Uterine Cervical NeoplasmsEECCNCT06254729
Clinical Study on the Evaluation of the Efficacy of Cervical Cancer
An observational study of Uterine Cervical Neoplasms, run by First Affiliated Hospital Xi'an Jiaotong University.
Trial summary
The main objectives of this study are to construct a multi-omics-based prognostic and side-effect prediction model for cervical cancer based on pre-treatment imaging, digital pathology, genomics, proteomics, molecular biology, metabolomics, and intestinal flora characteristics data of cervical cancer patients… Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Uterine Cervical Neoplasms
- What is being tested?
- Observational study
- How many people
- 4,000 participants
- Where
- No sites listed yet
- Study dates
- 16 Feb 2024to 16 Feb 2030
- How long will I take part?
- 5 Years
- 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 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Cervical cancer patients receiving radiotherapy
Who may be able to join
- Pathology: patients with pathologically confirmed cervical cancer
- Location: primary tumor of the cervix
Who cannot join
- Patients with no prior radiation therapy
- Patients without treatment
- Patients without regular follow-up
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Study on the Application of Multi-omics in the Assessment of Efficacy and Prediction of Side Effects in Cervical Cancer
The main objectives of this study are to construct a multi-omics-based prognostic and side-effect prediction model for cervical cancer based on pre-treatment imaging, digital pathology, genomics, proteomics, molecular biology, metabolomics, and intestinal flora characteristics data of cervical cancer patients, combined with patients' clinical information, to guide the precise treatment of cervical cancer patients; and to deeply excavate the characteristics related to recurrent cervical cancer based on time-series multi-omics data. Construct an artificial intelligence auxiliary model for dynamic monitoring of cervical cancer recurrence based on longitudinal multi-omics. To provide a real-time and timely tool for clinical early prediction, early identification, early diagnosis and early intervention of cervical cancer, to prolong the survival time and improve the quality of patients' survival.
- To realize multi-omics feature extraction of cervical cancer patients before treatment, and build a prognosis and side-effect prediction model of cervical cancer to guide accurate treatment;
- To make iterative, comprehensive, real-time assessment of the risk of recurrence of cervical cancer based on time-series multi-omics data, and to build an early warning model for early identification and early diagnosis of recurrent cervical cancer;
- To establish a prognostic and side-effect prediction and risk dynamic assessment model for cervical cancer, to build an intelligent decision support system, to implement the application of prognostic and side-effect prediction and dynamic monitoring model, to further assist in the precise diagnosis and treatment of cervical cancer, and to provide an accurate prognostic tool for identifying, diagnosing, and intervening in cervical cancer during the follow-up process.
Read the detailed description
- Construct a prognosis and side effect prediction model based on pre-treatment multi-omics features of cervical cancer patients.
- Case selection: According to the overall experimental design, 2800 patients in the training group were used as the training data set, and 1200 patients in the validation group were used as the validation data collection.
- Model training and tuning: a. Extract the multi-omics features of the training group, carry out self-learning of the features, and form a preliminary cervical cancer prognosis and side-effect prediction model; b. Input the multi-omics data of the validation group into the model, and carry out the structure of the model and the training parameters, and seek for the optimal model structure and training parameters; c. Determine the optimal cervical cancer prognosis prediction and side-effect model.
2.Mining recurrent tumor characteristics based on multi-omics data and constructing a comprehensive assessment model for recurrence risk .
- Case selection: In accordance with the overall experimental design, 2800 patients in the training group were used as the training dataset, and 1200 patients in the validation group were collected as the validation data.
- Model training and tuning: a. The multi-omics data features of the training group before the diagnosis of recurrence in previous follow-up visits are used to carry out self-learning of the features, assess the risk of tumor recurrence based on multi-omics features in the course of previous follow-up visits, form a dynamic, real-time recurrence risk assessment model, and derive a comprehensive risk value for the decision-making of recurrence intervention; b. Multi-omics features related to the previous follow-up visits of the validation group before the diagnosis of recurrence are inputted into the model, and the iterative time-series recurrence risk assessment is carried out on the patients. time-series recurrence risk iterative assessment of patients to assess the diagnostic performance of the model; c. Adjust the structure and training parameters of the model according to the segmentation accuracy of the validation group to seek the optimal model structure and training parameters; d. Use technical means such as data augmentation and other technical means to think of enlarging the sample size to improve the segmentation accuracy; e. Determine the optimal risk assessment model.
- Establish the prognosis and side-effect prediction and dynamic monitoring system of cervical cancer.
- Docking the above constructed model with the outpatient system to construct a prognosis and side reaction prediction and dynamic monitoring system in the process of cervical cancer diagnosis and treatment; b. Constructing an intelligent decision support system through the prognosis and side reaction prediction and risk dynamic assessment model, implementing the application of recurrence prediction and dynamic monitoring system, and assisting the clinicians to make decisions on intervention measures.
