scispace - formally typeset
Search or ask a question

Showing papers by "Qi Zhang published in 2020"


Journal ArticleDOI
TL;DR: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.
Abstract: Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

161 citations


Journal ArticleDOI
Yi Dong1, Liu Zhou, Wei Xia, Xingyu Zhao, Qi Zhang1, Junming Jian, Xin Gao, Wenping Wang1 
TL;DR: The authors' radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients and may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.
Abstract: Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross- and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

38 citations


Journal ArticleDOI
Yijie Qiu1, Daohui Yang1, Qi Zhang1, Kailing Chen1, Yi Dong1, Wen-Ping Wang1 
TL;DR: V Flow measurement is a simple, rapid and feasible imaging method for the WSS assessment of CCA in healthy volunteers, which will probably be an important tool for assessing CCA function.
Abstract: OBJECTIVE To evaluate the feasibility of vector flow imaging technique (V Flow) in measurement of wall shear stress (WSS) of common carotid arteries (CCA) in healthy adults and to provide the normal WSS values assessed by V Flow. METHODS & MATERIALS This prospective study was approved by the Ethics Committee of our University. Eighty healthy adult volunteers were included (mean age 43.3 y, 47 females, 33 males). The volunteers were classified into three groups according to their age: group I (age 20 - 39 y), group II (age 40 - 59 y) and group III (age 60 - 80 y). Mindray Resona 8 ultrasound machine and a linear array transducer (3-9 MHz) was used, equipped with the updated V Flow function. Common carotid arteries of both sides were evaluated in three segments (initial segment, middle segment and near bifurcation segment). The WSS values of CCA were measured by two independent radiologists. The intraclass correlation coefficient (ICC) of observer reliability in WSS measurement was calculated. Inter-observer reproducibility was also evaluated with the 95% Bland-Altman limits of agreement (LOA). RESULTS V Flow measurements were performed successfully in 79 volunteers (98.8 %, 79/80). The mean value of WSS in right CCA was (0.66±0.24) Pa, in left CCA was (0.66±0.18) Pa (P > 0.05). Mean WSS value had a moderately negative correlation with age group (P < 0.05). The mean WSS value of group I(mean±SD, 0.75±0.25 Pa) is larger than group II (mean±SD, 0.62±0.13 Pa) and group III (mean±SD, 0.49±0.11 Pa) (P < 0.05). The ICC of observer reliability of group I, II and III was 0.96 (95% confidence interval (95% CI) 0.92-0.98), 0.94 (95% CI 0.88-0.97), 0.93 (95% CI 0.76-0.98) respectively. The Bland-Altman plots showed that the 95% LOA were -0.17-0.12 (Pa) for group I, -0.09-0.13 (Pa) for group II and -0.08-0.10 (Pa) for group III. CONCLUSION V Flow measurement is a simple, rapid and feasible imaging method for the WSS assessment of CCA in healthy volunteers, which will probably be an important tool for assessing CCA function.

15 citations


Journal ArticleDOI
Yi Dong1, Lingxiao Liu1, Qiong Cao1, Qi Zhang1, Yijie Qiu1, Daohui Yang1, Lingyun Yu1, Wen-Ping Wang1 
TL;DR: With its superior contrast resolution, CEUS performed with high frequency transducers is helpful to achieve better visualization of gallbladder fundus and make differential diagnosis ofGallbladder lesions, which might greatly improve diagnostic confidence between malignant and benign FGL.
Abstract: Background and aim To evaluate the benefits of contrast-enhanced ultrasound (CEUS) with high frequency transducers in characterization of focal gallbladder lesions (FGL). Material and methods From January 2017 to April 2019, 59 FGL detected by B mode ultrasound (BMUS) were examined, first with the low frequency convex transducer (1-5 MHz) and afterwards with high frequency transducer (7.5-12 MHz). High frequency dynamic CEUS were applied after bolus injection of 4.8 ml Sulphur hexafluoride microbubbles (SonoVue®, Milan). The BMUS and CEUS imaging features were recorded and compared. All lesions were confirmed by surgical resection and histopathologic results. Results The final diagnoses of 59 FGL included gallbladder adenocarcinoma (n = 15), gallbladder polyps (n = 11), gallbladder adenomas (n = 18), focal adenomyomatosis (n = 9), and gallbladder Ascariasis debris (n = 6). The mean diameter of FGL was 24.5±11.4 mm, and mean depth to the abdominal wall was 21.2±7.3 mm. While applying CEUS with high frequency transducer, specific diagnostic features, including arterial phase irregular intralesional vascularity (10/15, 66.7%), late phase hypoenhancement (12/15, 80%), destruction of gallbladder wall (8/15, 53.3%), infiltration to the adjacent liver (6/15, 40.0%) were significantly higher in malignant FGL. The overall sensitivity, specificity and diagnostic accuracy for the correct characterization of malignant FGL were significantly improved by CEUS with high frequency transducer (sensitivity 93.3%, specificity 88.5%, accuracy 100%). Conclusion With its superior contrast resolution, CEUS performed with high frequency transducers is helpful to achieve better visualization of gallbladder fundus and make differential diagnosis of gallbladder lesions, which might greatly improve diagnostic confidence between malignant and benign FGL.

