Clinical trials: Radiomic
Nonsmall Cell Lung Cancer
CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
Lung cancer remains the leading cause of cancer related mortality worldwide, with more than 1.5 million related deaths annually.
- Trial name
- NCT03940846
- Testing
- Virtual biopsy
- Where
- Netherlands
- Volunteers
- 650 people
- Sponsor
- Maastricht University
Radiomic, Radiomics
RADIomics to Predict HER2 Status And T-DXd Efficacy in Metastatic Breast Cancer: the RADIOSPHER2 Study
RADIOSPHER2 study is a monocentric, retrospective, observational study aiming at identifying a radiomics signature able to predict HER2 expression (0 vs low vs overexpression) and trastuzumab deruxtecan efficacy in metastatic…
- Conditions
- Radiomic, Radiomics, Breast Cancer Metastatic, Breast Cancer Stage IV
- Trial name
- RADIOSPHER2 (NCT07030569)
- Testing
- Trastuzumab deruxtecan (DS-8201a)
- Where
- Italy
- Volunteers
- 500 people
- Sponsor
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Uterine Fibroid, Uterine Sarcoma
A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma
In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine…
- Conditions
- Uterine Fibroid, Uterine Sarcoma, AI (Artificial Intelligence), Radiomic
- Trial name
- NCT07269535
- Testing
- No intervention (observational study)
- Volunteers
- 500 people
- Sponsor
- Tongji Hospital
Lung Cancer, Image, Body
Influence of PET/CT Radiomic Features on the Outcome of Lung Cancer Patients
Radiomics is an attractive field in objectively quantifying image features, and may overcome the subjectivity of visually interpreting computed tomography (CT), or positron emission tomography (PET).
- Trial name
- NCT03648151
- Where
- 2 sites in China
- Volunteers
- 1,000 people
- Sponsor
- The First Affiliated Hospital of Shanxi Medical University
HNSCC, AI
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma
Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer.
- Conditions
- HNSCC, AI, Radiomic, MRI
- Trial name
- NCT06366906
- Testing
- The Resnet50 deep learning (DL) model
- Where
- 2 sites in China
- Volunteers
- 319 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Locally Advanced Rectal Cancer, Distant Metastasis
Constructing a Multimodal Imaging System to Predict the Risk of Heterochronous Metastasis of Rectal Cancer
The goal of this observational study is to construct a multimodal intelligent model to predict the risk of heterochronous metastasis of rectal cancer, which is helpful for individualized diagnosis and treatment and follow-up…
- Trial name
- MIS-MRC (NCT06293612)
- Testing
- No intervention was administrated to the two cohorts.
- Where
- China
- Volunteers
- 302 people
- Sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Glioma
Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis
Glioma are type of primary brain tumors arising within the substance of brain. Different type of gliomas are seen which are classified depending upon pathological examination and advanced molecular techniques, which help to…
- Trial name
- GLIO-RAD (NCT06036381)
- Testing
- Radiomic analysis of imaging - MRI, PET, CT
- Where
- India
- Volunteers
- 1,000 people
- Sponsor
- Tata Memorial Centre
Coronavirus, Machine Learning
Clinical and Radiomic Model of COVID-19
To develop and validate a machine-learning model based on clinical, laboratory, and radiological characteristics alone or combination of COVID-19 patients to facilitate risk Assessment before and after symptoms and triage (home…
- Trial name
- NCT04337502
- Testing
- Machine learning model
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Maastricht University
Sarcoma, Soft Tissue
Radiomics for Tumor Grading of Soft Tissue Sarcomas.
Radiomics is defined as a quantitative high-throughput analysis of imaging data combined with model development aiming to predict biological correlates or clinical endpoints.
- Trial name
- NCT03798795
- Where
- Germany
- Volunteers
- 285 people
- Sponsor
- Technical University of Munich
Brain Tumor, Brain Neoplasms
Brain Tumor Intraoperative Ultrasound Database
Predicting the survival of patients diagnosed with glioblastoma (GBM) is essential to guide surgical strategy and subsequent adjuvant therapies.
- Conditions
- Brain Tumor, Brain Neoplasms, Brain Cancer, Glioma
- Trial name
- BraTioUS-DB (NCT05062772)
- Testing
- Ultrasound
- Where
- 6 sites in United States, France and 3 more
- Volunteers
- 150 people
- Sponsor
- Hospital del Rio Hortega
Breast Cancer, Head and Neck Cancer
Radiomic Analysis for Predicting Treatment Response and Clinical Outcomes in Malignancies
In this study, the investigators aim to undertake a retrospective analysis of CT and MRI scans for patients undergoing radiation treatment to develop radiomic signatures to predict treatment response and clinical outcomes.
- Conditions
- Breast Cancer, Head and Neck Cancer, Brain Cancer, Gynecologic Cancer
- Trial name
- NCT05170087
- Where
- Canada
- Volunteers
- 1,200 people
- Sponsor
- Sunnybrook Health Sciences Centre
Pancreatic Cancer
Porto-Venous Radiomics in Pancreatic Cancer
Multi-centre Retrospective Observational Cohort study of patients who underwent pancreaticoduodenectomy from 01/01/2005 to 01/02/2022 with histologically confirmed pancreatic head malignancy
- Trial name
- NCT05700110
- Testing
- Non-Interventional
- Where
- United Kingdom
- Volunteers
- 800 people
- Sponsor
- Royal Marsden NHS Foundation Trust