- Conditions
- Uterine Cervical Neoplasms
- Being tested
- Observational study
- Medical subject headings
- Uterine Cervical Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational studyOther
Our study does not have any exposure factors.
| Study group | Role | What they receive |
|---|---|---|
| training group | The training group is used to train the model. Receives: Other: Observational study | |
| validation group | The validation group is used to evaluate model performance. Receives: Other: Observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of Circulating Tumor Cell Count
Number of Circulating Tumor Cell Count (CTC count), cells/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Alpha-fetoprotein (AFP)
Concentration of Alpha-fetoprotein (AFP), ng/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Carcinoembryonic Antigen (CEA)
Concentration of Carcinoembryonic Antigen (CEA), ng/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of carbohydrate antigen 199 (CA199)
Concentration of carbohydrate antigen 199 (CA199), U/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Squamous Epithelial Cell Carcinoma Antigen (SCC-Ag)
Concentration of Squamous Epithelial Cell Carcinoma Antigen (SCC-Ag), ng/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of carbohydrate antigen 125(CA125)
Concentration of carbohydrate antigen 125(CA125), U/mL.
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Count of Bacteria in urine
Count of Bacteria in urine, colony-forming units (CFU)/mL.
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Count of bacteria in stool
Count of bacteria in stool, colony-forming units (CFU)/mL
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
5-year overall survival rate of Participants
the proportion of patients who are alive at least 5 years after their initial diagnosis of cancer, regardless of the cause of death,%
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
disease-free survival of Participants
the length of time from the start of treatment until either the recurrence of cancer or death from any cause, months
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
progression-free survival of Participants
the start of treatment until either the recurrence of cancer or death from any cause, months
Measured: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2024
- Study start16 Feb 2024 (estimated)
- Primary completion16 Feb 2029 (estimated)
- Study completion16 Feb 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Uterine Cervical NeoplasmsStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital Xi'an Jiaotong UniversityThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Gansu Maternal and Child Health Hospital, Hanzhong Central Hospital
- Responsible party
- Sponsor
- Investigators
- Jinlu Ma, Study chair, First Affiliated Hospital of Xian Jiaotong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06254729
- Sponsor's protocol ID
- YXJLRH2022039
- Acronym
- EECC
- Status last verified
- Jan 2024
- First submitted
- 25 Jan 2024
- Last updated
- 12 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on the Application of Multi-omics in the Assessment of Efficacy and Prediction of Side Effects in Cervical Cancer
- ClinicalTrials.gov ID
- NCT06254729
- Acronym / sponsor ID
- EECC / YXJLRH2022039
- Conditions
- Uterine Cervical Neoplasms
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital Xi'an Jiaotong UniversityUniversity, hospital or other
- Collaborators
- Gansu Maternal and Child Health Hospital, Hanzhong Central Hospital
- Study start
- 16 Feb 2024 (estimated)
- Primary completion
- 16 Feb 2029 (estimated)
- Study completion
- 16 Feb 2030 (estimated)
- First posted
- 12 Feb 2024 (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.
The main objectives of this study are to construct a multi-omics-based prognostic and side-effect prediction model for cervical cancer based on pre-treatment imaging, digital pathology, genomics, proteomics, molecular biology, metabolomics, and intestinal flora characteristics data of cervical cancer patients, combined with patients' clinical information, to guide the precise treatment of cervical cancer patients; and to deeply excavate the characteristics related to recurrent cervical cancer based on time-series multi-omics data. Construct an artificial intelligence auxiliary model for dynamic monitoring of cervical cancer recurrence based on longitudinal multi-omics. To provide a real-time and timely tool for clinical early prediction, early identification, early diagnosis and early intervention of cervical cancer, to prolong the survival time and improve the quality of patients' survival.
- To realize multi-omics feature extraction of cervical cancer patients before treatment, and build a prognosis and side-effect prediction model of cervical cancer to guide accurate treatment;
- To make iterative, comprehensive, real-time assessment of the risk of recurrence of cervical cancer based on time-series multi-omics data, and to build an early warning model for early identification and early diagnosis of recurrent cervical cancer;
- To establish a prognostic and side-effect prediction and risk dynamic assessment model for cervical cancer, to build an intelligent decision support system, to implement the application of prognostic and side-effect prediction and dynamic monitoring model, to further assist in the precise diagnosis and treatment of cervical cancer, and to provide an accurate prognostic tool for identifying, diagnosing, and intervening in cervical cancer during the follow-up process.
Detailed description
- Construct a prognosis and side effect prediction model based on pre-treatment multi-omics features of cervical cancer patients.