13 citations


Journal ArticleDOI
TL;DR: The results demonstrate that multimodal feature learning and fusion using the PGBM-RBM2 can assist in the diagnosis of PCa.
Abstract: B-mode ultrasonography and sonoelastography are used in the clinical diagnosis of prostate cancer (PCa). A combination of the two ultrasound (US) modalities using computer aid may be helpful for improving the diagnostic performance. A technique for computer-aided diagnosis (CAD) of PCa is presented based on multimodal US. Firstly, quantitative features are extracted from both B-mode US images and sonoelastograms, including intensity statistics, regional percentile features, gray-level co-occurrence matrix (GLCM) texture features and binary texture features. Secondly, a deep network named PGBM-RBM2 is proposed to learn and fuse multimodal features, which is composed of the point-wise gated Boltzmann machine (PGBM) and two layers of the restricted Boltzmann machines (RBMs). Finally, the support vector machine (SVM) is used for prostatic disease classification. Experimental evaluation was conducted on 313 multimodal US images of the prostate from 103 patients with prostatic diseases (47 malignant and 56 benign). Under five-fold cross-validation, the classification sensitivity, specificity, accuracy, Youden's index and area under the receiver operating characteristic (ROC) curve with the PGBM-RBM2 were 87.0%, 88.8%, 87.9%, 75.8% and 0.851, respectively. The results demonstrate that multimodal feature learning and fusion using the PGBM-RBM2 can assist in the diagnosis of PCa. This deep network is expected to be useful in the clinical diagnosis of PCa.

12 citations


Journal ArticleDOI
TL;DR: An improved DPN algorithm with enhanced performance on both feature representation and classification is proposed, and the proposed D-P-EKN-DPN algorithm has a great potential in TCS-based CAD for PD due to its excellent performance.
Abstract: Transcranial sonography (TCS) is a valid neuroimaging tool for the diagnosis of Parkinson’s disease (PD). The TCS-based computer-aided diagnosis (CAD) has attracted increasing attention in recent years, in which feature representation and pattern classification are two critical issues. Deep polynomial network (DPN) is a newly proposed deep learning algorithm that has shown its advantage in learning effective feature representation for samples with a small size. In this work, an improved DPN algorithm with enhanced performance on both feature representation and classification is proposed. First, the empirical kernel mapping (EKM) algorithm is embedded into DPN (EKM-DPN) to improve its feature representation. Second, the network pruning strategy is utilized in the EKM-DPN (named P-EKM-DPN). It not only produces robust feature representation, but also addresses the overfitting issues for the subsequent classifiers to some extent. Lastly, the generalization ability is further enhanced by applying the Dropout approach to P-EKM-DPN (D-P-EKM-DPN). The proposed D-P-EKM-DPN algorithm has been evaluated on a TCS dataset with 153 samples. The experimental results indicate that D-P-EKM-DPN outperforms all the compared algorithms and achieves the best classification accuracy, sensitivity, and specificity of 86.95 ± 3.15%, 85.77 ± 7.87%, and 87.16 ± 6.50%, respectively. The proposed D-P-EKN-DPN algorithm has a great potential in TCS-based CAD for PD due to its excellent performance.

