Esophageal CancerNCT07354295
Integrating Multimodal AI to Predict Treatment Response and Refine Risk Stratification in Esophageal Cancer （Radiogenomics-Esophagus）
An observational study of Esophageal Cancer, run by Shu Peng at 1 site in China.
Trial summary
This AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Esophageal Cancer
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 26 Jul 2025to 30 Sep 2030
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients diagnosed with esophgeal cancer and have received treatment in Tongji hospital
Who may be able to join
- Histopathologically diagnosed esophageal cancer
- Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
- No other primary malignant tumors
- Provision of informed consent
- Availability of pre-treatment CT imaging
Who cannot join
- Imaging data quality insufficient for analysis
- Presence of another primary malignant tumor
- Severe systemic disease
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Tongji hospital, Tongji medical college, Huazhong university of science and technology
Wuhan, Other (Non U.s.), 430030
Shu Peng, doctor, +8618571716422, drpeng90@hotmail.com
drpeng90@hotmail.com
About this study
Official title: Multimodal AI-based Therapy Response Prediction and Risk Stratification for Esophageal Cancer
This AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis.
Read the detailed description
Built upon retrospective cohorts for model development and rigorously validated in prospective cohorts, the proposed AI predictive model integrates multimodal data (radiomics, pathomics, genomics, and multi-omics)-each reflecting distinct dimensions of tumor heterogeneity-to enable joint prediction of treatment response and clinical outcomes.
- Conditions
- Esophageal Cancer
- Keywords
- Esophageal Cancer; AI; prediction; prognosis
- Medical subject headings
- Esophageal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Surgical resection cohort | neither neoajuvant therapy nor anti-tumor treatment prior to surgery | |
| neoadjuvant therapy cohort | received neoadjuvant therapy and esophagectomy | |
| conservative treatment | concervative treatment includes chemo/immuno/radiotherapy and targeted theray | |
| Endoscopic submucosal dissection (ESD) | Endoscopic submucosal dissection (ESD) |
What is being measured
The primary outcome is the main question the study is designed to answer.
overall survival
overall survival rate in 3-years
Measured: From enrollment to the end of treatment at 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Jan 2026
- Study start26 Jul 2025
- Primary completion30 Sep 2030 (estimated)
- Study completion30 Sep 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Esophageal 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
- Other
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shu Peng
- Sponsor type
- University, hospital or other
- Collaborators
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, The First Affiliated Hospital of Henan University of Science and Technology, Henan Provincial People's Hospital, Zhongnan Hospital, Renmin Hospital of Wuhan University
- Responsible party
- Sponsor-investigator, Shu Peng, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07354295
- Sponsor's protocol ID
- 4059393
- Status last verified
- Feb 2026
- First submitted
- 12 Jan 2026
- Last updated
- 10 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No: In accordance with the institution's data confidentiality requirements
Publications
Xia T, Peng S, Yang F, Wang X, Yao W. Data-driven models in locally advanced oesophageal cancer. Lancet. 2025 Sep 27;406(10510):1334-1335. doi: 10.1016/S0140-6736(25)01766-0. No abstract available.
PubMed 41015514
Study at a glance
From the registry record, last updated by the sponsor on 10 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal AI-based Therapy Response Prediction and Risk Stratification for Esophageal Cancer
- ClinicalTrials.gov ID
- NCT07354295
- Acronym / sponsor ID
- 4059393
- Conditions
- Esophageal Cancer
- Keywords
- Esophageal Cancer; AI; prediction; prognosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Shu PengUniversity, hospital or other
- Collaborators
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, The First Affiliated Hospital of Henan University of Science and Technology, Henan Provincial People's Hospital, Zhongnan Hospital, Renmin Hospital of Wuhan University
- Study start
- 26 Jul 2025 (actual)
- Primary completion
- 30 Sep 2030 (estimated)
- Study completion
- 30 Sep 2030 (estimated)
- First posted
- 21 Jan 2026 (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 AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis.
Detailed description
Built upon retrospective cohorts for model development and rigorously validated in prospective cohorts, the proposed AI predictive model integrates multimodal data (radiomics, pathomics, genomics, and multi-omics)-each reflecting distinct dimensions of tumor heterogeneity-to enable joint prediction of treatment response and clinical outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Surgical resection cohort | – | –neither neoajuvant therapy nor anti-tumor treatment prior to surgery |
| neoadjuvant therapy cohort | – | –received neoadjuvant therapy and esophagectomy |
| conservative treatment | – | –concervative treatment includes chemo/immuno/radiotherapy and targeted theray |
| Endoscopic submucosal dissection (ESD) | – | –Endoscopic submucosal dissection (ESD) |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients diagnosed with esophgeal cancer and have received treatment in Tongji hospital
Key inclusion criteria (5 listed)
- Histopathologically diagnosed esophageal cancer
- Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.)
- No other primary malignant tumors
- Provision of informed consent
- Availability of pre-treatment CT imaging
Key exclusion criteria (3 listed)
- Imaging data quality insufficient for analysis
- Presence of another primary malignant tumor
- Severe systemic disease
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
overall survival
Time frame: From enrollment to the end of treatment at 3 years
overall survival rate in 3-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 10 Mar 2026.
- Results publication: PubMed 41015514Xia T, Peng S, Yang F, Wang X, Yao W. Data-driven models in locally advanced oesophageal cancer. Lancet. 2025 Sep 27;406(10510):1334-1335. doi: 10.1016/S0140-6736(25)01766-0. No abstract available.
- Data-driven models in locally advanced oesophageal cancerLink provided by the sponsor.
- Individual participant dataSharing planned: No. In accordance with the institution's data confidentiality requirements
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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.
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 Esophageal Cancer, run by Shu Peng at 1 site 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 5 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: looking for participants now, as of the sponsor's last update on 10 Mar 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 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?
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 Shu Peng, Doctor (+8618571716422, drpeng90@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
overall survival (time frame: From enrollment to the end of treatment at 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07354295, which also lists 1 linked publication.
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 10 Mar 2026.