Cervix CancerNCT05102240
Development of Clinically High Efficient Platforms for Individualised Treatment of Cervix Cancer
An observational study of Cervix Cancer, run by Tata Memorial Hospital at 1 site in India.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 90 years, women only
- Conditions the study is for
- Cervix Cancer
- How many people
- 1,800 participants
- Where
- 1 siteIndia
- Study dates
- 24 Feb 2022to 30 Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study 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.
Who may be able to join
- 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.
Who cannot join
- Lack of disease or toxicity outcomes.
- Lack of images in the hospital database.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
India1 site, 1 recruiting
- Recruiting
Advanced Centre of Treatment Research and Education In Cancer,Tata Memorial Centre
Navi Mumbai, Maharashtra, 410210
Dr Supriya Chopra, MD, 91-22-27405000 ext. 5491, schopra@actrec.gov.in
Supriya Chopra, MD
About this study
Official title: 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.
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.
Read the detailed description
For Aim 1. Automatic delineation of complex tumour targets for cervical cancer for the Gross Tumour Volume (GTV) at baseline and at brachytherapy and High Risk Clinical Target Volume(CTV) at baseline and brachytherapy will be done on MRI.
Following structures will be processed for automation on CT
- Low Risk Clinical Target Volume (Low Risk CTV)
- GTV: Nodal
- Elective Nodal Pelvic Target Volume
- Elective Nodal Pelvic and Paraaortic Volume
- Rectum
- Bladder
- Sigmoid
- Bowel
- Bone Marrow
For Aim 2. The Investigator intend to employ machine learning for developing more robust normal tissue toxicity prediction models. Further advanced techniques like texture analysis of radiation dose maps and follow up tissue density will also be performed to develop predictive models of toxicity. By using our patient datasets, Investigator want to create a library of proton beam plans with the proton planning systems that will be available in department of radiation oncology and using the developed normal tissue complication plots available the information of achievable doses through protons can help in identifying patients who will benefit from proton therapy.
For Aim 3. Within this project Investigator intend to integrate staging, pathology and quantitative imaging texture features for response prediction and identification of "high risk cohort" in cervix cancer. Images and clinical data from patients that have MRI at baseline will be included The texture features can be used to categorise "good" and "poor responders" after chemoradiation. For the same cohort of patients the Investigator also have tissue available including results of additional biomarkers (like AKT,LICAM, PDL1,CD4 and CD8). The Investigator intend to first correlate difference in texture features and see if there is a pattern of different molecular features. In the second step imaging and molecular features could be integrated for developing" risk prediction models". GTV and HRCTV delineated on 150 data sets at baseline and brachytherapy within Aim 1 will be utilised to categorise responders and non-responders and validate another 150 patient data sets.
- Conditions
- Cervix Cancer
- Keywords
- Advanced Radiation Technology, Quantitative Imaging, Molecular Biology, Machine Learning
- Medical subject headings
- Uterine Cervical Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Generation of software for automated target delineation for cervix cancer
- To develop and validate automated platforms for target delineation and planning for cervix cancer in time efficient manner through
- 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.
Measured: 3 years
Development and validation of Normal Tissue Complication Plots
- 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)
Measured: 3 years
Identify "high risk patient population" that may benefit from intensification of treatment in future
- 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
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Nov 2021
- Study start24 Feb 2022
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cervix CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tata Memorial Hospital
- Sponsor type
- Other government
- Collaborators
- Bhabha Atomic Research Centre, Erasmus Medical Center
- Responsible party
- Principal investigator, Supriya Sastri (chopra), Tata Memorial Hospital
- Investigators
- Supriya Sastri (nee Chopra), MD, Principal investigator, Tata Memorial Centre, The Advanced Centre for Treatment, Research and Education in Cancer (ACTREC)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05102240
- Sponsor's protocol ID
- TMC IRB 900787
- Other IDs
- CTRI/2021/08/035810
- Status last verified
- Feb 2026
- First submitted
- 20 Oct 2021
- Last updated
- 20 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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.
- ClinicalTrials.gov ID
- NCT05102240
- Acronym / sponsor ID
- TMC IRB 900787
- Conditions
- Cervix Cancer
- Keywords
- Advanced Radiation Technology, Quantitative Imaging, Molecular Biology, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,800 participants (estimated)
- Lead sponsor
- Tata Memorial HospitalOther government
- Collaborators
- Bhabha Atomic Research Centre, Erasmus Medical Center
- Study start
- 24 Feb 2022 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 1 Nov 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
For Aim 1. Automatic delineation of complex tumour targets for cervical cancer for the Gross Tumour Volume (GTV) at baseline and at brachytherapy and High Risk Clinical Target Volume(CTV) at baseline and brachytherapy will be done on MRI.
Following structures will be processed for automation on CT
- Low Risk Clinical Target Volume (Low Risk CTV)
- GTV: Nodal
- Elective Nodal Pelvic Target Volume
- Elective Nodal Pelvic and Paraaortic Volume
- Rectum
- Bladder
- Sigmoid
- Bowel
- Bone Marrow
For Aim 2. The Investigator intend to employ machine learning for developing more robust normal tissue toxicity prediction models. Further advanced techniques like texture analysis of radiation dose maps and follow up tissue density will also be performed to develop predictive models of toxicity. By using our patient datasets, Investigator want to create a library of proton beam plans with the proton planning systems that will be available in department of radiation oncology and using the developed normal tissue complication plots available the information of achievable doses through protons can help in identifying patients who will benefit from proton therapy.
For Aim 3. Within this project Investigator intend to integrate staging, pathology and quantitative imaging texture features for response prediction and identification of "high risk cohort" in cervix cancer. Images and clinical data from patients that have MRI at baseline will be included The texture features can be used to categorise "good" and "poor responders" after chemoradiation. For the same cohort of patients the Investigator also have tissue available including results of additional biomarkers (like AKT,LICAM, PDL1,CD4 and CD8). The Investigator intend to first correlate difference in texture features and see if there is a pattern of different molecular features. In the second step imaging and molecular features could be integrated for developing" risk prediction models". GTV and HRCTV delineated on 150 data sets at baseline and brachytherapy within Aim 1 will be utilised to categorise responders and non-responders and validate another 150 patient data sets.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 90 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- Lack of disease or toxicity outcomes.
- Lack of images in the hospital database.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Generation of software for automated target delineation for cervix cancer
Time frame: 3 years
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.
Development and validation of Normal Tissue Complication Plots
Time frame: 3 years
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)
Identify "high risk patient population" that may benefit from intensification of treatment in future
Time frame: 3 years
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Feb 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Supriya Sastri (nee Chopra), MD principal investigator
Tata Memorial Centre, The Advanced Centre for Treatment, Research and Education in Cancer (ACTREC)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cervix Cancer, run by Tata Memorial Hospital at 1 site in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Supriya Sastri (nee Chopra), MD (02227405000 ext. 5113, supriyasastri@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Generation of software for automated target delineation for cervix cancer (time frame: 3 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Feb 2026.