assemble into an H-module-based hairpin scaffold (HHS). Benefiting from highly spatial localization and high loading capacity, the HHS system showed higher sensitivity and faster speed. It has also been proven to work perfectly in vitro and in vivo, which could provide a promising bioanalysis system for low abundance biomolecule detection.

CT-based whole lung radiomics nomogram for identification of PRISm from non-COPD subjects.

BACKGROUND: Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening. METHODS: Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts. RESULTS: The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p < 0.001). The radiomics nomogram incorporating independent predicting factors (radiomics signature, age, BMI, and gender) well discriminated PRISm from non-COPD subjects compared with clinical model or radiomics signature alone for training cohort (AUC 0.787 vs. 0.675 vs. 0.778), internal (AUC 0.773 vs. 0.682 vs. 0.767) and external validation cohorts (AUC 0.702 vs. 0.610 vs. 0.699). Decision curve analysis suggested that our constructed radiomics nomogram outperformed clinical model. CONCLUSIONS: The CT-based whole lung radiomics nomogram could identify PRISm to help decision-making in clinic.

Differentiating calcifications from cerebral microbleeds using quantitative susceptibility mapping.

OBJECTIVES: To differentiate cerebral microbleeds (CMBs) and calcifications using quantitative susceptibility mapping (QSM). METHODS: CMBs were visualized and located using QSM from susceptibility-weighted imaging data collected on a 3-T MR scanner. Calcifications of the pineal gland and the choroid plexus were localized first using CT. All calcifications and CMBs were assessed using QSM to evaluate their magnetic susceptibility. The distribution of the magnetic susceptibility for the CMBs was determined and the CT attenuation was correlated with the mean magnetic susceptibility for the calcifications. RESULTS: A total of 232 hypointense foci were selected from the QSM data: 121 were CMBs and 111 were calcifications. The mean magnetic susceptibility was -214 ± 112 ppb for the calcifications and 392 ± 204 ppb for the CMBs. The minimum value of magnetic susceptibility was 75 ppb for all the CMBs and the maximum value was -52 ppb for all the calcifications. The calcifications were clearly differentiable from the CMBs from the sign alone (p < 0.001). The magnetic susceptibility for the CMBs was 299 ± 133 ppb in the lobar subcortical white matter and 499 ± 220 ppb for deep CMBs in the basal ganglia, thalamus, and brainstem. There was a significant difference in the susceptibility between these two regions (p < 0.001). CONCLUSION: The sign of the magnetic susceptibility was sufficient to differentiate calcifications and CMBs. The concentration of calcium or iron can be determined from the susceptibility value itself. The deep CMBs had higher susceptibility on average than lobar bleeds. CLINICAL RELEVANCE STATEMENT: This study's ability to differentiate between CMBs and calcifications using QSM could enhance diagnostic accuracy, guiding more precise treatment decisions for stroke or tumor patients. KEY POINTS: The sign of magnetic susceptibility is sufficient to differentiate calcifications and CMBs. QSM can successfully differentiate calcifications from microbleeds. The concentration of calcium or iron can be determined from the susceptibility value itself.

Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50.

Background: Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%. Methods: A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Results: Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. Conclusions: A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.

The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.

PURPOSE: To establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT). METHODS: 294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram. RESULTS: The combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models. CONCLUSION: An analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.

Ultra-broadband diffractive imaging with unknown probe spectrum.

