Esophageal Squamous Cell CarcinomaDL-ESCCNCT07088354
Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
An observational study of Esophageal Squamous Cell Carcinoma and Neoadjuvant Immunochemotherapy, run by Tongji Hospital at 1 site in China.
Trial summary
This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response
- What is being tested?
- The high-throughput extraction of large amounts of quantitative image features from medical images
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2025to 1 Dec 2026
- 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: Patients diagnosed with esophageal squamous cell carcinoma who received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy, with available contrast-enhanced chest CT scans before and after neoadjuvant treatment, and who underwent surgical resection.
Who may be able to join
- Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
- Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
- Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
- Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Who cannot join
- Diagnosis of other malignancies.
- Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- Incomplete clinical data.
- Poor-quality CT imaging.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Lin Zhou, MSc contact
China1 site, 1 recruiting
- Recruiting
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
Yangkai Li, MD, PhD, +8613995516396, doclyk@163.com
Lin Zhou, MSc, zhoul0928@163.com
About this study
This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.
Read the detailed description
This multicenter retrospective study will collect chest CT images and clinical data from patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery following neoadjuvant immunochemotherapy between January 2019 and July 2025. Deep learning features will be extracted from the CT images to develop a predictive model of pathological complete response (pCR). The model's performance will be evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Additionally, SHapley Additive exPlanations (SHAP) analysis will be employed to quantify the contribution of CT imaging features to the model's predictions. This study aims to improve early identification of responders to neoadjuvant immunochemotherapy and support personalized treatment strategies for ESCC patients.
- Conditions
- Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response, Deep Learning
- Being tested
- The high-throughput extraction of large amounts of quantitative image features from medical images
- Keywords
- Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Deep Learning, Pathological Complete Response
- Medical subject headings
- Esophageal Squamous Cell Carcinoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The high-throughput extraction of large amounts of quantitative image features from medical imagesDiagnostic test
The high-throughput extraction of large amounts of quantitative image features from medical images
| Study group | Role | What they receive |
|---|---|---|
| ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery | Patients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery. Receives: Diagnostic Test: The high-throughput extraction of large amounts of quantitative image features from medical images |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathological Complete Response (pCR) Rate
The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery.
Measured: Assessed at the time of surgery, within 1 month post-treatment.
Secondary outcomes (1)
Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV)
Evaluation of the deep learning model's predictive performance using receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Measured: At the time of model validation, approximately one year on average after the completion of the research.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jul 2025
- Study start1 Mar 2025
- Primary completion1 Jun 2026 (estimated)
- Study completion1 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Esophageal Squamous Cell CarcinomaStudies 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
- Retrospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tongji Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yangkai Li, Tongji Hospital
- Investigators
- Yangkai Li, MD, PhD, Principal investigator, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07088354
- Sponsor's protocol ID
- ESRA-01
- Acronym
- DL-ESCC
- Status last verified
- Jul 2025
- First submitted
- 17 Jul 2025
- Last updated
- 28 Jul 2025
- Results posted
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
- ClinicalTrials.gov ID
- NCT07088354
- Acronym / sponsor ID
- DL-ESCC / ESRA-01
- Conditions
- Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response, Deep Learning
- Keywords
- Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Deep Learning, Pathological Complete Response
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Tongji HospitalUniversity, hospital or other
- Study start
- 1 Mar 2025 (actual)
- Primary completion
- 1 Jun 2026 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 28 Jul 2025 (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 aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.
Detailed description
This multicenter retrospective study will collect chest CT images and clinical data from patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery following neoadjuvant immunochemotherapy between January 2019 and July 2025. Deep learning features will be extracted from the CT images to develop a predictive model of pathological complete response (pCR). The model's performance will be evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Additionally, SHapley Additive exPlanations (SHAP) analysis will be employed to quantify the contribution of CT imaging features to the model's predictions. This study aims to improve early identification of responders to neoadjuvant immunochemotherapy and support personalized treatment strategies for ESCC patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery | – | Diagnostic Test: The high-throughput extraction of large amounts of quantitative image features from medical imagesPatients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with esophageal squamous cell carcinoma who received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy, with available contrast-enhanced chest CT scans before and after neoadjuvant treatment, and who underwent surgical resection.
Key inclusion criteria (4 listed)
- Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
- Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
- Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
- Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Key exclusion criteria (4 listed)
- Diagnosis of other malignancies.
- Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- Incomplete clinical data.
- Poor-quality CT imaging.
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 high-throughput extraction of large amounts of quantitative image features from medical imagesThe high-throughput extraction of large amounts of quantitative image features from medical images | Diagnostic test | ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery |
Primary outcome
Pathological Complete Response (pCR) Rate
Time frame: Assessed at the time of surgery, within 1 month post-treatment.
The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery.
1 secondary outcome
Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV)
Time frame: At the time of model validation, approximately one year on average after the completion of the research.
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 Jul 2025.
- 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.
Lin Zhou, MSc contact
Yangkai Li, MD, PhD principal investigator
Tongji Hospital
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 Squamous Cell Carcinoma and Neoadjuvant Immunochemotherapy, run by Tongji Hospital 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 4 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: looking for participants now, as of the sponsor's last update on 28 Jul 2025. 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 Yangkai Li, MD, PhD (+8613995516396, doclyk@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pathological Complete Response (pCR) Rate (time frame: Assessed at the time of surgery, within 1 month post-treatment.). 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 Jul 2025.