Publications by authors named "Liyong Zhuo"
Article Synopsis
- The study focused on creating a deep learning (DL) model to aid in the detection and diagnosis of cerebral aneurysms, both with and without human involvement.
- The DL model was trained on data from 3,829 patients and tested on 484 patients, comparing performance between the model, human radiologists, and a combination of both.
- Results showed significant improvements in diagnostic speed and accuracy for radiologists when assisted by the DL model, particularly benefiting junior radiologists.
Article Synopsis
- The study aims to create a nomogram for early prediction of severe mycoplasma pneumoniae pneumonia (SMPP) in both pediatric and adult patients by analyzing 550 cases, classified into severe and non-severe groups.
- Various models were developed using clinical data, imaging, and radiomics features, assessed through different statistical methods, including logistic regression and AUC metrics.
- The integrated model showed strong predictive ability, particularly in testing cohorts, with a notable improvement when combining clinical and imaging features, demonstrating its effectiveness for identifying SMPP.
Objective: To explore the potential of the deep learning reconstruction (DLR) for ultralow dose calcium scoring CT (CSCT) with simultaneously reduced tube voltage and current.
Methods: In this prospective study, seventy-five patients (group A) undergoing routine dose CSCT (120kVp/30mAs) were followed by a low dose (120kVp/20mAs) scan and another 81 (group B) were followed by an ultralow dose (80kVp/20mAs) scan. The hybrid iterative reconstruction was used for the routine dose data while the DLR for data of reduced dose.
Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.
Materials And Methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences ( < 0.
Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3.
View Article and Find Full Text PDFObjective: To retrospectively investigate the value of various MRI image menifestations in the hepatobiliary phase (HBP), DWI and T2WI sequences in predicting the pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC).
Materials And Methods: Forty-three patients of IMCCs confirmed by pathology were enrolled including 25 cases in well- or moderately-differentiated group and 18 cases in poorly-differentiated group. All patients underwent DWI, T2WI and HBP scan.
Objective: To investigate the value of diffusion-weighted imaging (DWI) combined with the hepatobiliary phase (HBP) Gd-BOPTA enhancement in differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from atypical liver abscess.
Materials And Methods: A retrospective analysis was performed on 43 patients with IMCCs (IMCC group) and 25 patients with atypical liver abscesses (liver abscess group). The DWI signal, the absolute value of the contrast noise ratio (│CNR│) at the HBP, and visibility were analyzed.
Purpose: To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis.
Methods And Materials: The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features.
Objective: To identify diffusion-weighted imaging (DWI) patterns and conspicuity discrepancies on hepatobiliary phase imaging (HBPI) to distinguish atypical hepatic abscesses from hepatic metastases.
Materials And Methods: This retrospective study recruited 31 patients with 43 atypical hepatic abscesses and 32 patients with 35 hepatic metastases who underwent gadobenate dimeglumine-enhanced magnetic resonance imaging. All lesions were confirmed by pathological or clinical diagnosis.