Clinical trials: Cancer Education
Neuromyelitis Optica, Neuromyelitis Optica Spectrum Disorder
The Longitudinal CONQUER Study of Rare Neuroimmunologic Disorders
This study seeks to determine the biologic causes of inflammation in patients with Transverse Myelitis (TM) Neuromyelitis Optica Spectrum Disorder (NMOSD) and related conditions.
- Conditions
- Neuromyelitis Optica, Neuromyelitis Optica Spectrum Disorder, Transverse Myelitis, Optic Neuritis
- Trial name
- NCT01623076
- Where
- United States
- Volunteers
- 150 people, healthy volunteers welcome
- Sponsor
- University of Texas Southwestern Medical Center
Nasopharyngeal Cancinoma (NPC), Lymph Node Metastasis
A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it.
- Trial name
- NPC (NCT06829147)
- Where
- China
- Volunteers
- 500 people
- Sponsor
- Sun Yat-sen University
NSCLC
Distributed Learning of Edic and CardIac Dose Effects in Lung Cancer
Cardiac dose was not a major concern in lung radiotherapy patients until the results of the RTOG (Radiation Therapy Oncology Group) 0617 trial, which showed an association between cardiac dose and survival.
- Trial name
- DECIDE (NCT06329648)
- Volunteers
- 20,000 people
- Sponsor
- The Netherlands Cancer Institute
Healthy
St. Jude Cancer Education for Children Program: The Feasibility of Teacher Delivery (Pilot Study)
-The St. Jude Cancer Education for Children Program was created in 2006 as part of the outreach mandate of the Comprehensive Cancer Center at St. Jude Children's Research Hospital.
- Trial name
- NCT01591213
- Where
- United States
- Volunteers
- 426 people, healthy volunteers welcome
- Sponsor
- St. Jude Children's Research Hospital
Radiation Oncology
Radiation Treatment Plan Evaluation Learning Tool Pilot Project
This study involves both the development of a novel learning tool, the Radiation Treatment Plan Evaluation Learning Module, as well as a pilot program to validate this tool as an effective resource to teach treatment plan review…
- Trial name
- NCT06115811
- Testing
- Radiation Treatment Plan Evaluation Learning Tool
- Where
- United States
- Volunteers
- 20 people, healthy volunteers welcome
- Sponsor
- Massachusetts General Hospital
Glioma
Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence
This registry aims to collect clinical, molecular and histopathology imaging including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and…
- Trial name
- NCT04215224
- Testing
- Histopathology images based survival prediction for glioma patients
- Where
- China
- Volunteers
- 3,500 people
- Sponsor
- The First Affiliated Hospital of Zhengzhou University
Lymph Nodes With Tumor Metastasis
Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
In this study, the investigators aimed to construct a deep learning diagnostic model that uses cytological images to predict primary unknown tumor origins in patients with tumors combined with lymph node metastases.
- Trial name
- NCT06810349
- Where
- China
- Volunteers
- 10,000 people, healthy volunteers welcome
- Sponsor
- West China Hospital
Childhood Cancer
Rehabilitation Including Social and Physical Activity in Children and Teenagers With Cancer
The purpose of this study is to determine whether early rehabilitation intervention including individualized physical training and social activities with a class mate at two weeks intervals at the ped.onc.
- Trial name
- RESPECT (NCT01772862)
- Testing
- Intervention group
- Where
- Denmark
- Volunteers
- 240 people
- Sponsor
- Kjeld Schmiegelow
Pediatric Cancer
Evaluation of the Impact of a Family-Centered Empowerment Program on Self-Efficacy, Self-Esteem, Depression, Anxiety, Stress Level, and Care Skills in Parents of Children With Oncological Problems
This study aims to develop a web-based education program based on family centered empowerment model for parents of children with oncological problems and to evaluate the effect of the program on parents' self-efficacy…
- Trial name
- NCT05181228
- Testing
- Web-Based Education Based on Family-Centered Empowerment Model
- Where
- Turkey (Türkiye)
- Volunteers
- 76 people, healthy volunteers welcome
- Sponsor
- Mersin University
Chemotherapy Induced Nausea and Vomiting, Quality of Life (QOL)
Vomiting Prevention in Children With Cancer
The goal of this single arm trial is to learn if a machine learning (ML) model predicting the risk of vomiting within the next 96 hours will impact vomiting outcomes in inpatient cancer pediatric patients.
