Radiomics: a Study of Outcome in Lung Cancer
NCT01302626 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 216
Last updated 2017-03-22
Summary
Aim of the study: The main aim is to collect data of patients with lung cancer, and to perform different analyses on this data. The data contains information on patient and tumor characteristics, imaging, and treatment characteristics. With this data it is possible to improve and validate the predictive model for survival and long term toxicity in lung cancer by multicentric prospective data collection. The long term aim, beyond this specific study, is to build a Decision Support System based on the predictive models validated in this study.
Hypothesis: The general hypothesis is that we get a better prediction in terms of AUC (area under the curve) of survival and long term toxicity when we combine multifactorial variables. These variables consist of information from clinical data, imaging data, data related to treatment type and treatment quality.
Conditions
Sponsors & Collaborators
-
H. Lee Moffitt Cancer Center and Research Institute
collaborator OTHER -
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
collaborator OTHER -
Maastricht Radiation Oncology
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2010-03-31
- Primary Completion
- 2014-01-31
- Completion
- 2014-03-31
Countries
- United States
- Italy
- Netherlands
Study Locations
More Related Trials
-
Usefulness of Blood Biomarkers for Overall Survival in NSCLC
NCT01936571 ·Status: COMPLETED
-
Pathological and Nuclear Medicine Factors for Prognosis in Lung Carcinoma
NCT04276025 ·Status: COMPLETED
-
Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization
NCT06531343 ·Status: NOT_YET_RECRUITING ·Phase: NA
-
A Radiomic Model for Risk of Local Recurrence and DFS for T3 and T4 Non-small Cell Lung Cancer
NCT06405815 ·Status: COMPLETED
-
Rapid Learning for Lung Cancer
NCT01949259 ·Status: COMPLETED
-
AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs
NCT06320184 ·Status: ACTIVE_NOT_RECRUITING
-
Radiomics to Identify Patients at Risk for Developing Pneumonitis, Differentiate Immune Checkpoint Inhibitor-induced Pneumonitis From Other Lung Inflammation and Distinguish Tumour Pseudo-progression From Real Tumour Growth
NCT03305380 ·Status: COMPLETED
-
Benefit of Spectral Information in Patients Suspected for Lung Cancer
NCT06440616 ·Status: RECRUITING ·Phase: NA
-
CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
NCT03940846 ·Status: UNKNOWN
-
Surveillance With PET/CT and Liquid Biopsies of Stage I-III Lung Cancer Patients After Completion of Definitive Therapy
NCT03740126 ·Status: ACTIVE_NOT_RECRUITING ·Phase: NA
-
Cutting-edge IMAGING Technologies to Improve the SAFEty and the Sustainability of LUNG Cancer Screening and the Accuracy of Non-invasive Lung Nodules Characterization
NCT06963515 ·Status: NOT_YET_RECRUITING
-
Validation of Multiparametric Models and Circulating and Imaging Biomarkers to Improve Lung Cancer EARLY Detection.
NCT04323579 ·Status: UNKNOWN
-
Predicting Immunotherapy Response and Survival of Lung Cancer Patients Using Artificial Intelligence and Radiomics (Radiology-AI-Lung)
NCT07059923 ·Status: RECRUITING
-
PRophylactic Cerebral Irradiation or Active MAgnetic Resonance Imaging Surveillance in Small-cell Lung Cancer Patients (PRIMALung Study)
NCT04790253 ·Status: RECRUITING ·Phase: NA
-
A Multi-omics Sequencing-based Model for Predicting Efficacy and Dynamic Monitoring of Treatment in Small Cell Lung Cancer
NCT07026669 ·Status: RECRUITING
-
LC-NMR Study Biomarkers to Detect Lung Cancer
NCT02024113 ·Status: COMPLETED
-
AI Models for Predicting Occult Pleural Dissemination in NSCLC
NCT07065422 ·Status: COMPLETED
-
Image Mining and ctDNA to Improve Risk Stratification and Outcome Prediction in NSCLC Applying Artificial Intelligence.
NCT06163846 ·Status: RECRUITING
-
Lung Cancer Screening Program Using Low-dose Tomography and Metabolomic Evaluation in a Public Service.
NCT06376097 ·Status: RECRUITING
-
Identify Prognostic Biomarkers of Lung Cancer
NCT05010330 ·Status: UNKNOWN
-
Using Imaging and Molecular Markers to Predict Tumor Response and Lung Toxicity in Lung Cancer
NCT00603057 ·Status: COMPLETED
-
Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer
NCT07107035 ·Status: NOT_YET_RECRUITING
-
Real-Time Image Guided Lymphatic Mapping and Nodal Targeting in Lung Cancer
NCT00264602 ·Status: ACTIVE_NOT_RECRUITING ·Phase: PHASE1
-
Circulating and Imaging Biomarkers to Improve Lung Cancer Management and Early Detection
NCT04315753 ·Status: UNKNOWN
-
The Detection Of Circulating Tumor Cells (CTC) In Patients With NSCLC Undergoing Definitive Radiotherapy Or Chemoradiotherapy
NCT02135679 ·Status: COMPLETED