RESUMEN
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy. METHODS: The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions. RESULTS: The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups. CONCLUSION: The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction.
Asunto(s)
COVID-19 , Redes Neurales de la Computación , SARS-CoV-2 , Humanos , COVID-19/mortalidad , COVID-19/diagnóstico , Masculino , Femenino , Persona de Mediana Edad , Hong Kong/epidemiología , Anciano , Adulto , Pruebas Hematológicas/métodos , Aprendizaje Automático , Modelos de Riesgos Proporcionales , Estudios de CohortesRESUMEN
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known as target domain) is often degraded in clinical scenarios due to the variations that were not encountered in training data (also known as source domain). Unsupervised domain adaptation (UDA) of LDCT reconstruction has been proposed to solve this problem through distribution alignment. However, existing UDA methods fail to explore the usage of uncertainty quantification, which is crucial for reliable intelligent medical systems in clinical scenarios with unexpected variations. Moreover, existing direct alignment for different patients would lead to content mismatch issues. To address these issues, we propose to leverage a probabilistic reconstruction framework to conduct a joint discrepancy minimization between source and target domains in both the latent and image spaces. In the latent space, we devise a Bayesian uncertainty alignment to reduce the epistemic gap between the two domains. This approach reduces the uncertainty level of target domain data, making it more likely to render well-reconstructed results on target domains. In the image space, we propose a sharpness-aware distribution alignment (SDA) to achieve a match of second-order information, which can ensure that the reconstructed images from the target domain have similar sharpness to normal-dose CT (NDCT) images from the source domain. Experimental results on two simulated datasets and one clinical low-dose imaging dataset show that our proposed method outperforms other methods in quantitative and visualized performance.
RESUMEN
Importance: Whether stereotactic body radiotherapy (SBRT) as a bridge to liver transplant for hepatocellular carcinoma (HCC) is effective and safe is still unknown. Objective: To investigate the feasibility of SBRT before deceased donor liver transplant (DDLT) for previously untreated unresectable HCC. Design, Setting, and Participants: In this phase 2 nonrandomized controlled trial conducted between June 1, 2015, and October 18, 2019, 32 eligible patients within UCSF (University of California, San Francisco) criteria underwent dual-tracer (18F-fluorodeoxyglucose and 11C-acetate [ACC]) positron emission tomography with computed tomography (PET-CT) and magnetic resonance imaging (MRI) with gadoxetate followed by SBRT of 35 to 50 Gy in 5 fractions, and the same imaging afterward while awaiting DDLT. Statistical analysis was performed on an intention-to-treat basis between October 1 and 31, 2023. Intervention: Patients received SBRT followed by DDLT when matched deceased donor grafts were available. Main Outcomes and Measures: Coprimary end points were progression-free survival (PFS) and objective response rates (ORRs) by the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1), modified RECIST (mRECIST), and PET Response Criteria in Solid Tumors (PERCIST). Secondary end points were local control rate, overall survival (OS), and safety. Results: A total of 32 patients (median age, 59 years [IQR, 54-63 years]; 22 men [68.8%]) with 56 lesions received SBRT. After a median follow-up of 74.6 months (IQR, 40.1-102.9 months), the median PFS was 17.6 months (95% CI, 6.6-28.6 months), and the median OS was 60.5 months (95% CI, 29.7-91.2 months). The 5-year PFS was 39.9% (95% CI, 19.9%-59.9%), and the 5-year OS was 51.3% (95% CI, 31.7%-70.9%). In terms of number of patients, ORRs were 62.5% ([n = 20] 95% CI, 54.2%-68.7%) by RECIST 1.1, 71.9% ([n = 23] 95% CI, 63.7%-79.0%) by mRECIST, and 78.1% ([n = 25] 95% CI, 73.2%-86.7%) by PERCIST. In terms of number of lesions, ORRs were 75.0% ([n = 42] 95% CI, 61.6%-80.8%) by RECIST 1.1, 83.9% ([n = 47] 95% CI, 74.7%-90.6%) by mRECIST, and 87.5% ([n = 49] 95% CI, 81.3%-98.6%) by PERCIST. Twenty patients with 36 lesions received DDLT, of whom 15 patients (75.0%) with 21 lesions (58.3%) exhibited pathologic complete response. Multivariable analyses revealed that pretreatment metabolic tumor volume (MTV) based on ACC (hazard ratio [HR], 1.06 [95% CI, 1.01-1.10]; P = .01) and complete metabolic response (CMR) by PERCIST (HR, 0.31 [95% CI, 0.10-0.96]; P = .04) were associated with PFS, while pretreatment MTV based on ACC (HR, 1.07 [95% CI, 1.03-1.16]; P = .01), total lesion activity based on ACC (HR, 1.01 [95% CI, 1.00-1.02]; P = .02), and CMR by PERCIST (HR, 0.21 [95% CI, 0.07-0.73]; P = .01) were associated with OS. Toxic effects associated with SBRT were reported for 9 patients (28.1%), with 1 grade 3 event. Conclusions and Relevance: This phase 2 nonrandomized controlled trial demonstrated promising survival and safety outcomes of SBRT before DDLT for unresectable HCC. Future randomized clinical trials are warranted.