12 citations


Journal ArticleDOI
TL;DR: It is proved that the performance of the BUS-based CAD can be significantly improved by transferring the knowledge of EUS, and suggests that CW-PM-DNN has the potential for more applications in the field of medical image–based CAD.
Abstract: Elastography ultrasound (EUS) imaging has shown its effectiveness for diagnosis of tumors by providing additional information about tissue stiffness to the conventional B-mode ultrasound (BUS). However, due to the lack of EUS devices and experienced sonologists, EUS is not widely used, especially in rural areas. It is still a challenging task to improve the performance of the single-modal BUS-based computer-aided diagnosis (CAD) for tumors. In this work, we propose a novel transfer learning (TL)–based deep neural network (DNN) algorithm, named CW-PM-DNN, for the BUS-based CAD by transferring diagnosis knowledge from EUS during model training. CW-PM-DNN integrates both the feature-level and classifier-level knowledge transfer into a unified framework. In the feature-level TL, a bichannel DNN is learned by the cross-weight-based multimodal DL (MDL-CW) algorithm to transfer informative features from EUS to BUS. In the classifier-level TL, a projective model (PM)–based classifier is then embedded to the pretrained bichannel DNN to implement the parameter transfer in the classifier model at the second stage. The back-propagation procedure is then applied to optimize the whole CW-PM-DNN to further improve its performance. Experimental results on two bimodal ultrasound tumor datasets demonstrate that the proposed CW-PM-DNN achieves the best classification accuracy, sensitivity, and specificity of 89.02 ± 1.54%, 88.37 ± 4.72%, and 89.63 ± 4.06%, respectively, for the breast ultrasound dataset, and the corresponding values of 80.57 ± 3.41%, 76.67 ± 3.85%, and 83.94 ± 3.95%, respectively, for the prostate ultrasound dataset. The proposed two-stage TL-based CW-PM-DNN algorithm outperforms all the compared algorithms. It is also proved that the performance of the BUS-based CAD can be significantly improved by transferring the knowledge of EUS. It suggests that CW-PM-DNN has the potential for more applications in the field of medical image–based CAD.

12 citations


Journal ArticleDOI
Yi Dong1, Ben-hua Xu1, Qiong Cao1, Qi Zhang1, Yijie Qiu1, Daohui Yang1, Lingyun Yu1, Wen-Ping Wang1 
TL;DR: CEUS performed with high frequency linear transducers could be a useful alternative in the differential diagnosis of focal fundal GB wall thickening on conventional ultrasound.
Abstract: AIM To investigate the value of contrast enhanced ultrasound with high resolution linear transducers (HF-CEUS) for differential diagnosis of focal fundal gallbladder (GB) wall thickening. METHODS A total of 32 patients with incidentally detected focal fundal GB wall thickening were included. After conventional B mode ultrasound (BMUS) examinations, HF-CEUS were performed with a 7.5-12 MHz 9L4 linear transducer (S2000 HELX OXANA unit, Siemens). Two radiologists independently reviewed the HF-CEUS enhancement patterns to determine the differential features between malignancy and benignity with a five-point confidence scale. The diagnostic accuracy of BMUS and HF-CEUS for GB wall thickening was compared. The final gold standard was surgery with histological examination. RESULTS Final diagnoses included GB adenocarcinoma (n = 16), adenomyomatosis (n = 12), Xanthogranulomatous (n = 2) and cholecystitis (n = 2). HF-CEUS features associated with GB adenocarcinoma including arterial phase inhomogeneous hyperenhancement, venous phase hypoenhancement and disruption of GB wall layer structure (P < 0.05). Two small (5 mm) liver metastasis were confirmed by HF-CEUS during the late phase liver sweep as hypoenhanced lesions. Nonenhanced Rokitansky-Aschoff sinuses were clearly observed in 83.3% focal adenomyomatosis. Overall sensitivity, specificity and accuracy for differentiation between malignant and benign focal fundal GB wall thickening of HF-CEUS and BMUS were 84.3% vs 53.1%, 90.6% vs 59.3% and 87.5% vs 56.2% (P < 0.005). CONCLUSIONS CEUS performed with high frequency linear transducers could be a useful alternative in the differential diagnosis of focal fundal GB wall thickening on conventional ultrasound.

