A5101910737- Gastric Cancer Management and Outcomes
- Radiomics and Machine Learning in Medical Imaging
- Cancer Immunotherapy and Biomarkers
- Gastrointestinal Tumor Research and Treatment
- Helicobacter pylori-related gastroenterology studies
- Metastasis and carcinoma case studies
- Colorectal Cancer Treatments and Studies
- Colorectal and Anal Carcinomas
- Ferroptosis and cancer prognosis
- Cancer-related molecular mechanisms research
- Immune cells in cancer
- Intraperitoneal and Appendiceal Malignancies
- Esophageal Cancer Research and Treatment
- Cancer Cells and Metastasis
- Inflammatory Biomarkers in Disease Prognosis
- Immunotherapy and Immune Responses
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Advanced X-ray and CT Imaging
- Urinary and Genital Oncology Studies
- Cancer Diagnosis and Treatment
- DNA Repair Mechanisms
- Bladder and Urothelial Cancer Treatments
- MRI in cancer diagnosis
- Cancer-related Molecular Pathways
- PARP inhibition in cancer therapy
Nanfang Hospital
2018-2024
Southern Medical University
2018-2024
Guangzhou Medical University
2024
We aimed to evaluate whether radiomic feature-based fluorine 18 ( F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits.Methods: A total 214 GC patients (training (n = 132) or validation 82) cohort) were subjected feature extraction (80 features).Radiomic features in the training cohort a LASSO cox analysis predict disease-free (DFS) overall (OS) validated cohort.A radiomics nomogram with...
Abstract The tumor immune microenvironment (TIME) is associated with prognosis and immunotherapy response. Here we develop validate a CT-based radiomics score (RS) using 2272 gastric cancer (GC) patients to investigate the relationship between imaging biomarker neutrophil-to-lymphocyte ratio (NLR) in TIME, including its correlation response advanced GC. RS achieves an AUC of 0.795–0.861 predicting NLR TIME. Notably, indistinguishable from IHC-derived status DFS OS each cohort (HR range:...
The tumor microenvironment (TME) plays a critical role in disease progression and is key determinant of therapeutic response cancer patients. Here, we propose noninvasive approach to predict the TME status from radiological images by combining radiomics deep learning analyses. Using multi-institution cohorts 2,686 patients with gastric cancer, show that model accurately predicted an independent prognostic factor beyond clinicopathologic variables. further predicts benefit adjuvant...
BACKGROUND. The tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of context, investigate their associations with prognosis immunotherapy response in gastric cancer (GC).
Background Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is deficiency in precise predictive biomarkers for ICI efficacy. The aim this study was to develop and validate pathomics-driven ensemble model predicting the response ICIs cancer, using H&E-stained whole slide images (WSI). Methods This multicenter retrospectively collected analyzed WSIs clinical data 584 cancer. An model, integrating four...
Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status. Methods: We retrospectively analyzed radiomics features CT images 1,689 consecutive patients from three centers. The model was developed the training cohort validated internal external validation cohorts. Lasso regression utilized select build signature....
Background Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies. Methods Based on the abundance tumor-infiltrating cells in patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm identify robust clusters patients, assessed their reproducibility an independent cohort Gene Expression Omnibus. We further confirmed feasibility...
Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, disease diagnosis, biological research, drug development. However, these techniques are often hindered by high time cost requirements.
Background Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim this study was to develop validate a CT imaging biomarker predict the immunotherapy patients with GC investigate associated infiltration patterns. Methods This retrospective included 294 who received anti-PD-1/PD-L1 from three independent medical centers between...
Abstract Chemoresistance remains the primary challenge of clinical treatment gastric cancer (GC), making biomarkers chemoresistance crucial for decision. Our previous study has reported that p21-actived kinase 6 (PAK6) is a prognostic factor selecting which patients with GC are resistant to 5-fluorouracil/oxaliplatin chemotherapy. However, mechanistic role PAK6 in chemosensitivity unknown. The present identified as an important modulator DNA damage response (DDR) and GC. Analysis specimens...