Asunto(s)
Carcinoma Hepatocelular , Neoplasias Hepáticas , Trasplante de Hígado , Radiocirugia , Humanos , Radiocirugia/métodos , Masculino , Neoplasias Hepáticas/radioterapia , Neoplasias Hepáticas/cirugía , Femenino , Persona de Mediana Edad , Carcinoma Hepatocelular/cirugía , Carcinoma Hepatocelular/radioterapia , Carcinoma Hepatocelular/mortalidad , Anciano , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Supervivencia sin ProgresiónAsunto(s)
Insuficiencia Cardíaca , Volumen Sistólico , Humanos , Insuficiencia Cardíaca/diagnóstico por imagen , Insuficiencia Cardíaca/fisiopatología , Volumen Sistólico/fisiología , Masculino , Femenino , Persona de Mediana Edad , Anciano , Imagen por Resonancia Magnética/métodos , Medios de Contraste , Imagen por Resonancia Cinemagnética/métodosRESUMEN
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.
RESUMEN
Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously. We found several significant associations with traditional measurements such as abdominal obesity, as defined by waist-to-hip ratio (OR = 2.50 (male), 3.35 (female)), android-gynoid ratio (OR = 3.35 (male), 6.39 (female)) and waist circumference (OR = 1.79 (male), 3.80 (female)) with hepatic steatosis. Similarly, A Body Shape Index (Quantile 4 OR = 1.89 (male), 5.81 (female)), and for fat mass index, both overweight (OR = 6.93 (male), 2.83 (female)) and obese (OR = 14.12 (male), 5.32 (female)) categories were likewise significantly associated with hepatic steatosis. DXA parameters were shown to be highly associated such as visceral adipose tissue mass (OR = 8.37 (male), 19.03 (female)), trunk fat mass (OR = 8.64 (male), 25.69 (female)) and android fat mass (OR = 7.93 (male), 21.77 (female)) with NAFLD. We trained machine learning classifiers with logistic regression and two histogram-based gradient boosting ensembles for the prediction of hepatic steatosis using traditional body composition indices and DXA parameters which achieved reasonable performance (AUC = 0.83-0.87). Based on SHapley Additive exPlanations (SHAP) analysis, DXA parameters that had the largest contribution to the classifiers were the features predicted with significant association with NAFLD. Overall, this study underscores the potential utility of DXA as a practical and potentially opportunistic method for the screening of hepatic steatosis.
RESUMEN
The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.
Asunto(s)
Bancos de Muestras Biológicas , Biobanco del Reino Unido , Humanos , Estudios Prospectivos , Estudios de Cohortes , Espectroscopía de Resonancia Magnética , Biomarcadores , EnvejecimientoRESUMEN
PURPOSE: Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N8th) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort. METHODS & MATERIALS: Pre-treatment MRI of 750 patients from the internal cohort were retrospectively reviewed. Predictive values of six modified nodal staging systems that incorporated four patterns of ENE and two patterns of CNN to the current stage N3 for disease-free survival (DFS) were compared with that of N8th using multivariate cox-regression and concordance statistics in the internal cohort. Performance of stage N3 for predicting disease recurrence was calculated. An external cohort of 179 patients was used to validate the findings. RESULTS: Incorporation of advanced ENE, which infiltrates into adjacent muscle/skin/salivary glands outperformed the other five modifications for predicting outcomes (p < 0.01) and achieved a significantly higher c-index for 5-year DFS (0.69 vs 0.72) (p < 0.01) when compared with that of N8th staging system. By adding advanced ENE to the current N3 increased the sensitivity for predicting disease recurrence from 22.4 % to 47.1 %. The finding was validated in the external cohort (5-year DFS 0.65 vs. 0.72, p < 0.01; sensitivity of stage N3 increased from 14.0 % to 41.9 % for disease recurrence). CONCLUSION: Results from two centre cohorts confirmed that the radiological advanced ENE should be considered as a criterion for stage N3 disease in NPC.
