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OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. RESULTS: Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. CONCLUSIONS: DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.
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PURPOSE: To investigate the enhancement of image quality achieved through the utilization of SnapShot Freeze 2 (SSF2), a comparison was made against the results obtained from the original SnapShot Freeze algorithm (SSF) and standard motion correction (STND) in stent patients undergoing coronary CT angiography (CCTA) across the entire range of heart rates. MATERIALS AND METHODS: A total of 118 patients who underwent CCTA, were retrospectively included in this study. Images of these patients were reconstructed using three different algorithms: SSF2, SSF, and STND. Objective assessments include signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), diameters of stents and artifact index (AI). The image quality was subjectively evaluated by two readers. RESULTS: Compared with SSF and STND, SSF2 had similar or even higher quality in the parameters (AI, SNR, CNR, inner diameters) of coronary artery, stent, myocardium, MV (mitral valve), TV (tricuspid valve), AV (aorta valve), and PV (pulmonary valve), and aortic root (AO). Besides the above structures, SSF2 also demonstrated comparable or even higher subjective scores in atrial septum (AS), ventricular septum (VS), and pulmonary artery root (PA). Furthermore, the enhancement in image quality with SSF2 was significantly greater in the high heart rate group compared to the low heart rate group. Moreover, the improvement in both high and low heart rate groups was better in the SSF2 group compared to the SSF and STND group. Besides, when using the three algorithms, an effect of heart rate variability on stent image quality was not detected. CONCLUSION: Compared to SSF and STND, SSF2 can enhance the image quality of whole-heart structures and mitigate artifacts of coronary stents. Furthermore, SSF2 has demonstrated a significant improvement in the image quality for patients with a heart rate equal to or higher than 85 bpm.
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Algoritmos , Angiografia por Tomografia Computadorizada , Angiografia Coronária , Frequência Cardíaca , Processamento de Imagem Assistida por Computador , Razão Sinal-Ruído , Stents , Humanos , Angiografia por Tomografia Computadorizada/métodos , Estudos Retrospectivos , Angiografia Coronária/métodos , Frequência Cardíaca/fisiologia , Feminino , Masculino , Pessoa de Meia-Idade , Processamento de Imagem Assistida por Computador/métodos , Idoso , Artefatos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Adulto , Vasos Coronários/diagnóstico por imagemRESUMO
INTRODUCTION: Observational studies have found that most patients with arthritis have depression. We aimed to determine the causal relationship between various types of arthritis and depression. METHODS: We conducted a two-sample bidirectional Mendelian randomized (MR) analysis to determine whether there was a significant causal relationship between depression and multiple types of arthritis. The data of our study were derived from the publicly released genome-wide association studies (GWASs) and the largest GWAS meta-analysis. MR analysis mainly used inverse-variance weighted method; supplementary methods included weighted median, weighted mode, and MR-Egger using MR pleiotropy residual sum and outlier to detect and correct for the presence of pleiotropy. RESULTS: After adjusting for heterogeneity and horizontal pleiotropy, we found that depression was associated with an increased risk of osteoarthritis (OA) (OR = 1.02, 95%CI: 1.01-1.02, p = 2.96 × E - 5). In the reverse analysis, OA was also found to increase the risk of depression (OR = 1.10, 95%CI: 1.04-1.15, p = .0002). Depression only increased the risk of knee OA (KOA) (OR = 1.25, 95%CI: 1.10-1.42, p = 6.46 × E - 4). Depression could potentially increase the risk of spondyloarthritis (OR = 1.52, 95%CI: 1.19-1.94, p ≤ 8.94 × E - 4). CONCLUSION: There is a bidirectional causal relationship of depression with OA. However, depression only augments the risk of developing KOA. Depression may increase the risk of spondyloarthritis and gout.
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Depressão , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Osteoartrite , Humanos , Análise da Randomização Mendeliana/métodos , Depressão/genética , Depressão/epidemiologia , Osteoartrite/genética , Osteoartrite/epidemiologia , Osteoartrite do Joelho/genética , Osteoartrite do Joelho/epidemiologia , Artrite/genética , Artrite/epidemiologia , Artrite Reumatoide/genética , Artrite Reumatoide/epidemiologia , Gota/genética , Gota/epidemiologia , Fatores de Risco , Espondilartrite/genéticaRESUMO
Purpose: We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods: A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results: Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions: MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.
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RATIONALE AND OBJECTIVES: The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS: This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS: Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION: Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adenocarcinoma de Pulmão/diagnóstico por imagem , Adenocarcinoma de Pulmão/patologia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Gradação de Tumores , Nomogramas , Tomografia Computadorizada por Raios X , Período Pré-OperatórioRESUMO
Purpose: This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses. Methods: Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis. Results: A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D * (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in "nodules or masses" than that in "nodules." Conclusions: The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664.
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BACKGROUND: The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions. METHODS: In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0-600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves. RESULTS: There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29. CONCLUSIONS: A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.