Clinical trial · Observational
Development of Clinically High Efficient Platforms for Individualised Treatment of Cervix Cancer
Developing Clinical High Efficiency Platforms for Individualised Treatment Through Integration of Advanced Radiation Technology, Quantitative Imaging and Molecular Biology and Machine Learning for Treatment of Cervix Cancer.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Retrospective study utilizing patient data to develop and validate Machine Learning application. Available imaging data sets of patients who have completed treatment will be used to develop Normal tissue complication probability and Tumour control probability Hypothesis Integrating existing radiation treatment information, quantitative imaging and patient outcome data from completed and ongoing clinical trials will allow development of knowledge based systems for efficient treatment delivery and allow selection of patients for intensified treatment approaches in cervix cancer.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cervix Cancer | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (3)
- measure
- Generation of software for automated target delineation for cervix cancer
- timeFrame
- 3 years
- description
- 1\. To develop and validate automated platforms for target delineation and planning for cervix cancer in time efficient manner through a. Machine learning based detection of abnormal cancerous tissues in multimodality medical diagnostic images. b . To train machine base systems for automated planning of external radiation and brachytherapy for gynaecological cancers.
- measure
- Development and validation of Normal Tissue Complication Plots
- timeFrame
- 3 years
- description
- 2\. To use existing databases and radiation dose maps, imaging texture features and adverse events data for machine learning to develop "normal tissue complication plots "and to identify cervix cancer patient subgroups that may benefit from advanced radiation techniques (like proton treatment)
- measure
- Identify "high risk patient population" that may benefit from intensification of treatment in future
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
Inclusion Criteria: For Aim 1 and Aim 3: * Patients treated within ongoing and completed clinical trials of chemoradiation and brachytherapy for cervix cancer with access to MRI/CT images at the time of diagnosis and brachytherapy For Aim 2 * Patients undergoing postoperative or definitive radiotherapy and treated within trials of postoperative or definitive RT. Exclusion Criteria: 1. Lack of disease or toxicity outcomes. 2. Lack of images in the hospital database.
References
Publications (0)
Data not yet available