Gastrointestinal CancerNCT05648084
Artificial Intelligence and Cancer Staging in Upper Gastrointestinal Malignancies
An observational study of Gastrointestinal Cancer, run by Sebahattin Celik MD at 1 site in Turkey (Türkiye).
Trial summary
Esophageal and stomach cancers, which constitute cancers of the upper region of the digestive system, are cancers that are frequently observed and unfortunately have a low rate of cured patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastrointestinal Cancer
- How many people
- 50 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Dec 2022to 30 Jul 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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
- All sexes
- Adult, Older adult
Study population: esophageal and stomach cancer patients over 18 years old diagnosed in 2 centers in van turkey, Van Training and Research Hospital and Van Yuzuncu Yıl University.
Who may be able to join
- Being diagnosed with esophageal cancer (adenocarcinoma or squamous cancer)
- Being over 18 years old
- Having a tomography image before or after chemotherapy.
- Giving informed consent to participate in the study.
- Having final pathological staging after surgery.
Who cannot join
- Previous thoracic surgery.
- Having a recurrent tumor
- Inability to perform clinical staging due to technical reasons
- Drawings cannot be made due to poor tomography quality.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: To Investigate the Predictive Efficiency of Staging by Processing Tomography Images in Esophageal and Stomach Malignancies
Esophageal and stomach cancers, which constitute cancers of the upper region of the digestive system, are cancers that are frequently observed and unfortunately have a low rate of cured patients. In these cases, the stage of cancer at diagnosis is very important for two reasons; First, the stage of the cancer is directly related to the survival time. Secondly, treatment is planned according to the stage. Different treatments are applied to patients at different stages. Currently, the TNM staging (Tumor, Lymph Node and Metastases) system is the accepted one worldwide. Despite many advanced technology tools used in staging (Computed Tomography, Magnetic Resonance Imaging, Endoscopic Ultrasonography), there are still difficulties in correct staging before surgery or before-after neoadjuvant therapy. Artificial intelligence techniques are increasingly used in the field of health, especially in the diagnosis and treatment of cancers. Obtaining cancer details in radiological images, which cannot be noticed by the human eye, by analyzing big data with the help of algorithms gave rise to the application area of "radiomics". It is stated that with Radiomics, there will be improvements in both the diagnosis and staging of cancers and, accordingly, in the treatment. While there are studies on the use of endoscopic methods with artificial intelligence for the early diagnosis of esophageal cancers, a limited number of studies have been conducted on stage estimation from radiological images. In particular, there are not enough studies on the investigation of changes in tumor size after chemotherapy with artificial intelligence and the estimation of staging. In this study, it was aimed to investigate the predictive efficiency of staging and the accuracy of the algorithm developed with artificial intelligence by processing tomography images in a region where esophageal cancers are endemic as a primary outcome and to evaluate the post-treatment mortality, morbidity rates and complication rates of the patients as a secondary outcome.
- Conditions
- Gastrointestinal Cancer
- Medical subject headings
- Gastrointestinal Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Artificial intelligence's sensitivity and accuracy to predict the stage of the cancer
to investigate the predictive efficiency of staging and the accuracy of the algorithm developed with artificial intelligence by processing tomography images in a region where esophageal cancers are endemic
Measured: 1 year
Secondary outcomes (1)
to evaluate the post-treatment mortality, morbidity rates and complication rates of the patients
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Dec 2022
- Study start15 Dec 2022
- Primary completion30 Jun 2024
- Study completion30 Jul 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrointestinal 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sebahattin Celik MD
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Sebahattin Celik MD, Yuzuncu Yil University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05648084
- Sponsor's protocol ID
- 2021-667-19/20210502
- Status last verified
- Dec 2024
- First submitted
- 4 Dec 2022
- Last updated
- 17 Dec 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Investigate the Predictive Efficiency of Staging by Processing Tomography Images in Esophageal and Stomach Malignancies
- ClinicalTrials.gov ID
- NCT05648084
- Acronym / sponsor ID
- 2021-667-19/20210502
- Conditions
- Gastrointestinal Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 50 participants (actual)
- Lead sponsor
- Sebahattin Celik MDUniversity, hospital or other
- Study start
- 15 Dec 2022 (actual)
- Primary completion
- 30 Jun 2024 (actual)
- Study completion
- 30 Jul 2024 (actual)
- First posted
- 13 Dec 2022 (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.
Esophageal and stomach cancers, which constitute cancers of the upper region of the digestive system, are cancers that are frequently observed and unfortunately have a low rate of cured patients. In these cases, the stage of cancer at diagnosis is very important for two reasons; First, the stage of the cancer is directly related to the survival time. Secondly, treatment is planned according to the stage. Different treatments are applied to patients at different stages. Currently, the TNM staging (Tumor, Lymph Node and Metastases) system is the accepted one worldwide. Despite many advanced technology tools used in staging (Computed Tomography, Magnetic Resonance Imaging, Endoscopic Ultrasonography), there are still difficulties in correct staging before surgery or before-after neoadjuvant therapy. Artificial intelligence techniques are increasingly used in the field of health, especially in the diagnosis and treatment of cancers. Obtaining cancer details in radiological images, which cannot be noticed by the human eye, by analyzing big data with the help of algorithms gave rise to the application area of "radiomics". It is stated that with Radiomics, there will be improvements in both the diagnosis and staging of cancers and, accordingly, in the treatment. While there are studies on the use of endoscopic methods with artificial intelligence for the early diagnosis of esophageal cancers, a limited number of studies have been conducted on stage estimation from radiological images. In particular, there are not enough studies on the investigation of changes in tumor size after chemotherapy with artificial intelligence and the estimation of staging. In this study, it was aimed to investigate the predictive efficiency of staging and the accuracy of the algorithm developed with artificial intelligence by processing tomography images in a region where esophageal cancers are endemic as a primary outcome and to evaluate the post-treatment mortality, morbidity rates and complication rates of the patients as a secondary outcome.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- esophageal and stomach cancer patients over 18 years old diagnosed in 2 centers in van turkey, Van Training and Research Hospital and Van Yuzuncu Yıl University.
Key inclusion criteria (5 listed)
- Being diagnosed with esophageal cancer (adenocarcinoma or squamous cancer)
- Being over 18 years old
- Having a tomography image before or after chemotherapy.
- Giving informed consent to participate in the study.
- Having final pathological staging after surgery.
Key exclusion criteria (4 listed)
- Previous thoracic surgery.
- Having a recurrent tumor
- Inability to perform clinical staging due to technical reasons
- Drawings cannot be made due to poor tomography quality.
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
Artificial intelligence's sensitivity and accuracy to predict the stage of the cancer
Time frame: 1 year
to investigate the predictive efficiency of staging and the accuracy of the algorithm developed with artificial intelligence by processing tomography images in a region where esophageal cancers are endemic
1 secondary outcome
to evaluate the post-treatment mortality, morbidity rates and complication rates of the patients
Time frame: 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Dec 2024.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Gastrointestinal Cancer, run by Sebahattin Celik MD at 1 site in Turkey (Türkiye).
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 5 inclusion and 4 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: finished, as of the sponsor's last update on 17 Dec 2024. 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 Turkey (Türkiye). 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial intelligence's sensitivity and accuracy to predict the stage of the cancer (time frame: 1 year). 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 17 Dec 2024.