9 citations


Journal ArticleDOI
Qi Zhang1, Lili Wu1, Daohui Yang, Yijie Qiu1, Lingyun Yu, Yi Dong1, Wen-Ping Wang1 
TL;DR: Depending on its unique advantages as non-radiation, effective and convenient, D-CEUS analysis and quantitative parameters, particularly MI, has potential application value in following up of the CRT treatment response in LAPC patients.
Abstract: OBJECTIVES To investigate the value of dynamic contrast enhanced ultrasound (D-CEUS) in monitoring the chemoradiotherapy (CRT) therapeutic response of local advanced pancreatic ductal adenocarcinoma (LAPC). PATIENTS AND METHODS From October 2017 to December 2018, 11 patients diagnosed as LAPC were included (7 men, 4 women; mean age: 61.1±8.6 years). The algorithm of CRT was as following: the radiotherapy dose was 50.4 Gy/28Fx with S-1 40 mg bid orally taken in radiotherapy day. Conventional ultrasound scan and CEUS were performed before and 4 weeks after CRT. All ultrasound examinations were performed by an ACUSON Oxana 2 ultrasound equipment (Siemens Medical Solutions, Germany) with a C 6-1 convex array transducer (1-6 MHz). Time intensity curves (TICs) were generated in the region of interests (ROIs) both in LAPC lesions and in its surrounding pancreas parenchyma by SonoLiver software (TOMTEC Imaging Systems). Quantitative perfusion parameters including maximum intensity (MI), rise time (RT), mean transit time (mTT) and time to peak (TTP) were analyzed and compared before and after CRT. RESULTS No significant difference could be found by conventional B mode ultrasound scan after CRT. TICs of CEUS showed lower ascending and descending slopes rate after CRT. Among all perfusion quantitative parameters, MI decreased significantly after CRT (42.1±18.8% vs 27.8±17.2%, P < 0.05). CONCLUSIONS Depending on its unique advantages as non-radiation, effective and convenient, D-CEUS analysis and quantitative parameters, particularly MI, has potential application value in following up of the CRT treatment response in LAPC patients.

7 citations


Journal ArticleDOI
TL;DR: Ultrasound radiomic analysis based on the spatial and morphological features extracted from ultrasound images effectively contributed to the preoperative diagnosis of true and pseudo gallbladder polyps and may be valuable in their clinical management.
Abstract: Purpose: To explore the value of ultrasound radiomics in the preoperative identification of true and pseudo gallbladder polyps and to evaluate the associated diagnostic accuracy. Methods: Totally, 99 pathologically proven gallbladder polyps in 96 patients were enrolled, including 58 cholesterol polyps (55 patients) and 41 gallbladder tubular adenomas (41 patients). Features on preoperative ultrasound images, including spatial and morphological features, were acquired for each lesion. Following this, two-stage feature selection was adopted using Fisher's inter-intraclass variance ratios and Z-scores for the selection of intrinsic features important for differential diagnosis achievement with support vector machine use. Results: Eighty radiomic features were extracted from each polyp. Eight intrinsic features were identified after two-stage selection. The contrast 14 (Cont14) and entropy 6 (Entr6) values in the cholesterol polyp group were significantly higher than those in the gallbladder adenoma group (4.063 ± 1.682 vs. 2.715 ± 1.867, p < 0.001 for Cont14; 4.712 ± 0.427 vs. 4.380 ± 0.720, p = 0.003 for Entr6); however, the homogeneity 13 (Homo13) and energy 8 (Ener8) values in the cholesterol polyp group were significantly lower (0.500 ± 0.069 vs. 0.572 ± 0.057, p < 0.001 for Homo13; 0.050 ± 0.023 vs. 0.068 ± 0.038, p = 0.002 for Ener8). These results indicate that the pixel distribution of cholesterol polyps was more uneven than that of gallbladder tubular adenomas. The dispersion degree was also significantly lower in the cholesterol polyp group than the gallbladder adenoma group (0.579 ± 0.054 vs. 0.608 ± 0.041, p = 0.005), indicating a lower dispersion of high-intensity areas in the cholesterol polyps. The long axis length of the fitting ellipse (Maj.Len), diameter of a circle equal to the lesion area (Eq.Dia) and perimeter (Per) values in the cholesterol polyp group were significantly lower than those in the gallbladder adenoma group (0.971 ± 0.485 vs. 1.738 ± 0.912, p < 0.001 for Maj.Len; 0.818 ± 0.393 vs. 1.438 ± 0.650, p < 0.001 for Eq.Dia; 2.637 ± 1.281 vs. 5.033 ± 2.353, p < 0.001 for Per), demonstrating that the cholesterol polyps were smaller and more regular in terms of morphology. The classification accuracy, sensitivity, specificity, and area under the curve values were 0.875, 0.885, 0.857, and 0.898, respectively. Conclusions: Ultrasound radiomic analysis based on the spatial and morphological features extracted from ultrasound images effectively contributed to the preoperative diagnosis of true and pseudo gallbladder polyps and may be valuable in their clinical management.

