Bin Zhou's research while affiliated with Sichuan University and other places
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Publications (8)
The development of various antitumor drugs has significantly improved the survival of patients with cancer. Many first-line chemotherapy drugs are cytotoxic and the cardiotoxicity is one of the most significant effects that could leads to poor prognosis and decreased survival rate. Cancer treatment include traditional anthracycline drugs, as well a...
LGE and T2 Mapping Images of Representative Cases. On LGE Images (A for Animals and C for Human), Infarcted Myocardium Demonstrated the Area with High Intensity; On T2 Maps (B for Animals and D for Human), The High Ntensity of Infarcted Myocardium Decreases from 3 Days to 3 Months Following MI. The Measurement of Remote T2 is Performed within the D...
Purpose
To quantify global and regional left ventricular (LV) strain parameters in patients with Kawasaki disease (KD) using cardiovascular magnetic resonance (CMR) tissue tracking and assess the association of coronary artery dilation (CA dilation) with LV systolic dysfunction.
Methods
Thirty-one KD patients with CA dilation, 22 patients without...
Background:
Progressive cardiomyopathy accounts for almost all mortality among Duchenne muscular dystrophy (DMD) patients. Thus, our aim was to comprehensively characterize myocardial involvement by investigating the heterogeneity of native T1 mapping in DMD patients using global and regional (including segmental and layer-specific) analysis acro...
Background:
The pathophysiological changes in the remote myocardium after acute myocardial infarction (MI) remains less understood.
Purpose:
To assess the inflammation in the remote myocardium post-MI and its association with left ventricular (LV) remodeling using T2 mapping.
Study type:
Prospective.
Animal model and subjects:
Twelve pigs at...
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Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC).
Methods: This study had a retrospective multicenter (two hospitals in China) design...
Background and purpose:
Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model...
In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated...
Citations
... After manual segmentation, the original DICOM images and segmentation results were normalized according to pixel spacing and slice thickness to reduce the influence of various acquisition parameters of different MR image systems on the stability of radiomics features [31]. Subsequently, radiomics features, including first-order features, shape-based features, gray level co-occurrence matrix (GLCM) features, gray level dependence matrix (GLDM) features, gray level run length matrix (GLRLM) features, gray level size zone matrix (GLSZM) features, and neighboring gray-tone difference matrix (NGTDM) features were extracted from each ROI. ...
... On the basis of predictive models based on those radiomics features, clinicians can deliver more personalized medical care about tumor diagnosis, histopathological classification, therapeutic assessment, and prognosis (16,17). Several studies investigated the role of applying radiomics features extracted from CT images for noninvasive predicting tumor recurrence of HGSOC patients (18)(19)(20)(21). The nomogram built by radiomics signatures and clinical factors demonstrated the feasibility of predicting the recurrence of HGSOC (18,21). ...
... Other studies have also demonstrated associations between radiomics features and response to immunotherapy and histological subtypes [54,55]. With CT imaging feature selection techniques using deep-learning methods, a radiomic model was predictive of PFS and showed AUCs of 0.77-0.83 in the test and two validation cohorts [56]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/732736704761864-1551709452914_Q64/Shuo-Wang-55.jpg)
