Gastric CancerNCT06035250
AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
An observational study of Gastric Cancer and Image, run by Chinese Academy of Sciences at 22 sites in 2 countries.
Trial summary
This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastric Cancer, Image, Pathology
- What is being tested?
- Neoadjuvant Chemotherapy
- How many people
- 200 participants
- Where
- 22 sitesin 2 countries
- Study dates
- 10 Sep 2023to 31 Dec 2029
- 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
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population comprises gastric cancer patients from various hospitals. Participants are individuals diagnosed with advanced gastric cancer and are currently undergoing neoadjuvant chemotherapy treatments. Selection is based on criteria such as age, specific diagnosis, past treatment history, and the clarity of their medical images and pathology images.
Who may be able to join
- Age 18 years or older;
- Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
- Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
- Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
- CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
- Patients possess comprehensive preoperative clinical information and post-operative TRG grading.
Who cannot join
- Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
- Patients diagnosed with other concurrent tumors.
Trial locations and contacts
22 sites in 2 countries. Contact the study team or a site near you to ask about taking part.
22 sites in 2 countries
China21 sites, 21 recruiting
- Not yet recruiting
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Beijing
Xinming Zhao
- Not yet recruiting
Peking Union Medical College Hospital
Beijing
Zhenyu Jin
- Recruiting
Peking University Cancer Hospital & Institute
Beijing
Lei Tang
- Not yet recruiting
Peking University People's Hospital
Beijing
Yi Wang
- Not yet recruiting
Xiangya Hospital of Central South University
Changsha
Weihua Liao
- Not yet recruiting
Fujian Cancer Hospital
Fuzhou
Yangming Li
- Recruiting
Fujian Medical University Union Hospital
Fuzhou
Changming Huang
- Not yet recruiting
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Guangzhou
Shuzhong Cui
- Not yet recruiting
First Affiliated Hospital, Sun Yat-Sen University
Guangzhou
Shenping Yu
- Not yet recruiting
Nanfang Hospital of Southern Medical University
Guangzhou
Guoxin Li
- Recruiting
Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou
Xiaochun Meng
- Recruiting
Yunnan Cancer Hospital
Kunming
Zhenhui Li
- Not yet recruiting
Cancer Hospital of Guangxi Medical University
Nanning
Guanqiao Jin
- Not yet recruiting
The Affiliated Hospital of Qingdao University
Qingdao
Hexiang Wang
- Not yet recruiting
Ruijin Hospital
Shanghai
Jun Zhang
- Not yet recruiting
First Hospital of China Medical University
Shenyang
Zhenning Wang
- Not yet recruiting
The First Affiliated Hospital of Soochow University
Suzhou
Jie Bao
- Not yet recruiting
Tianjin Medical University Cancer Institute and Hospital
Tianjin
Zhaoxiang Ye
- Recruiting
Henan Cancer Hospital
Zhengzhou
Jing Li
- Recruiting
The First Affiliated Hospital of Zhengzhou University
Zhengzhou
Jianbo Gao
- Recruiting
Zhenjiang First People's Hospital
Zhenjiang
Xiuhong Shan
Italy1 site, 1 recruiting
- Recruiting
San Raffaele University Hospital, Italy
Milan
Francesco De Cobelli
About this study
Official title: Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.
Read the detailed description
This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants.
Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset.
Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.
Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.
Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.
TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.
Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.
- Conditions
- Gastric Cancer, Image, Pathology
- Being tested
- Neoadjuvant Chemotherapy
- Keywords
- Gastric Cancer, Neoadjuvant Chemotherapy, Radiomics, Treatment Outcome Prediction, Pathomics, Radiopathomics
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Neoadjuvant ChemotherapyDrug
Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.
| Study group | Role | What they receive |
|---|---|---|
| Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy | This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen. Receives: Drug: Neoadjuvant Chemotherapy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model
The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.
Measured: two months
Accuracy of TRG prediction by the AI model
Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.
Measured: two months
Secondary outcomes (2)
Progression-Free Survival (PFS) at 3 years
The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.
Measured: Three years
Overall Survival (OS) at 5 years
The duration from the date of patient confirmation to the date of death of the patient.
