Pancreatic AdenocarcinomaNCT06760234
Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis
An observational study of Pancreatic Adenocarcinoma, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
Trial summary
This study describes the development and validation of a deep learning prediction model, which extracts deep learning features from preoperative enhanced CT scans and analyzes postoperative pathological specimens of pancreatic cancer patients. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Pancreatic Adenocarcinoma
- How many people
- 247 participants
- Where
- 1 siteChina
- Study dates
- 5 Jul 2024to 3 Jan 2026
- 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 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Pancreatic cancer patients who were undergo surgery and received adjuvant chemotherapy after surgery.
Who may be able to join
- Patients with pancreatic cancer, diagnosed through pathology;
- Patients underwent surgery and received adjuvant chemotherapy after surgery.
Who cannot join
- Missing or inadequate quality of CT,
- Incomplete clinical or pathological data.
- Multiple primary malignancies;
- History of malignancy.
Trial locations and contacts
1 site in 1 country.
China1 site
the Second Affiliated Hospital Zhejiang University School of Medicine
Hangzhou, Zhejiang, 310009
About this study
Official title: Prediction of Pancreatic Cancer Prognosis Using a Multimodal Deep Learning Model Based on Intratumoral Immune Microenvironment
This study describes the development and validation of a deep learning prediction model, which extracts deep learning features from preoperative enhanced CT scans and analyzes postoperative pathological specimens of pancreatic cancer patients. The aim is to predict patient prognosis and response to chemotherapy treatment.
Read the detailed description
This study retrospectively collected enhanced CT scan data, pathological paraffin blocks, and clinical data from pancreatic cancer patients who underwent surgery at multiple centers between March 2013 and May 2024. The pathological paraffin blocks were stained using immunohistochemistry for prognostic immune microenvironment markers, and patients were classified based on these results. Subsequently, deep learning features were extracted from enhanced CT scans, and a multimodal prediction model was constructed using imaging features and clinical information. The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
- Conditions
- Pancreatic Adenocarcinoma
- Being tested
- No Interventions
- Keywords
- Deep Learning Model, Pancreatic adenocarcinoma, prognosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionsDiagnostic test
The high-throughput extraction of quantitative image features from medical images
No InterventionsDiagnostic test
Immunohistochemical analysis
| Study group | Role | What they receive |
|---|---|---|
| Training Cohort | Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Second Affiliated Hospital, Zhejiang University School of Medicine Receives: Diagnostic Test: No Interventions | |
| Test Cohort | Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Fourth Affiliated Hospital, Zhejiang University School of Medicine and Hangzhou Hosptial of Traditional Chinese Medicine Receives: Diagnostic Test: No Interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of deep learning model
The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Measured: Baseline treatment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jan 2025
- Study start5 Jul 2024
- Primary completion15 Dec 2024
- Study completion3 Jan 2026
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 Pancreatic AdenocarcinomaStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- 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
- 247 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou Hospital of Traditional Chinese Medicine
- Responsible party
- Sponsor
- Investigators
- Yulian Wu, PhD., Principal investigator, Second Affiliated Hospital of Zhejiang University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06760234
- Sponsor's protocol ID
- PCPAI
- Status last verified
- Jul 2024
- First submitted
- 29 Dec 2024
- Last updated
- 7 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Fan Y, Du B, Pu K, Sun Y, Lv C, Hu S, Song T, Wu R, Chen Y, Tang J, Zhong Y, Bian W, Wu J, Zhang H, Ding Y, Xu H, Wu Y, Li X. Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma. NPJ Digit Med. 2026 Jan 3;9(1):123. doi: 10.1038/s41746-025-02303-9.
PubMed 41484243
Study at a glance
From the registry record, last updated by the sponsor on 7 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Pancreatic Cancer Prognosis Using a Multimodal Deep Learning Model Based on Intratumoral Immune Microenvironment
- ClinicalTrials.gov ID
- NCT06760234
- Acronym / sponsor ID
- PCPAI
- Conditions
- Pancreatic Adenocarcinoma
- Keywords
- Deep Learning Model, Pancreatic adenocarcinoma, prognosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 247 participants (actual)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Collaborators
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou Hospital of Traditional Chinese Medicine
- Study start
- 5 Jul 2024 (actual)
- Primary completion
- 15 Dec 2024 (actual)
- Study completion
- 3 Jan 2026 (actual)
- First posted
- 6 Jan 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 describes the development and validation of a deep learning prediction model, which extracts deep learning features from preoperative enhanced CT scans and analyzes postoperative pathological specimens of pancreatic cancer patients. The aim is to predict patient prognosis and response to chemotherapy treatment.
Detailed description
This study retrospectively collected enhanced CT scan data, pathological paraffin blocks, and clinical data from pancreatic cancer patients who underwent surgery at multiple centers between March 2013 and May 2024. The pathological paraffin blocks were stained using immunohistochemistry for prognostic immune microenvironment markers, and patients were classified based on these results. Subsequently, deep learning features were extracted from enhanced CT scans, and a multimodal prediction model was constructed using imaging features and clinical information. The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training Cohort | – | Diagnostic Test: No InterventionsPatients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Second Affiliated Hospital, Zhejiang University School of Medicine |
| Test Cohort | – | Diagnostic Test: No InterventionsPatients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Fourth Affiliated Hospital, Zhejiang University School of Medicine and Hangzhou Hosptial of Traditional Chinese Medicine |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Pancreatic cancer patients who were undergo surgery and received adjuvant chemotherapy after surgery.
Key inclusion criteria (2 listed)
- Patients with pancreatic cancer, diagnosed through pathology;
- Patients underwent surgery and received adjuvant chemotherapy after surgery.
Key exclusion criteria (4 listed)
- Missing or inadequate quality of CT,
- Incomplete clinical or pathological data.
- Multiple primary malignancies;
- History of malignancy.
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 |
|---|---|---|
| No InterventionsThe high-throughput extraction of quantitative image features from medical images | Diagnostic test | Test Cohort, Training Cohort |
| No InterventionsImmunohistochemical analysis | Diagnostic test | Test Cohort, Training Cohort |
Primary outcome
Performance of deep learning model
Time frame: Baseline treatment
The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
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 7 Jan 2026.
- Derived from this study: PubMed 41484243Fan Y, Du B, Pu K, Sun Y, Lv C, Hu S, Song T, Wu R, Chen Y, Tang J, Zhong Y, Bian W, Wu J, Zhang H, Ding Y, Xu H, Wu Y, Li X. Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma. NPJ Digit Med. 2026 Jan 3;9(1):123. doi: 10.1038/s41746-025-02303-9.
- 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. This study is not recruiting at the moment.
Yulian Wu, PhD. principal investigator
Second Affiliated Hospital of Zhejiang University School of Medicine
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 Pancreatic Adenocarcinoma, run by Second Affiliated Hospital, Zhejiang University, School of Medicine 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 2 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 7 Jan 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?
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?
Performance of deep learning model (time frame: Baseline treatment). 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 NCT06760234, 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 7 Jan 2026.