A5100434436- Radiomics and Machine Learning in Medical Imaging
- Prostate Cancer Diagnosis and Treatment
- Prostate Cancer Treatment and Research
- MRI in cancer diagnosis
- Gastric Cancer Management and Outcomes
- Advanced MRI Techniques and Applications
- Hepatocellular Carcinoma Treatment and Prognosis
- AI in cancer detection
- Urologic and reproductive health conditions
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Advanced Neuroimaging Techniques and Applications
- Pediatric Urology and Nephrology Studies
- Renal cell carcinoma treatment
- Lung Cancer Diagnosis and Treatment
- Pancreatic and Hepatic Oncology Research
- Gastrointestinal Tumor Research and Treatment
- Renal and Vascular Pathologies
- Child and Adolescent Psychosocial and Emotional Development
- Urinary and Genital Oncology Studies
- Online and Blended Learning
- Bladder and Urothelial Cancer Treatments
- Colorectal Cancer Screening and Detection
- Advanced X-ray and CT Imaging
- Esophageal Cancer Research and Treatment
- Colorectal Cancer Treatments and Studies
Nanjing Medical University
2016-2025
Jiangsu Province Hospital
2016-2025
Southeast University
2025
Northwestern University
2023-2024
University of Leicester
2024
Hefei First People's Hospital
2024
Beijing Chaoyang Emergency Medical Center
2023
Capital Medical University
2023
Zhejiang University
2022
Chengdu University of Traditional Chinese Medicine
2018-2022
In radiomics studies, researchers usually need to develop a supervised machine learning model map image features onto the clinical conclusion. A classical pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious find an optimal with appropriate combinations. We designed open-source software package named FeAture Explorer (FAE). was programmed Python used NumPy, pandas, scikit-learning modules. FAE can be extract features,...
Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value disease-specific recurrence-free survival. Materials Methods For this retrospective study, was developed on the basis of primary cohort 177 patients with BTC who underwent resection LN dissection between June 2010 December 2016. Radiomic features were extracted from portal venous CT scans. A signature built reproducible by using least absolute...
Background Deep learning is the most promising methodology for automatic computer‐aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp‐MRI). Purpose To develop an approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) mp‐MRI. Study Type Retrospective. Subjects In all, 195 patients localized were collected from a PROSTATEx database. total, 159/17/19 444/48/55 observations (215/23/23 PCas 229/25/32 NCs) randomly selected...
Perfluorooctane sulfonate (PFOS) is associated with male reproductive disorders, but its targets and mechanisms are poorly understood. We used in vitro vivo models to explore the roles of Sertoli cells blood-testis barrier (BTB) PFOS-induced dysfunction. First, we primary cell estimate cytotoxicity, junction proteins expression, changes function. ICR mice were then administered PFOS (0.25-50mg/kg/day) for 4 weeks. Sperm count, ultrastructure permeability cell-based BTB, testicular estimated....
Background Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value imaging prostate cancer (PCa) remains unknown. Purpose To investigate the feasibility of MRI–based microstructural mapping for noninvasively properties PCa and discriminating between clinically significant insignificant disease. Materials Methods Men with a clinical suspicion were enrolled prospectively October 2019 August 2020. Time-dependent data...
Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis Lung AdenocarcinomaYan Zhong1, Mei Yuan1, Teng Zhang1, Yu-Dong Hai Li2 and Tong-Fu Yu1Audio Available | Share
OBJECTIVE. The objective of our study was to compare the performance radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation benign malignant solid renal masses using contrast-enhanced CT examinations. MATERIALS AND METHODS. This retrospective included a cohort 254 cell carcinomas (RCCs) (190 clear RCCs [ccRCCs], 38 chromophobe [chrRCCs], 26 papillary [pRCCs]), fat-poor angioleiomyolipomas, 10 oncocytomas with preoperative Lesions identified by four...
BackgroundDue to heterogeneity of hepatocellular carcinoma (HCC), outcome assessment HCC with transarterial chemoembolization (TACE) is challenging.MethodsWe built histologic-related scores determine microvascular invasion (MVI) and Edmondson-Steiner grade by training CT radiomics features using machine learning classifiers in a cohort 494 HCCs hepatic resection. Meanwhile, we developed deep (DL)-score for disease-specific survival imaging DL networks 243 TACE. Then, three newly hallmarks...
Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy multiparametric MRI (mpMRI) versus dual-energy CT (DECT) GC not known. Purpose To compare the diagnostic personalized mpMRI with that DECT T and N in patients receiving curative surgical intervention. Materials Methods Patients who underwent before gastrectomy lymphadenectomy were eligible this single-center prospective noninferiority study...
Generative adversarial networks (GANs) have recently demonstrated significant potential for producing virtual images with the same characteristics as real-life landscapes, thereby enhancing various medical tasks. To design a region-guided focal GAN (Focal-GAN) translating between CT and MRI test its clinical applicability in patients hepatocellular carcinoma (HCC). Between January 2012 October 2021, two cohorts of HCC who underwent contrast-enhanced (Center 1, n = 685) 516; Center 2, 318)...
Comparison of Utility Histogram Apparent Diffusion Coefficient and R2* for Differentiation Low-Grade From High-Grade Clear Cell Renal CarcinomaYu-Dong Zhang1, Chen-Jiang Wu1, Qing Wang1, Jing Xiao-Ning Xi-Sheng Liu1 Hai-Bin Shi1Audio Available | Share