Publications by authors named "Ruizhi Gao"

Purpose: We aimed to develop and validate a combined model integrating radiomic features derived from Contrast-Enhanced Ultrasound (CEUS) images and clinical parameters for preoperative prediction of CK19-positive status in hepatocellular carcinoma (HCC).

Methods: A total of 434 patients who underwent CEUS and surgical resection from January 2020 to December 2023 were included. Patients were randomly divided into a training cohort (n = 304) and a validation cohort (n = 130).

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  • The study aims to investigate how loneliness relates to socioeconomic status and social networks among older adults in China who have disabilities affecting their daily activities.
  • Conducted in six districts of Nantong, the study included 880 older adults, revealing that a significant number experienced moderate to severe loneliness, particularly among those with fewer social connections and living in rural areas.
  • Findings highlight that factors such as being female, married, and having more social interactions are linked to lower feelings of loneliness, indicating the need for targeted support for this vulnerable population.
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Objective: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics.

Methods: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the "CancerSubtypes" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics.

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  • The study aimed to evaluate the effectiveness of a multi-modal ultrasomics model in predicting the response to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer, comparing it to traditional clinical models.
  • It involved 106 patients, with data collected from B-mode and contrast-enhanced ultrasound images, and utilized various algorithms to build and assess five different prediction models.
  • The results indicated that the Combined_2 model performed the best in predicting treatment efficacy, significantly outperforming the clinical model, while age and clinical stage were found to be independent predictors of treatment outcomes.
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  • The study aimed to create and validate an ultrasound-based radiomics model to predict the presence of synchronous liver metastases in patients with rectal cancer before surgery.
  • A total of 239 rectal cancer patients were analyzed, and a logistic regression method was used to develop both a radiomics model using 13 features and a combined radiomics-clinical model incorporating clinical characteristics.
  • The results showed that the radiomics-clinical model offered strong predictive performance, improving the ability to diagnose and predict outcomes for liver metastases in rectal cancer patients.
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Purpose: The high proportion of HCC in CEUS LR-M decreases the sensitivity of LR-5 for the diagnosis of HCC. However, when modifying LR-M criteria to further improve the sensitivity of LR-5, it is also important not to compromise the diagnostic performance (especially sensitivity) of LR-M for non-hepatocellular carcinoma malignancies (non-HCCMs). The purpose of this study was to evaluate the diagnostic performance of CEUS LI-RADS (2017 version) for non-HCCMs and to explore the impact of modified CEUS LI-RADS on the diagnostic performance of LR-M.

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Objectives: To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features' molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC).

Methods: A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis.

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Chinese medicine extracts are currently the hotspot of new drug research and development. Herein, we report the mechanism of action of the traditional Chinese medicine extract Forsythiaside A in the treatment of male infertility and experimental verification. We first obtained 95 intersection genes between the target protein of Forsythiaside A and the target genes of male infertility and screened 13 key genes.

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Objective: To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer.

Methods: A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions.

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Objective: To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery.

Methods: A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion.

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Purpose: To explore the diagnostic performance and interreader agreement of CEUS LI-RADS in diagnosing ≤ 30 mm liver nodules with different experienced radiologists.

Methods: Between January 2018 and October 2020, 244 patients at high-risk for HCC who underwent CEUS were enrolled. Two novice radiologists and two expert radiologists independently evaluated LI-RADS categories and main features.

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Purpose: To compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC).

Methods: In this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018.

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Hepatocellular carcinoma (HCC) is one of the most common malignancies and displays high heterogeneity of molecular phenotypes. We investigated DNA damage repair (DDR) alterations in HCC by integrating multi-omics data. HCC patients were classified into two heterogeneous subtypes with distinct clinical and molecular features: the DDR-activated subtype and the DDR-suppressed subtype.

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  • The study aimed to develop a CT radiomics model to predict the presence of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC).
  • Researchers analyzed CT images from 172 ccRCC patients, extracting nearly 3000 radiomic features, and used LASSO regression for feature selection, ultimately identifying 9 important features for the prediction model.
  • The model demonstrated effective performance in both training and validation cohorts, indicating its potential as a noninvasive tool for personalized SDM risk assessment in ccRCC patients.
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Purpose: The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).

Methods: A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction.

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Background: To identify radiomic subtypes of clear cell renal cell carcinoma (ccRCC) patients with distinct clinical significance and molecular characteristics reflective of the heterogeneity of ccRCC.

Methods: Quantitative radiomic features of ccRCC were extracted from preoperative CT images of 160 ccRCC patients. Unsupervised consensus cluster analysis was performed to identify robust radiomic subtypes based on these features.

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Objectives: To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients.

Methods: We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest.

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Objectives: To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease.

Methods: A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies).

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Purpose: To evaluate a radiomic approach for the stratification of diffuse gliomas with distinct prognosis and provide additional resolution of their clinicopathological and molecular characteristics.

Methods: For this retrospective study, a total of 704 radiomic features were extracted from the multi-channel MRI data of 166 diffuse gliomas. Survival-associated radiomic features were identified and submitted to distinguish glioma subtypes using consensus clustering.

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Background: Thyroid carcinoma (TC) is a common malignancy of the endocrine system. This research aimed to examine the expression levels of miR-136-5p and metadherin (MTDH) in TC and unveil their potential targeting relationship.

Methods: TC microRNA (miRNA) microarray and miRNA-sequencing data were collected to evaluated miR-136-5p expression.

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Neuroblastoma (NBL) is the most frequently encountered extracranial solid neoplasm and impacts significantly on the survival of patients, especially in cases of advanced tumor stage or relapse. A long noncoding RNA (lncRNA) signature to predict the survival of patients with NBL is proposed in this paper. Differentially expressed lncRNA (DElncRNA) was selected using the Limma plus Voom package in R based on the RNA-sequencing data downloaded from the Therapeutically Applicable Research To Generate Effective Treatments database and Genotype-Tissue Expression database.

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Tubeimoside-1 (TBMS1) possesses broad anticancer activities, including the cytostatic and anti-angiogenesis effects in lung cancer. However, the effect of TBMS1 on the metastasis of non-small cell lung cancer (NSCLC) cells and the potential underlying mechanism remain unclear. In the present study, a cell counting kit-8 assay revealed that TBMS1 suppressed the proliferation of NCI-H1299 cells significantly, particularly following 48 h of treatment.

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Tubeimoside-1 (TBMS1), a triterpenoid saponin isolated from the tuber of Bolbostemma paniculatum (Maxim) Franquet, serves an universal suppressive role in multiple cancer types, including lung cancer. However, the mechanism involved in non‑small cell lung cancer (NSCLC) cells by which TBMS1 elicits its antitumor effects is not yet comple-tely understood. The present study indicated that 10 µmol/l TBMS1 significantly enhanced apoptosis and notably blocked the migration and invasion of NCI‑H1299 cells.

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