- Conditions
- Chemotherapy Induced Nausea and Vomiting, Quality of Life (QOL), Pediatric Cancer
- Trial name
- NCT06886451
- Testing
- ML-based intervention
- Where
- Canada
- Volunteers
- 1,953 people
- Sponsor
- The Hospital for Sick Children
Colorectal Cancer
Use of Telehealth In-home Messaging to Improve GI (Gastrointestinal) Endoscopy Completion Rates
Low endoscopy completion rates are a major problem in the VA, causing delay or failure to receive essential care, increased clinic wait times, lost capacity, increased costs, thus limiting endoscopic screening for colorectal…
- Trial name
- GIVER (NCT00310362)
- Testing
- Use of Interactive Voice Response (IVR) system to improve adherence to GI appointments and prep procedures
- Where
- United States
- Volunteers
- 3,610 people
- Sponsor
- VA Office of Research and Development
Cancer
Veterans as Leaders in Understanding and Education (VALUE)
The primary objectives for this study are to develop an estimate of the prevalence of health literacy at four geographically diverse Veterans Affairs Medical Centers (Minneapolis, Portland, Durham, and West Los Angeles), and for…
- Trial name
- NCT00137241
- Where
- United States
- Volunteers
- 1,850 people
- Sponsor
- US Department of Veterans Affairs
Breast Cancer, Molecular Typing
Clinical Study of Imaging Genomics Based on Machine Learning for BCIG
1. Identify the imaging features of breast cancer with different molecular types 2. Reveal the association between hormone receptor positive/HER2 negative breast cancer and imaging histology, Oncotype Dx recurrence score 3.
- Conditions
- Breast Cancer, Molecular Typing, Pathology, Image, Body
- Trial name
- NCT04461990
- Testing
- Multidisciplinary cooperative comprehensive treatment
- Where
- China
- Volunteers
- 1,500 people
- Sponsor
- Fudan University
Non Small Cell Lung Cancer
20K Distributed Learning Challenge
Machine learn a predictive model from more than 20.000 non-small cell lung cancer patients from more than 5 health care providers from more than 5 countries.
- Trial name
- NCT03564457
- Testing
- No interventions will take place (observational)
- Where
- Netherlands
- Volunteers
- 20,000 people
- Sponsor
- Maastricht Radiation Oncology
CNS Tumor
Artificial Intelligence in CNS Radiation Oncology
Radiotherapy involves the use of high-energy X-rays, which can be used to stop the growth of tumor cells. Radiotherapy constitutes an essential avenue in the treatment of brain tumors.
- Trial name
- AI-RAD (NCT06036394)
- Testing
- The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of IGRT or disease evaluation, and response assessment/ surveillance post-RT
- Where
- India
- Volunteers
- 6,000 people
- Sponsor
- Tata Memorial Centre
Retinoblastoma
Deep Learning in Retinoblastoma Detection and Monitoring.
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment.
- Trial name
- NCT05308043
- Testing
- Deep learning algorism
- Where
- China
- Volunteers
- 200 people
- Sponsor
- Beijing Tongren Hospital
Nasopharyngeal Carcinoma, Oral Mucositis
Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
The goal of this observational study is to apply the CNN-based DL method to extract the three-dimensional spatial information of IMRT dose distribution to predict the occurrence probability of serious radiotherapy and…
- Trial name
- NCT06032767
- Testing
- observational group
- Where
- China
- Volunteers
- 480 people
- Sponsor
- Sun Yat-sen University
Endometrial Cancer
Developing a MRI-based Deep Learning Model to Predict MMR Status
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to…
- Trial name
- NCT05783986
- Testing
- randomly divided
- Volunteers
- 600 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Cancer, Metastatic, Cancer
SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry
International registry for cancer patients evaluating the feasibility and clinical utility of an Artificial Intelligence-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and…
- Conditions
- Cancer, Metastatic, Cancer, Cancer of Pancreas, Cancer of Liver
- Trial name
- NCT03452774
- Testing
- Clinical Trial Matching
- Where
- 68 sites in United States, Puerto Rico
- Volunteers
- 50,000 people
- Sponsor
- Massive Bio, Inc.
Melanoma (Skin Cancer), Artificial Intelligence (AI)
Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
The goal of this study is to develop supportive diagnostic artificial intelligence algorithms to distinguish melanoma from nevi or other benign pigmented skin lesions, especially in younger patients (below the age of 30).
- Conditions
- Melanoma (Skin Cancer), Artificial Intelligence (AI), Pediatric Cancer
- Trial name
- AI-MEL (NCT06621810)
- Where
- 3 sites in Germany, Italy and 1 more
- Volunteers
- 3,000 people, healthy volunteers welcome
- Sponsor
- German Cancer Research Center