Stomach NeoplasmsNCT05001321
Multi-center and Multi-modal Deep Learning Study of Gastric Cancer
An observational study of Stomach Neoplasms, run by First Hospital of China Medical University at 6 sites in China.
Trial summary
To assist postoperative pathological diagnosis and classification of gastric cancer by machine learning; To improve the accuracy of pathological diagnosis of gastric cancer by machine learning; To predict the effectiveness of treatment for gastric cancer by deep learning; To construct a model to predict the survival… Read more about this study
- Who is it for?
- Ages 18 to 79 years
- Conditions the study is for
- Stomach Neoplasms
- What is being tested?
- The whole abdomen contrast-enhanced CT scan and H&E stained sections and slides
- Type of treatment
- Surgery or procedure
- How many people
- 3,300 participants
- Where
- 6 sitesChina
- Study dates
- 1 Jul 2021to 31 Dec 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 to 79 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 3000 gastric cancer patients will participate in the phase I study, they will be divided into training group and internal validation group. 300 gastric cancer patients in five other medical centers will form the external validation group.
Who may be able to join
- The diagnosis of gastric cancer was confirmed by pathology;
- Preoperative enhanced abdominal CT;
- Available detailed clinical and pathological data;
- Integrated follow-up data.
Who cannot join
- The patients had severe underlying disease;
- Overall survival was less than 3 months;
- No detailed information could be collected.
Trial locations and contacts
6 sites in 1 country.
6 sites in 1 country
China6 sites
The fourth People's Hospital of Changzhou
Changzhou, Jiangsu, 213001
Chaoyang Central Hospital
Chaoyang, Liaoning, 122099
The General Hospital of Fushun Mining Bureau
Fushun, Liaoning, 113012
The Second Hospital of Shandong University
Ji'nan, Shandong, 250033
First Hospital of Jinzhou Medical University
Jinzhou, Liaoning, 121012
The First Affiliated Hospital of China Medical University
Shenyang, Liaoning, 110000
About this study
Official title: Multi-center and Multi-modal Deep Learning Study of Diagnosis, Therapeutic Outcome and Prognosis of Gastric Cancer
To assist postoperative pathological diagnosis and classification of gastric cancer by machine learning; To improve the accuracy of pathological diagnosis of gastric cancer by machine learning; To predict the effectiveness of treatment for gastric cancer by deep learning; To construct a model to predict the survival of gastric cancer patients by multimodal deep learning.
- Conditions
- Stomach Neoplasms
- Being tested
- The whole abdomen contrast-enhanced CT scan, H&E stained sections and slides
- Keywords
- Diagnosis, Prognosis
- Medical subject headings
- Stomach Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The whole abdomen contrast-enhanced CT scanRadiation
All the participants were measured by the whole abdomen contrast-enhanced CT scan.
H&E stained sections and slidesOther
HE pathological examination was performed on all specimens of enrolled patients.
| Study group | Role | What they receive |
|---|---|---|
| Training Group | Based on the inclusion criteria, 2000 gastric cancer patients will be recruited in the analysis. And a model will be constructed based on deep learning. Receives: Radiation: The whole abdomen contrast-enhanced CT scan; Other: H&E stained sections and slides | |
| Internal Validation Group | Based on the inclusion criteria, 1000 gastric cancer patients will be recruited in this group to verify the sensitivity and specificity of the constructed model. Receives: Radiation: The whole abdomen contrast-enhanced CT scan; Other: H&E stained sections and slides | |
| External Validation Group | Based on the inclusion criteria, 300 gastric cancer patients from 5 other medical centers will be recruited in this group to verify the sensitivity and specificity of the constructed model. Receives: Radiation: The whole abdomen contrast-enhanced CT scan; Other: H&E stained sections and slides |
What is being measured
The primary outcome is the main question the study is designed to answer.
Maximum diameter of tumor
To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.
Measured: 1 day
Growth pattern
To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.
Measured: 1 day
Enhancement pattern
To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.
Measured: 1 day
Enhancement degree
To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.
Measured: 1 day
Nucleus size
To obtain the nucleus size of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Measured: 1 day
Nucleus shape
To obtain the nucleus shape of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Measured: 1 day
Distribution of pixel intensity
To obtain the distribution of pixel intensity of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Measured: 1 day
Texture of nuclei
To obtain the texture of nuclei of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Measured: 1 day
Secondary outcomes (3)
Survival status
To analyze the survival status of patients with gastric cancer, involving dead and alive.
Measured: 1 day
Overall survival
To calculate the overall survival of patients with gastric cancer based on days to death and days to last follow-up.
Measured: 1 day
Recurrence/metastasis
To calculate the days to recurrence/metastasis of patients with gastric cancer.