Asunto(s)
Extensión Extranodal , Neoplasias Nasofaríngeas , Humanos , Carcinoma Nasofaríngeo/diagnóstico por imagen , Carcinoma Nasofaríngeo/patología , Estudios Retrospectivos , Extensión Extranodal/patología , Estadificación de Neoplasias , Recurrencia Local de Neoplasia/patología , Pronóstico , Neoplasias Nasofaríngeas/diagnóstico por imagen , Neoplasias Nasofaríngeas/radioterapia , Ganglios Linfáticos/diagnóstico por imagen , Ganglios Linfáticos/patologíaRESUMEN
Due to the adverse effects of de-metallation in past concerning FDA-approved gadolinium-based contrast agents (GBCAs), researchers have been focusing on developing safer and more efficient alternatives that could avoid toxicity caused by free gadolinium ions. Herein, two chiral GBCAs, Gd-LS with sulfonate groups and Gd-T with hydroxyl groups, are reported as potential candidates for magnetic reasonance imaging (MRI). The r1 relaxivities of TSAP, SAP isomers of Gd-LS and SAP isomer of Gd-T at 1.4 T, 37 °C in water are 7.4 mM-1s-1, 14.5 mM-1s-1 and 5.2 mM-1s-1, respectively. Results show that the hydrophilic functional groups introduced to the chiral macrocyclic scaffold of Gd-T and Gd-LS both give constructive influences on the second-sphere relaxivity and enhance the overall r1 value. Both cases indicate that the design of GBCAs should also focus on the optimal window in Solomon-Bloembergen-Morgan (SBM) theory and the effects caused by the second-sphere and outer-sphere relaxivity.
RESUMEN
The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification. Advanced photon-counting detector CT (PCD-CT) possesses high spectral sensitivity and material decomposition capabilities to simultaneously determine qualitative and quantitative information. In this study, we explored the diagnostic utility of PCD-CT to provide high-resolution 3-D imaging of bone microarchitecture and composition for the sensitive diagnosis of bone in untreated and ovariectomized rats. PCD-CT accurately decomposed the calcium content within hydroxyapatite phantoms (r = 0.99). MicroCT analysis of tibial bone revealed significant differences in the morphological parameters between the untreated and ovariectomized samples. However, differences in the structural parameters of the mandible between the treatment groups were not observed. BMD determined with microCT and calcium concentration decomposed using PCD-CT differed significantly between the treatment groups in both the tibia and mandible. Quantitative analysis with PCD-CT is sensitive in determining the distribution of calcium and water components in bone and may have utility in the screening and diagnosis of bone conditions such as osteoporosis.
RESUMEN
Background and Purpose: Physiological changes in tumour occur much earlier than morphological changes. They can potentially be used as biomarkers for therapeutic response prediction. This study aimed to investigate the optimal time for early therapeutic response prediction with multi-parametric magnetic resonance imaging (MRI) in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemo-radiotherapy (CCRT). Material and Methods: Twenty-seven NPC patients were divided into the responder (N = 23) and the poor-responder (N = 4) groups by their primary tumour post-treatment shrinkages. Single-voxel proton MR spectroscopy (1H-MRS), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI were scanned at baseline, weekly during CCRT and post-CCRT. The median choline peak in 1H-MRS, the median apparent diffusion coefficient (ADC) in DW-MRI, the median influx rate constant (Ktrans), reflux rate constant (Kep), volume of extravascular-extracellular space per unit volume (Ve), and initial area under the time-intensity curve for the first 60 s (iAUC60) in DCE-MRI were compared between the two groups with the Mann-Whitney tests for any significant difference at different time points. Results: In DW-MRI, the percentage increase in ADC from baseline to week-1 for the responders (median = 11.39%, IQR = 18.13%) was higher than the poor-responders (median = 4.91%, IQR = 7.86%) (p = 0.027). In DCE-MRI, the iAUC60 on week-2 was found significantly higher in the poor-responders (median = 0.398, IQR = 0.051) than the responders (median = 0.192, IQR = 0.111) (p = 0.012). No significant difference was found in median choline peaks in 1H-MRS at all time points. Conclusion: Early perfusion and diffusion changes occurred in primary tumours of NPC patients treated with CCRT. The DW-MRI on week-1 and the DCE-MRI on week-2 were the optimal time points for early therapeutic response prediction.