Measured: Five years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2023
- Study start10 Sep 2023
- Primary completion31 Aug 2024 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric 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
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese Academy of Sciences
- Sponsor type
- Other government
- Collaborators
- Peking University Cancer Hospital & Institute, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Yunnan Cancer Hospital, Henan Cancer Hospital, Zhenjiang First People's Hospital, First Hospital of China Medical University, Cancer Hospital of Guangxi Medical University, Peking University People's Hospital, Tianjin Medical University Cancer Institute and Hospital, The First Affiliated Hospital of Zhengzhou University, Nanfang Hospital, Southern Medical University, The Affiliated Hospital of Qingdao University, Ruijin Hospital, Sixth Affiliated Hospital, Sun Yat-sen University, Peking Union Medical College Hospital, Xiangya Hospital of Central South University, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, The First Affiliated Hospital of Soochow University, First Affiliated Hospital, Sun Yat-Sen University, Fujian Medical University Union Hospital, Fujian Cancer Hospital, San Raffaele University Hospital, Italy
- Responsible party
- Principal investigator, Di Dong, Institute of Automation, Chinese Academy of Sciences
- Investigators
- Yali Zang, Ph.D., Study director, Institute of Automation, Chinese Academy of Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06035250
- Sponsor's protocol ID
- CASMI004
- Status last verified
- Sep 2023
- First submitted
- 13 Aug 2023
- Last updated
- 28 Sep 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
- ClinicalTrials.gov ID
- NCT06035250
- Acronym / sponsor ID
- CASMI004
- Conditions
- Gastric Cancer, Image, Pathology
- Keywords
- Gastric Cancer, Neoadjuvant Chemotherapy, Radiomics, Treatment Outcome Prediction, Pathomics, Radiopathomics
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Chinese Academy of SciencesOther government
- Collaborators
- Peking University Cancer Hospital & Institute, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Yunnan Cancer Hospital, Henan Cancer Hospital, Zhenjiang First People's Hospital, First Hospital of China Medical University, Cancer Hospital of Guangxi Medical University, Peking University People's Hospital, Tianjin Medical University Cancer Institute and Hospital, The First Affiliated Hospital of Zhengzhou University, Nanfang Hospital, Southern Medical University, The Affiliated Hospital of Qingdao University, Ruijin Hospital, Sixth Affiliated Hospital, Sun Yat-sen University, Peking Union Medical College Hospital, Xiangya Hospital of Central South University, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, The First Affiliated Hospital of Soochow University, First Affiliated Hospital, Sun Yat-Sen University, Fujian Medical University Union Hospital, Fujian Cancer Hospital, San Raffaele University Hospital, Italy
- Study start
- 10 Sep 2023 (actual)
- Primary completion
- 31 Aug 2024 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 13 Sep 2023 (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.
This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.
Detailed description
This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants.
Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset.
Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.
Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.
Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.
TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.
Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy | – | Drug: Neoadjuvant ChemotherapyThis group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population comprises gastric cancer patients from various hospitals. Participants are individuals diagnosed with advanced gastric cancer and are currently undergoing neoadjuvant chemotherapy treatments. Selection is based on criteria such as age, specific diagnosis, past treatment history, and the clarity of their medical images and pathology images.
Key inclusion criteria (6 listed)
- Age 18 years or older;
- Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
- Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
- Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
- CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
Key exclusion criteria (2 listed)
- Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
- Patients diagnosed with other concurrent tumors.
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 |
|---|---|---|
| Neoadjuvant ChemotherapyParticipants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition. | Drug | Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy |
Primary outcome
Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model
Time frame: two months
The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.
Accuracy of TRG prediction by the AI model
Time frame: two months
Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.
2 secondary outcomes
Progression-Free Survival (PFS) at 3 years
Time frame: Three years
Overall Survival (OS) at 5 years
Time frame: Five years
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 28 Sep 2023.
- Individual participant dataSharing planned: Yes. Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
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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.
Yali Zang, Ph.D. study director
Institute of Automation, Chinese Academy of Sciences
22 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer and Image, run by Chinese Academy of Sciences at 22 sites in 2 countries.
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 6 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 28 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 22 sites in China, Italy. 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 Di Dong, Ph.D. (+86 13811833760, di.dong@ia.ac.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model (time frame: two months). The study lists 2 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 28 Sep 2023.