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Aug 2021
- Study start1 Jul 2021
- Primary completion31 Jan 2022 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stomach NeoplasmsStudies 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 3,300 participants (actual)
Sponsor and investigators
- Lead sponsor
- First Hospital of China Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- The Second Hospital of Shandong University, Chaoyang Central Hospital, The General Hospital of Fushun Mining Bureau, The fourth People's Hospital of Changzhou, First Hospital of Jinzhou Medical University
- Responsible party
- Principal investigator, Kai Li, First Hospital of China Medical University
- Investigators
- Kai Li, MD, Principal investigator, First Hospital of China Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05001321
- Sponsor's protocol ID
- FirstHCMU_DL_oncology
- Status last verified
- Aug 2021
- First submitted
- 1 Aug 2021
- Last updated
- 11 Aug 2021
- 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 11 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-center and Multi-modal Deep Learning Study of Diagnosis, Therapeutic Outcome and Prognosis of Gastric Cancer
- ClinicalTrials.gov ID
- NCT05001321
- Acronym / sponsor ID
- FirstHCMU_DL_oncology
- Conditions
- Stomach Neoplasms
- Keywords
- Diagnosis, Prognosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,300 participants (actual)
- Lead sponsor
- First Hospital of China Medical UniversityUniversity, hospital or other
- Collaborators
- The Second Hospital of Shandong University, Chaoyang Central Hospital, The General Hospital of Fushun Mining Bureau, The fourth People's Hospital of Changzhou, First Hospital of Jinzhou Medical University
- Study start
- 1 Jul 2021 (actual)
- Primary completion
- 31 Jan 2022 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 11 Aug 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.
To assist postoperative pathological diagnosis and classification of gastric cancer by machine learning; To improve the accuracy of pathological diagnosis of gastric cancer by machine learning; To predict the effectiveness of treatment for gastric cancer by deep learning; To construct a model to predict the survival of gastric cancer patients by multimodal deep learning.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training Group | – | Radiation: The whole abdomen contrast-enhanced CT scan, Other: H&E stained sections and slidesBased on the inclusion criteria, 2000 gastric cancer patients will be recruited in the analysis. And a model will be constructed based on deep learning. |
| Internal Validation Group | – | Radiation: The whole abdomen contrast-enhanced CT scan, Other: H&E stained sections and slidesBased on the inclusion criteria, 1000 gastric cancer patients will be recruited in this group to verify the sensitivity and specificity of the constructed model. |
| External Validation Group | – | Radiation: The whole abdomen contrast-enhanced CT scan, Other: H&E stained sections and slidesBased on the inclusion criteria, 300 gastric cancer patients from 5 other medical centers will be recruited in this group to verify the sensitivity and specificity of the constructed model. |
Who may qualify
- Ages
- 18 to 79 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 3000 gastric cancer patients will participate in the phase I study, they will be divided into training group and internal validation group. 300 gastric cancer patients in five other medical centers will form the external validation group.
Key inclusion criteria (4 listed)
- The diagnosis of gastric cancer was confirmed by pathology;
- Preoperative enhanced abdominal CT;
- Available detailed clinical and pathological data;
- Integrated follow-up data.
Key exclusion criteria (3 listed)
- The patients had severe underlying disease;
- Overall survival was less than 3 months;
- No detailed information could be collected.
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 |
|---|---|---|
| The whole abdomen contrast-enhanced CT scanAll the participants were measured by the whole abdomen contrast-enhanced CT scan. | Radiation | External Validation Group, Internal Validation Group, Training Group |
| H&E stained sections and slidesHE pathological examination was performed on all specimens of enrolled patients. | Other | External Validation Group, Internal Validation Group, Training Group |
Primary outcome
Maximum diameter of tumor
Time frame: 1 day
To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.
Growth pattern
Time frame: 1 day
To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.
Enhancement pattern
Time frame: 1 day
To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.
Enhancement degree
Time frame: 1 day
To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.
Nucleus size
Time frame: 1 day
To obtain the nucleus size of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Nucleus shape
Time frame: 1 day
To obtain the nucleus shape of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Distribution of pixel intensity
Time frame: 1 day
To obtain the distribution of pixel intensity of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
Texture of nuclei
Time frame: 1 day
To obtain the texture of nuclei of postoperative H\&E stained sections and slides of gastric cancer by deep learning.
3 secondary outcomes
Survival status
Time frame: 1 day
Overall survival
Time frame: 1 day
Recurrence/metastasis
Time frame: 1 day
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Aug 2021.
Related trials for Stomach Neoplasms
Recruiting
TEAS Timing for Gastrectomy Recovery (TEAS-TIME Trial)
Gastric Cancer (GC), Stomach Neoplasms
RecruitingPhase 1
A Study to Learn About the Study Medicine Called PF-08098488 in People With Advanced Solid Tumors.
Pancreatic Ductal Adenocarcinoma, Esophageal Cancer
RecruitingPhase 1
Study of [177Lu]Lu-DWJ155 and [68Ga]Ga-DWJ155 in Patients With Solid Tumors
Breast Cancer, Non-small Cell Lung Cancer (NSCLC)
RecruitingPhase 1
A Phase 1 Study of IM-1617 in Participants With Advanced Cancer
Colorectal Cancer, Non-Small Cell Lung Cancer
RecruitingPhase 1
Study of RYZ401 in Subjects With Solid Tumors Expressing SSTRs.
GEP-NET, Gastroenteropancreatic Neuroendocrine Tumor
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.
Kai Li, MD principal investigator
First Hospital of China Medical University
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Stomach Neoplasms, run by First Hospital of China Medical University at 6 sites in China.
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 4 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: status not confirmed, as of the sponsor's last update on 11 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in China. 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?
Results are not available in the source record.
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?
Maximum diameter of tumor (time frame: 1 day). The study lists 8 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 11 Aug 2021.