RESUMEN
Background: In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment. Aims: Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare. Methods: We designed the study in two parts. First, we performed an intra-individual comparison of the 4558 participants from the UK Biobank cohorts with matching data of MRI abdominal body composition, DXA with VAT estimation, and BIA. Second, we evaluated 174 CT scans from the publicly available dataset to assess the correlation of the commonly used single-slice technique compared to three-dimensional VAT volume. Results: Across the UK Biobank cohort, the DXA-derived VAT measurement correlated better (R2 0.94, p<0.0001) than BIA (R2 0.49, p<0.0001) with reference three-dimensional volume on MRI. However, DXA-derived VAT correlation was worse for participants with a BMI of < 20 (R2 = 0.62, p=0.0013). A commonly used single slice method on CT demonstrated a modest correlation (R2 between 0.51 - 0.64), with best values at L3- and L4 (R2 L3 = 0.63, p<0.0001; L4 = 0.64, p<0.0001) compared to reference three-dimensional volume. Combining multiple slices yielded a better correlation, with a strong correlation when L2-L3 levels were combined (R2 = 0.92, p<0.0001). Conclusion: When deployed at scale, DXA-derived VAT volume measurement shows excellent correlation with three-dimensional volume on MRI based on the UK Biobank cohort. Whereas a single slice CT technique demonstrated moderate correlation with three-dimensional volume on CT, with a stronger correlation achieved when multiple levels were combined.
Asunto(s)
Grasa Intraabdominal , Obesidad , Humanos , Grasa Intraabdominal/diagnóstico por imagen , Absorciometría de Fotón/métodos , Impedancia Eléctrica , Tomografía Computarizada por Rayos XRESUMEN
Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging. Intraventricular four-dimensional flow (4D flow) phase-contrast cardiovascular magnetic resonance (CMR) can assess different components of left ventricular (LV) flow including direct flow, delayed ejection, retained inflow and residual volume. This could be utilised to identify HFpEF. This study investigated if intraventricular 4D flow CMR could differentiate HFpEF patients from non-HFpEF and asymptomatic controls. Suspected HFpEF patients and asymptomatic controls were recruited prospectively. HFpEF patients were confirmed using European Society of Cardiology (ESC) 2021 expert recommendations. Non-HFpEF patients were diagnosed if suspected HFpEF patients did not fulfil ESC 2021 criteria. LV direct flow, delayed ejection, retained inflow and residual volume were obtained from 4D flow CMR images. Receiver operating characteristic (ROC) curves were plotted. 63 subjects (25 HFpEF patients, 22 non-HFpEF patients and 16 asymptomatic controls) were included in this study. 46% were male, mean age 69.8 ± 9.1 years. CMR 4D flow derived LV direct flow and residual volume could differentiate HFpEF vs combined group of non-HFpEF and asymptomatic controls (p < 0.001 for both) as well as HFpEF vs non-HFpEF patients (p = 0.021 and p = 0.005, respectively). Among the 4 parameters, direct flow had the largest area under curve (AUC) of 0.781 when comparing HFpEF vs combined group of non-HFpEF and asymptomatic controls, while residual volume had the largest AUC of 0.740 when comparing HFpEF and non-HFpEF patients. CMR 4D flow derived LV direct flow and residual volume show promise in differentiating HFpEF patients from non-HFpEF patients.
RESUMEN
INTRODUCTION: The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West. METHOD: A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population. RESULTS: Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment. CONCLUSIONS: Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.
Asunto(s)
Neoplasias Pulmonares , Humanos , Persona de Mediana Edad , Anciano , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/epidemiología , Detección Precoz del Cáncer/métodos , Consenso , Tomografía Computarizada por Rayos X/métodos , Asia/epidemiología , Tamizaje MasivoRESUMEN
Aims: Heart failure with preserved ejection fraction (HFpEF) continues to be a diagnostic challenge. Cardiac magnetic resonance atrial measurement, feature tracking (CMR-FT), tagging has long been suggested to diagnose HFpEF and potentially complement echocardiography especially when echocardiography is indeterminate. Data supporting the use of CMR atrial measurements, CMR-FT or tagging, are absent. Our aim is to conduct a prospective case-control study assessing the diagnostic accuracy of CMR atrial volume/area, CMR-FT, and tagging to diagnose HFpEF amongst patients suspected of having HFpEF. Methods and results: One hundred and twenty-one suspected HFpEF patients were prospectively recruited from four centres. Patients underwent echocardiography, CMR, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements within 24â h to diagnose HFpEF. Patients without HFpEF diagnosis underwent catheter pressure measurements or stress echocardiography to confirm HFpEF or non-HFpEF. Area under the curve (AUC) was determined by comparing HFpEF with non-HFpEF patients. Fifty-three HFpEF (median age 78 years, interquartile range 74-82 years) and thirty-eight non-HFpEF (median age 70 years, interquartile range 64-76 years) were recruited. Cardiac magnetic resonance left atrial (LA) reservoir strain (ResS), LA area index (LAAi), and LA volume index (LAVi) had the highest diagnostic accuracy (AUCs 0.803, 0.815, and 0.776, respectively). Left atrial ResS, LAAi, and LAVi had significantly better diagnostic accuracy than CMR-FT left ventricle (LV)/right ventricle (RV) parameters and tagging (P < 0.01). Tagging circumferential and radial strain had poor diagnostic accuracy (AUC 0.644 and 0.541, respectively). Conclusion: Cardiac magnetic resonance LA ResS, LAAi, and LAVi have the highest diagnostic accuracy to identify HFpEF patients from non-HFpEF patients amongst clinically suspected HFpEF patients. Cardiac magnetic resonance feature tracking LV/RV parameters and tagging had low diagnostic accuracy to diagnose HFpEF.