7 citations


Journal ArticleDOI
TL;DR: A deep transfer learning framework for histopathological image analysis by using convolutional neural networks with visualization schemes, which can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy is proposed.
Abstract: This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.

Journal ArticleDOI
TL;DR: Depending on its unique imaging features including enhancement filling pattern, hyper-enhanced subcapsular artery and presence of washout, CEUS might provide helpful diagnostic information for preoperative prediction of various HCA molecular subtypes.
Abstract: OBJECTIVE To explore the specific contrast-enhanced ultrasound (CEUS) features of hepatocellular adenomas (HCA) according to their pathological molecular classifications. METHODS & MATERIALS In this retrospective study, fifty-three histopathologically proved HCA lesions (mean size, 39.7±24.9 mm) were included. Final histopathological diagnosis of HCA lesions were identified by surgical resection (n = 51) or biopsy (n = 2) specimens. CEUS imaging features were compared among four subgroups according to World Health Organization (WHO) 2019 pathological molecular classifications standards. Analysis of variance (ANOVA) were used for statistical analysis of continuous variables. Fisher's exact test were used for categorical variables. The sensitivity (SE), specificity (SP), and accuracy of CEUS feature in diagnosis of each HCA subtype were calculated and compared. RESULTS Final histopathological diagnosis included HNF-1α inactivated HCAs (H-HCA, n = 12), β-catenin activated HCAs (B-HCA, n = 8), inflammatory HCAs (I-HCA, n = 31), and unclassified HCAs (U-HCA, n = 2). During arterial phase of CEUS, all HCAs were hyper-enhanced, 66.6% (8/12) of H-HCAs and 50% (4/8) of B-HCAs displayed complete hyperenhancement, whereas 58.0% (18/31) of I-HCAs showed centripetal filling hyperenhancement pattern (P = 0.016). Hyper-enhanced subcapsular arteries could be detected in 64.5% (20/31) I-HCAs during early arterial phase. During portal venous and late phase, sustained hyper- or iso-enhancement were observed in 91.7% (11/12) of H-HCAs, while most of I-HCAs (61.3%, 19/31) and B-HCAs (7/8, 87.5%) were hypo-enhanced (P = 0.000). Central unenhanced areas were most commonly observed in I-HCAs (29.0%, 9/31) (P = 0.034). CONCLUSION Depending on its unique imaging features including enhancement filling pattern, hyper-enhanced subcapsular artery and presence of washout, CEUS might provide helpful diagnostic information for preoperative prediction of various HCA molecular subtypes.