Purpose: Population-based data on the proportion and prognosis of liver metastases at diagnosis gastric cancer are currently lacking.Besides, treatment with is still controversial now.Methods: Patients (GCLM) time in advanced were identified using Surveillance, Epidemiology, End Result (SEER) database National Cancer Institute.Multivariable logistic Cox regression performed to identify predictors presence GCLM factors associated all-cause mortality. Results:We 3507 patients diagnosis,...
Objective: The aim of this study is to evaluate whether radiomic imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and develop a nomogram for preoperative prediction PM status. Methods: We collected CT images pathological T4 955 consecutive patients two centers analyzing the radiomics features retrospectively then developed validated model built from 292 quantitative image training cohort validation cohorts. Lasso regression...
Background The tumor microenvironment (TME) is crucial for recurrence, prognosis, and therapeutic responses. We comprehensively investigated the TME characterization associated with relapse survival outcomes of gastric cancer (GC) to predict chemotherapy immunotherapy response. Methods A total 2,456 GC patients complete gene-expression data clinical annotations from twelve cohorts were included. characteristics evaluated using three proposed computational algorithms. then developed a...
Background: Tertiary lymphoid structures (TLSs) are associated with favorable prognosis and enhanced response to anti-cancer therapy. A digital assessment of TLSs could provide an objective alternative that mitigates variability inherent in manual evaluation. This study aimed develop validate a gene panel based on biological prior knowledge for TLSs, further investigate its associations survival multiple therapies. Materials Methods: The present involved 1,704 patients gastric cancer from...
Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction PR crucial for patient management treatment. The authors aimed to develop noninvasive imaging biomarker from computed tomography (CT) evaluation, investigate its associations with prognosis chemotherapy benefit. Methods: In this multicenter study including five independent cohorts 2005 GC patients, extracted...
Purpose The purpose of this study was to analyze the frequency and prognosis pulmonary metastases in newly diagnosed gastric cancer using population-based data from SEER. Methods Patients with (GCPM) at time diagnosis advanced were identified Surveillance, Epidemiology, End Result (SEER) database National Cancer Institute 2010 2014. Multivariable logistic Cox regression performed identify predictors presence GCPM factors associated all-cause mortality cancer-specific mortality. Survival...
Object: The risk of lymph node positivity (LN+) in gastric cancer (GC) impacts therapeutic recommendations. aim this study was to quantify the effect younger age on LN+. Methods: Data from a Chinese multi-institutional database and US SEER stage I III resected GC were analyzed for relationship between LN+ status. association status examined with logistic regression separately each T stage, adjusting multiple covariates. Poisson used evaluate number Results: 4,905 14,877 patients identified...
Treatments for young patients with gastric cancer (GC) remain poorly defined, and their effects on survival are uncertain. We aimed to investigate the receipt of chemotherapy by age category (18-49, 50-64, 65-85 years) explore whether differences in matched gains GC.Patients who were histologically diagnosed GC included from a Chinese multi-institutional database Surveillance, Epidemiology, End Results database. There 5,122 31,363 aged 18-85 years treated between 2000 2014, respectively....
Background: The risk of lymph node positivity (LN ) in gastric cancer (GC) impacts therapeutic recommendations. aim this study was to quantify the effect younger age on LN .
Gastric cancer (GC) is a product of multiple genetic abnormalities, including and epigenetic modifications. This study aimed to integrate various biomolecules, such as miRNAs, mRNA, DNA methylation, into genome-wide network develop nomogram for predicting the overall survival (OS) GC. A total 329 GC cases, training cohort with random 150 examples included validation cohort, were screened from The Cancer Genome Atlas database. was constructed based on combination univariate Cox regression...
<title>Abstract</title> Background Gastric cancer (GC) remains a major challenge in oncology due to its late diagnosis and poor prognosis. Predicting Vascular Endothelial Growth Factor (VEGF) levels survival outcomes accurately can significantly enhance therapeutic decision-making. This study introduces an innovative approach utilizing [18F] FDG PET/CT radiomics predict VEGF status outcomes, aiming improve personalized treatment strategies GC. Methods We performed retrospective analysis of...