Development of Clinically High Efficient Platforms for Individualised Treatment of Cervix Cancer
ConditionCervix Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18-90
SponsorTata Memorial Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
Lack of disease or toxicity outcomes.
Lack of images in the hospital database.
Trial population
Cervical cancer patients treated within ongoing and completed clinical trials of chemoradiation and brachytherapy for cervix cancer at our institute with access to MRI/CT images at time of diagnosis and brachytherapy, undergoing postoperative or definitive radiotherapy and treated within trials of postoperative or definitive RT.
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
Not listed
Trial groups
No trial groups listed
Trial outcomes
Primary outcomes
1
Generation of software for automated target delineation for cervix cancer
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.
Time frame
3 years2
Development and validation of Normal Tissue Complication Plots
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)
Time frame
3 years3
Identify "high risk patient population" that may benefit from intensification of treatment in future
3\. To use advanced image texture analysis within ongoing institutional and collaborative clinical trials to identify "high risk patient population" that may benefit from intensification of treatment in future
Time frame
3 yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
Tata Memorial Hospital
Lead sponsor
Bhabha Atomic Research Centre
Collaborator
Erasmus Medical Center
Collaborator
Source ClinicalTrials.gov