A5086578270- Advanced Radiotherapy Techniques
- Lung Cancer Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Head and Neck Cancer Studies
- Medical Imaging Techniques and Applications
- Endometrial and Cervical Cancer Treatments
- Lung Cancer Treatments and Mutations
- Hepatocellular Carcinoma Treatment and Prognosis
- Brain Tumor Detection and Classification
- Brain Metastases and Treatment
- Gastric Cancer Management and Outcomes
- Head and Neck Surgical Oncology
- Management of metastatic bone disease
- Medical Image Segmentation Techniques
- Lung Cancer Research Studies
- Medical Imaging and Analysis
- Renal cell carcinoma treatment
- Salivary Gland Tumors Diagnosis and Treatment
- Radiation Therapy and Dosimetry
- Peptidase Inhibition and Analysis
- MRI in cancer diagnosis
- Cardiac Structural Anomalies and Repair
- Ear and Head Tumors
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Advanced X-ray and CT Imaging
Affiliated Hospital of Southwest Medical University
2017-2025
Second Affiliated Hospital of Zhejiang University
2020
Zhejiang University
2020
Southwest Medical University
2010-2020
First Affiliated Hospital of Sichuan Medical University
2015
Abstract Background & aims The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming establish an automatic classification model. Methods We recruited 104 pathologically confirmed patients for this study. GTV samples were manually segmented into regions of interest randomly divided five-fold cross-validation groups. Dimensionality...
This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on transition from acute radiation damage to a fibrotic state. It reviewed cellular and molecular responses radiation, highlighting role myofibroblasts significant impact Transforming Growth Factor-beta (TGF-β) in promoting fibroblast-to-myofibroblast transformation. The delves into epigenetic regulation gene expression, contribution extracellular matrix proteins microenvironment, immune system...
Objective The objective of this study is to develop a model predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making improving assessment prognosis neurological function. Methods This retrospective encompassed 101 who underwent aneurysm embolization surgery. 851 radiomic features were extracted from images. 512 are last layer...
Objective This study aims to construct a predictive model based on machine learning algorithms assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and analyze preoperative postoperative factors associated with extended hospitalization. Methods We prospectively collected clinical data from 83 patients. The included 40 variables (comprising 39 predictor 1 target variable). Important were identified through variable selection via Lasso regression algorithm,...
Chemotherapy is the primary treatment for advanced non-small-cell lung cancer (NSCLC). However, related dose-dependent toxicity limits its clinical use. Therefore, it necessary to explore new strategies improving outcomes while reducing side effects of chemotherapy in NSCLC. In this study, we designed and synthesized epidermal growth factor (EGF)-modified doxorubicin nanoparticles (EGF@DOX-NPs) that selectively targets receptor (EGFR) overexpressed tumor cells. When administered combination...
Brain metastases (BM) are the most common type of intracranial tumor and leading cause mortality in patients with systemic cancer. In recent years, stereotactic radiosurgery (SRS) has been widely used radiotherapy BM due to its advantages high positional accuracy highly conformal dose distributions. However, this area lacks a bibliometric analysis. This study aims provide an overview trends key topics related SRS for treatment over past decade anticipate future directions through methods. We...
Objective The objective of this study was to develop a predictive model combining radiomic characteristics and clinical features forecast overall survival in cervical cancer patients treated with intensity-modulated radiotherapy concurrent chemotherapy. Methods In retrospective observational study, 159 were divided into training group (n = 95) validation 64). Radiomic extracted from contrast-enhanced computed tomography scans. least absolute shrinkage selection operator regression analysis...
More clinical practice need to be performed verify the toxicity of hypofractionated radiotherapy (HFRT) combined with PD-1 blockade in lung cancer. This phase I study aimed investigate safety and efficacy nivolumab HFRT patients progressive advanced cancer following multiline treatment.We enrolled 31 pathologically confirmed have disease treated first-line or a higher therapy. Selected lesions were using HFRT, was administered within 7 days subsequently. Nivolumab once month partial...
Abstract Objective This study aimed to discuss the dosimetric advantages of helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT) technology in hippocampal avoidance whole‐brain radiotherapy provide references for clinical selection ideal technology. Methods A total 20 patients with were chosen randomly. Computed tomography (CT) MRI scanning images input into treatment planning system (TPS). After CT enhanced magnetic resonance T1 weighted fused registered, same radiation...
Abstract Background We evaluated the treatment response and predictive factors for overall survival (OS) in patients with hepatocellular carcinoma (HCC) portal vein tumour thrombosis (PVTT), who underwent stereotactic body radiotherapy (SBRT). Additionally, we developed validated a personalised prediction model patient survival. Methods Clinical information was retrospectively collected 80 HCC PVTT, were treated SBRT at Cancer Hospital of University Chinese Academy Sciences (Zhejiang...
In radiotherapy, the delineation of gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite criticality this process, a pronounced gap exists availability tools tailored for automatic segmentation GTV based on CT images.
Introduction: This study aimed to develop and validate the combination of radiomic features clinical characteristics that can predict patient survival in hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) treated stereotactic body radiotherapy (SBRT). Materials Methods: The prediction model was developed a primary cohort 70 patients HCC PVTT SBRT, using data acquired between December 2015 June 2017. were extracted from computed tomography (CT) scans. A least absolute...
A neural network method was employed to establish a dose prediction model for organs at risk (OAR) in patients with cervical cancer receiving brachytherapy using needle insertion.A total of 218 CT-based needle-insertion fraction plans loco-regionally advanced treatment were analyzed 59 patients. The sub-organ OAR automatically generated by self-written MATLAB, and the volume read. Correlations between D2cm3 each sub-organ-as well as high-risk clinical target bladder, rectum, sigmoid...
Preoperative identification of axillary lymph node metastasis can play an important role in treatment selection strategy and prognosis evaluation. This study aimed to establish a clinical nomogram based on images predict breast cancer patients. A total 193 patients with non-specific invasive were divided into training (n = 135) validation set 58). Radiomics features extracted from imagesinstead tumor region, the least absolute shrinkage operator logistic algorithm was used select generate...
Brain metastases present significant challenges in radiotherapy due to the need for precise tumor delineation. Traditional methods often lack efficiency and accuracy required optimal treatment planning. This paper proposes an improved U-Net model that uses a position attention module (PAM) automated segmentation of gross volumes (GTVs) computed tomography (CT) simulation images patients with brain improve planning segmentation.
BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise designers. AIM To predict uninvolved liver dose in stereotactic body (SBRT) for cancer using neural network-based method. METHODS A total 114 SBRT plans were used to test network Sub-organs automatically generated. Correlations between volume each sub-organ, dose, prediction model established MATLAB. Of cases, 70% selected as training set, 15% validation set. regression R -value mean square error (MSE)...
To develop a model for predicting the dose absorbed by organ at risk (OAR) during intensity modulated radiation therapy nasopharyngeal carcinoma (NPC).55 patients underwent NPC. The OARs were divided into several suborgans, and SPSS software was used to evaluate multiple linear method fitting normalized volume each suborgan, mean (Dmean) Dnm (Dnm = Dmean/Dprescription), D10%-D100% values Dn10%-n100%(Dn10%-n100% D10%-D100%/Dprescription) OAR. Based on Matlab software, predicted Dn10%-n100%...