Journal ArticleDOI
TL;DR: The radiomics features and intrinsic imaging phenotypes derived from the dual-mode ultrasound can capture the distinctions between benign, lymphomatous, and metastatic nodes and are valuable in node differentiation.
Abstract: Background The ultrasonic diagnosis of lymph node lesions is usually based on a small number of subjective visual features from a single ultrasonic modality, which limits diagnostic accuracy. Therefore, our study aimed to propose a computerized method for using dual-mode ultrasound radiomics and the intrinsic imaging phenotypes for accurately differentiating benign, lymphomatous, and metastatic lymph nodes. Methods A total of 543 lymph nodes from 538 patients were examined with both B-mode ultrasonography and elastography. The data set was randomly divided into a training set of 407 nodes and a validation set of 136 nodes. First, we extracted 430 radiomic features from dual-mode images. Then, we combined the least absolute shrinkage and selection operator with the analysis of variance to select several typical features. We retrieved the intrinsic imaging phenotypes by using a hierarchical clustering of all radiomics features, and we integrated the phenotypes with the selected features for the classification of benign, lymphomatous, and metastatic nodes. Results The areas under the receiver operating characteristic curves (AUCs) on the validation set were 0.960 for benign vs. lymphomatous, 0.716 for benign vs. metastatic, 0.933 for lymphomatous vs. metastatic, and 0.856 for benign vs. malignant. Conclusions The radiomics features and intrinsic imaging phenotypes derived from the dual-mode ultrasound can capture the distinctions between benign, lymphomatous, and metastatic nodes and are valuable in node differentiation.

Journal ArticleDOI
TL;DR: The analysis of SWD slope and liver viscosity parameters provide additional viscoelastic information about FLLs before operation.
Abstract: Background The aim of our study is to analyze viscosity characteristics of focal liver lesions (FLLs) and the diagnostic performance of shear wave dispersion (SWD) in differentiating benign and malignant FLLs. Methods Between January 2018 and April 2018, 58 consecutive patients (median age 57, age range 21–74 years, 37 males) with 58 FLLs located on the right lobe of liver were prospectively studied. The Aplio i900 series diagnostic ultrasound system (Canon Medical systems) equipped with a curvilinear PV1-475BX transducer (1–8 MHz) was used. SWD slope and viscosity measurements were expressed as mean ± standard deviation for both liver tumors and background liver parenchyma. Histopathological results after surgery were regarded as the gold standard for diagnosis. Results Final diagnosis included 40 cases of malignant and 18 cases of benign FLLs. The mean viscosity value were 14.78 ± 1.86 m/s/kHz for hepatocellular carcinoma (n = 30), 14.81 ± 2.35 m/s/kHz for liver metastasis lesions (n = 10), 13.23 ± 1.31 m/s/kHz for hemangioma (n = 13), and 13.67 ± 2.72 m/s/kHz for focal nodular hyperplasia (n = 5). Malignant FLLs showed higher mean viscosity values (14.79 ± 3.15 m/s/KHz) than benign FLLs (13.36 ± 2.76 m/s/KHz) (p Conclusions The analysis of SWD slope and liver viscosity parameters provide additional viscoelastic information about FLLs before operation.

Journal ArticleDOI
TL;DR: COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge, suggesting the negative conversion duration of viral nucleic acid and the recovery time of hs -CPR may be the indicator of the pneumonia resolution.
Abstract: Background: To retrospectively evaluate several clinical indicators related to the improvement of COVID-19 pneumonia on CT. Methods: A total of 62 patients with COVID-19 pneumonia were included. The CT scores based on lesion patterns and distributions in serial CT were investigated. The improvement and deterioration of pneumonia was assessed based on the changes of CT scores. Grouped by using the temperature, serum lymphocytes and high sensitivity CRP (hs-CRP) on admission respectively, the CT scores on admission, at peak time and at discharge were evaluated. Correlation analysis was carried out between the time to onset of pneumonia resolution on CT images and the recovery time of temperature, negative conversion of viral nucleic acid, serum lymphocytes and hs-CRP. Results: The CT scores of the fever group and lymphopenia group were significantly higher than those of normal group on admission, at peak time and at discharge; and the CT scores of normal hs-CRP group were significantly lower than those of the elevated hs-CRP group at peak time and at discharge (P all<0.05). The time to onset of pneumonia resolution on CT image was moderately correlated with negative conversion duration of viral nucleic acid (r =0.501, P<0.05) and the recovery time of hs-CPR (r =0.496, P<0.05). Conclusions: COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge. The negative conversion duration of viral nucleic acid and the recovery time of hs-CPR may be the indicator of the pneumonia resolution.