Radiomics-based Models for the Prediction of Pathological Response to Neoadjuvant Therapy in Gastric and Gastroesophageal Cancer
NCT06044961 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 80
Last updated 2023-09-21
Summary
Identifying predictors of response to neoadjuvant therapy in gastric and gastro-oesophageal cancer early in the history of the disease would ensure optimal treatment planning.
The study aims to apply radiomics for the prediction of response to neoadjuvant therapy.
Conditions
- Patients With Locally Advanced Carcinoma of the Stomach and Oesophagogastric Junction
Interventions
- RADIATION
-
CT scan
All pre-operative CT scan collected
Sponsors & Collaborators
-
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
lead OTHER
Eligibility
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2005-01-01
- Primary Completion
- 2022-08-30
- Completion
- 2022-11-15
More Related Trials
-
AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
NCT06035250 ·Status: RECRUITING
-
Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in Rectal Cancer
NCT04273477 ·Status: UNKNOWN
-
A Transfer Learning Radiomics Model for Predicting Response to Initial Transarterial Embolization in Patients with Gastroenteropancreatic Neuroendocrine Tumor Liver Metastases
NCT06853457 ·Status: COMPLETED
-
Use of Positron Emission Tomography/Computed Tomography (PET/CT) to Assess Tumor Response to Neoadjuvant Treatment for Distal Rectal Cancer
NCT00254683 ·Status: UNKNOWN ·Phase: PHASE2
-
RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer
NCT04271657 ·Status: COMPLETED
-
Integrating Multi-Omics Data for Enhanced Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
NCT07190040 ·Status: COMPLETED
-
Efficacy of ICG-based NIR Imaging in Intralesional Curettage of Giant Cell Tumors of Bone in Limbs: a Prospective, Single-center, Single-arm, Open Study
NCT06647901 ·Status: RECRUITING ·Phase: PHASE1/PHASE2
-
Recurrence and Prognosis Prediction Model for Gastric Cancer
NCT07243847 ·Status: COMPLETED
-
Multi-omics Sequencing in Neoadjuvant Immunotherapy of Gastrointestinal Tumors
NCT05515796 ·Status: COMPLETED ·Phase: PHASE2
-
Multi-center and Multi-modal Deep Learning Study of Gastric Cancer
NCT05001321 ·Status: UNKNOWN
-
RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer
NCT04273451 ·Status: UNKNOWN
-
Interaction Between Host, Microenvironment and Immunity on Gastrointestinal Neoplasms
NCT04363983 ·Status: RECRUITING ·Phase: NA
-
ML Decision Model for G-NEC Adjuvant Therapy
NCT06663852 ·Status: COMPLETED
-
Multimodal Model Predicts Recurrence
NCT06690268 ·Status: COMPLETED
-
Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
NCT06548464 ·Status: COMPLETED
-
Preoperative Concurrent Chemoradiotherapy for Locally Advanced Gastroesophageal Junction or Upper Gastric Adenocarcinoma
NCT02193594 ·Status: UNKNOWN ·Phase: PHASE2/PHASE3
-
THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI Guided Radiotherapy
NCT04815694 ·Status: UNKNOWN ·Phase: NA
-
Tumor Response Prediction in Neoadjuvant Chemoradiation of Locally Advanced Rectal Cancer Using Metabonomics Analysis
NCT03149978 ·Status: UNKNOWN
-
A Study On The Prediction of Neoadjuvant Efficacy For Rectal Cancer Based On MR Cytometry Imaging and Deep-radiomics
NCT07107815 ·Status: ENROLLING_BY_INVITATION
-
Application of Radiomics in Precise Preoperative Diagnosis and Prognsis Evaluation of Colorectal Cancer.
NCT03787667 ·Status: UNKNOWN
-
Biomarkers for Predicting Neoadjuvant Chemoradio-resistance for Middle-low Advanced Rectal Cancer
NCT03573791 ·Status: RECRUITING
-
Predict 5-Year Survival in Elderly Gastric Cancer
NCT06208046 ·Status: COMPLETED
-
Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases
NCT06023173 ·Status: COMPLETED
-
Radiomics in Rectal Cancer
NCT05331040 ·Status: UNKNOWN
-
Predicting RadIotherapy ReSponse of Rectal Cancer With MRI and PET
NCT02233374 ·Status: COMPLETED ·Phase: NA