- Case selection: According to the overall experimental design, 2800 patients in the training group were used as the training data set, and 1200 patients in the validation group were used as the validation data collection.
- Model training and tuning: a. Extract the multi-omics features of the training group, carry out self-learning of the features, and form a preliminary cervical cancer prognosis and side-effect prediction model; b. Input the multi-omics data of the validation group into the model, and carry out the structure of the model and the training parameters, and seek for the optimal model structure and training parameters; c. Determine the optimal cervical cancer prognosis prediction and side-effect model.
2.Mining recurrent tumor characteristics based on multi-omics data and constructing a comprehensive assessment model for recurrence risk .
- Case selection: In accordance with the overall experimental design, 2800 patients in the training group were used as the training dataset, and 1200 patients in the validation group were collected as the validation data.
- Model training and tuning: a. The multi-omics data features of the training group before the diagnosis of recurrence in previous follow-up visits are used to carry out self-learning of the features, assess the risk of tumor recurrence based on multi-omics features in the course of previous follow-up visits, form a dynamic, real-time recurrence risk assessment model, and derive a comprehensive risk value for the decision-making of recurrence intervention; b. Multi-omics features related to the previous follow-up visits of the validation group before the diagnosis of recurrence are inputted into the model, and the iterative time-series recurrence risk assessment is carried out on the patients. time-series recurrence risk iterative assessment of patients to assess the diagnostic performance of the model; c. Adjust the structure and training parameters of the model according to the segmentation accuracy of the validation group to seek the optimal model structure and training parameters; d. Use technical means such as data augmentation and other technical means to think of enlarging the sample size to improve the segmentation accuracy; e. Determine the optimal risk assessment model.
- Establish the prognosis and side-effect prediction and dynamic monitoring system of cervical cancer.
- Docking the above constructed model with the outpatient system to construct a prognosis and side reaction prediction and dynamic monitoring system in the process of cervical cancer diagnosis and treatment; b. Constructing an intelligent decision support system through the prognosis and side reaction prediction and risk dynamic assessment model, implementing the application of recurrence prediction and dynamic monitoring system, and assisting the clinicians to make decisions on intervention measures.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| training group | – | Other: Observational studyThe training group is used to train the model. |
| validation group | – | Other: Observational studyThe validation group is used to evaluate model performance. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Cervical cancer patients receiving radiotherapy
Key inclusion criteria (2 listed)
- Pathology: patients with pathologically confirmed cervical cancer
- Location: primary tumor of the cervix
Key exclusion criteria (3 listed)
- Patients with no prior radiation therapy
- Patients without treatment
- Patients without regular follow-up
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observational studyOur study does not have any exposure factors. | Other | training group, validation group |
Primary outcome
Number of Circulating Tumor Cell Count
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Number of Circulating Tumor Cell Count (CTC count), cells/mL
Concentration of Alpha-fetoprotein (AFP)
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Alpha-fetoprotein (AFP), ng/mL
Concentration of Carcinoembryonic Antigen (CEA)
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Carcinoembryonic Antigen (CEA), ng/mL
Concentration of carbohydrate antigen 199 (CA199)
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of carbohydrate antigen 199 (CA199), U/mL
Concentration of Squamous Epithelial Cell Carcinoma Antigen (SCC-Ag)
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of Squamous Epithelial Cell Carcinoma Antigen (SCC-Ag), ng/mL
Concentration of carbohydrate antigen 125(CA125)
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Concentration of carbohydrate antigen 125(CA125), U/mL.
Count of Bacteria in urine
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Count of Bacteria in urine, colony-forming units (CFU)/mL.
Count of bacteria in stool
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
Count of bacteria in stool, colony-forming units (CFU)/mL
5-year overall survival rate of Participants
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
the proportion of patients who are alive at least 5 years after their initial diagnosis of cancer, regardless of the cause of death,%
disease-free survival of Participants
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
the length of time from the start of treatment until either the recurrence of cancer or death from any cause, months
progression-free survival of Participants
Time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.
the start of treatment until either the recurrence of cancer or death from any cause, months
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 12 Feb 2024.
- Individual participant dataSharing planned: No
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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.
Jinlu Ma study chair
First Affiliated Hospital of Xian Jiaotong University
Questions about this trial
What is this clinical trial studying?
An observational study of Uterine Cervical Neoplasms, run by First Affiliated Hospital Xi'an Jiaotong University.
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 2 inclusion and 3 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: opening soon, as of the sponsor's last update on 12 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
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 Jinlu Ma, Doctor (18992842959, majinlu@xjtufh.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Circulating Tumor Cell Count (time frame: From data of randomization until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 5 years.). The study lists 11 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 12 Feb 2024; AzTrials last refreshed its copy on 8 Nov 2026.