RESUMEN
Background: Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. Methods: In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity. Results: Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. Conclusions: To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.
RESUMEN
MRI is the primary imaging approach for diagnosing prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability. In this study, we proposed a novel multi-branch network, MiniSegCaps, for prostate cancer segmentation and PI-RADS classification on mpMRI. MiniSeg branch outputted the segmentation in conjunction with PI-RADS prediction, guided by the attention map from the CapsuleNet. CapsuleNet branch exploited the relative spatial information of prostate cancer to anatomical structures, such as the zonal location of the lesion, which also reduced the sample size requirement in training due to its equivariance properties. In addition, a gated recurrent unit (GRU) is adopted to exploit spatial knowledge across slices, improving through-plane consistency. Based on the clinical reports, we established a prostate mpMRI database from 462 patients paired with radiologically estimated annotations. MiniSegCaps was trained and evaluated with fivefold cross-validation. On 93 testing cases, our model achieved a 0.712 dice coefficient on lesion segmentation, 89.18% accuracy, and 92.52% sensitivity on PI-RADS classification (PI-RADS ≥ 4) in patient-level evaluation, significantly outperforming existing methods. In addition, a graphical user interface (GUI) integrated into the clinical workflow can automatically produce diagnosis reports based on the results from MiniSegCaps.
RESUMEN
Accurate diagnosis of minor cartilage injuries with delayed contrast-enhanced computed tomography (CECT) is challenging as poor diffusion and toxicity issues limit the usage of common CT contrast agents. Hence, the design of safe contrast agents with physiochemical properties suitable for fast, deep cartilage imaging is imminent. Herein, a novel cationic bismuth contrast agent (Bi-DOTAPXD) based on dodecane tetraacetic acid (DOTA) was synthesized and examined for CECT of cartilage. The complex was designed to improve diagnosis by utilising a net-positive charge for enhanced permeability through cartilage, inherent low-toxicity and high X-ray attenuation of bismuth. Osteochondral plugs (n = 12), excised from visually intact porcine articular cartilage were immersed in Bi-DOTAPXD (8 mg/mL) and Gd-DOTAPXD (10 mg/mL) contrast agents and scanned with a high-resolution microcomputed tomography scanner at multiple time-points. The mean Bi-DOTAPXD and Gd-DOTAPXD partitions at 45-min time-point were 85.7 ± 35.1 and 69.8 ± 30.2%, and the partitions correlated with the histopathological analysis of cartilage proteoglycan (PG) content (r) at 0.657 and 0.632, respectively. The time diffusion constants (τ) for Bi-DOTAPXD and Gd-DOTA were 121 and 159 min, respectively. Diffusion Bi-DOTAPXD and Gd-DOTAPXD reflected inter-sample variation in cartilage PG content. Cationic Bi-DOTAPXD may have the potential as a CT agent for the diagnosis of cartilage.
Asunto(s)
Cartílago Articular , Medios de Contraste , Animales , Porcinos , Medios de Contraste/química , Microtomografía por Rayos X/métodos , Bismuto , Cartílago Articular/diagnóstico por imagen , Cartílago Articular/patología , ProteoglicanosRESUMEN
This study proposed a semisupervised loss function named level-set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid-attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V-Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU-SVD, n = 360) and the multiple sclerosis cohort (HKU-MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer-assisted Intervention (MICCAI) WMH challenge database (MICCAI-WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI-CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU-SVD testing set (n = 20), DSC = 0.77 on the HKU-MS testing set (n = 5), and DSC = 0.78 on MICCAI-WMH testing set (n = 30). The segmentation results obtained by our semisupervised V-Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.
Asunto(s)
Enfermedad de Alzheimer , Sustancia Blanca , Humanos , Sustancia Blanca/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Neuroimagen , Cráneo , Procesamiento de Imagen Asistido por Computador/métodos , Encéfalo/diagnóstico por imagenRESUMEN
Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.