Strict requirement of a coherent spectrum in coherent diffractive imaging (CDI) architectures poses a significant obstacle to achieving efficient photon utilization across the full spectrum. To date, nearly all broadband computational imaging experiments have relied on accurate spectroscopic measurements, as broad spectra are incompatible with conventional CDI systems. This paper presents an advanced approach to broaden the scope of CDI to ultra-broadband illumination with unknown probe spectrum, effectively addresses the key challenges encountered by existing state-of-the-art broadband diffractive imaging frameworks. This advancement eliminates the necessity for prior knowledge of probe spectrum and relaxes constraints on non-dispersive samples, resulting in a significant extension in spectral bandwidth, achieving a nearly fourfold improvement in bandlimit compared to the existing benchmark. Our method not only monochromatizes a broadband diffraction pattern from unknown illumination spectrum, but also determines the compressive sampled profile of spectrum of the diffracted radiation. This superiority is experimentally validated using both CDI and ptychography techniques on an ultra-broadband supercontinuum with relative bandwidth exceeding 40%, revealing a significantly enhanced coherence and improved reconstruction with high fidelity under ultra-broadband illumination.

Deep-learning-assisted sidewall profiling white light interferometry system for accurately measuring 3D profiles and surface roughness on the groove sidewalls of precision components.

The accurate measurement of surface three-dimensional (3D) profile and roughness on the groove sidewalls of components is of great significance to diverse fields such as precision manufacturing, machining processes, energy transportation, medical equipment, and semiconductor industry. However, conventional optical measurement methods struggle to measure surface profiles on the sidewall of a small groove. Here, we present a deep-learning-assisted sidewall profiling white light interferometry system, which consists of a microprism-based interferometer, an optical path compensation device, and a convolutional neural network (CNN), for the accurate measurement of surface 3D profile and roughness on the sidewall of a small groove. We have demonstrated that the sidewall profiling white light interferometry system can achieve a measurement accuracy of 2.64 nm for the 3D profile on a groove sidewall. Moreover, we have demonstrated that the CNN-based single-image super-resolution (SISR) technique could improve the measurement accuracy of surface roughness by over 30%. Our system can be utilized in cases where the width of the groove is only 1 mm and beyond, limited only by the size of the microprism and the working distance of the objective used in our system.

Image Quality Assessment of a Deep Learning-Based Automatic Bone Removal Algorithm for Cervical CTA.

BACKGROUND: The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed. RESULTS: Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity (r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm. CONCLUSIONS: The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.

Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci.

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Comparison of single-shot, FOCUS single-shot, MUSE, and FOCUS MUSE diffusion weighted imaging for pulmonary lesions: A pilot study.

Rationale and objectives: To compare the performance of SS, FOCUS SS, MUSE, and FOCUS MUSE DWI for pulmonary lesions to obtain a better technique for pulmonary DWI imaging. Materials and methods: 44 patients with pulmonary lesions were recruited to perform pulmonary DWI using SS, FOCUS SS, MUSE, and FOCUS MUSE sequences. Then, two radiologists with 12 and 10 years of chest MRI experiences assessed the overall image quality while another two radiologists both with 3 years of experiences evaluated the SNR, DR, and ADC of pulmonary lesions. Using interclass correlation coefficient (ICC) and kappa statistics to assess consistency of readers, Friedman test and Dunn-Bonferroni post hoc were used to calculate the difference between sequences. Mann-Whitney test and ROC curve were used to distinguish malignant from benign lesions. Results: All the assessed variables of the four sequences presented good to excellent intra-/inter-observer consistency. Compared with SS, FOCUS SS and MUSE, FOCUS MUSE demonstrated better image quality, including significantly higher 5-point Likert scale score (P < 0.001) and smaller DR (P < 0.001). SNR was comparable among SS, FOCUS SS, and FOCUS MUSE (P > 0.05) while MUSE presented with significantly higher SNR over them (P < 0.01). ADC of malignant was significantly smaller than that of benign for all the four sequences (P < 0.05). ROC analysis showed relatively better diagnostic performance of FOCUS MUSE (AUC = 0.820) over SS (AUC = 0.748), FOCUS SS (AUC = 0.778), and MUSE (AUC = 0.729) in distinguishing malignant from benign lesions. Conclusion: FOCUS MUSE possessed sufficient SNR and was better over SS, FOUCS SS, and MUSE for characterizing pulmonary lesions.