Pancreatic Ductal AdenocarcinomaNCT06302881
Differentiating Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma
An observational study of Pancreatic Ductal Adenocarcinoma, run by First Affiliated Hospital of Chongqing Medical University.
Trial summary
This study introduces a novel transfer learning-based contrastive language-image pretraining adapter (CLIP-adapter) model for predicting the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma (PDAC) using preoperative dual-phase CT images. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Pancreatic Ductal Adenocarcinoma
- How many people
- 207 participants
- Where
- No sites listed yet
- Study dates
- Jan 2013to Mar 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: A total of 207 patients were chosen from three independent4 hospitals: the First Affiliated Hospital of Chongqing Medical University (FAHCQMU), Daping Hospital of Army Medical University (DPHAMU), and the Third Affiliated Hospital of Chongqing Medical University (TAHCQMU).
Who may be able to join
- patients with pathologically proven PDAC by surgical resection
- patients who underwent CT scan within a month before surgery
- observable pancreatic lesions on available images.
Who cannot join
- any anti-cancer therapy before CT scanning
- conspicuous interference or significant motion distortions found on images
- partial clinical data
- patients with liver metastases or peritoneal carcinomatosis prior to surgical intervention.
About this study
Official title: One Novel Transfer Learning-based CLIP Model Combined With Self-attention Mechanism for Differentiating the Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma: a Multi-center Retrospective Cohort Study
This study introduces a novel transfer learning-based contrastive language-image pretraining adapter (CLIP-adapter) model for predicting the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma (PDAC) using preoperative dual-phase CT images. The primary aim is to develop an efficient and accessible tool for risk stratification and personalized treatment planning.
Read the detailed description
The proposed novel Contrastive Language-Image Pretraining-Adapter (CLIP-adapter) model, leveraging transfer learning, framing CLIP and a self-attention mechanism for predicting TSR in PDAC, in order to exhibit high performance in distinguishing low and high TSR PDAC in the test cohort. We speculated the CLIP-adapter model outperformed single-phase models, specifically CLIP models based on arterial or venous phase images alone. The addition of a feature fusion module could enhance the model's differentiation capacity, emphasizing its superiority over single-phase models. Besides, the model we designed utilized both image and text information during network training, instead of focusing on images only. This underscores the importance of comprehensive assessment in PDAC imaging evaluation, with the potential to contribute to risk stratification and personalized treatment planning.
- Conditions
- Pancreatic Ductal Adenocarcinoma
- Keywords
- Pancreatic ductal adenocarcinoma, Tumor stroma ratio, Contrastive language-image pretraining, Self-attention mechanism, Multi-modality feature fusion
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 |
|---|---|---|
| low TSR and high TSR group | The assessment of the tumor-stroma ratio (TSR) entailed measuring the percentage of tumor and stroma constituents. Based on earlier research, 5/5 was deemed as ideal threshold of TSR measurement. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm.
AUC=(Sensitivity+Specificity)-1
Measured: 1 year
Secondary outcomes (5)
The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.
Measured: 1 year
The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.
Measured: 1 year
The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.
Measured: 1 year
The diagnostic positive predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic positive predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
Measured: 1 year
The diagnostic negative predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic negative predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2024
- Study startJan 2013
- Primary completionJul 2022
- Study completionMar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pancreatic Ductal AdenocarcinomaStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Chongqing Medical UniversityThe 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
- 207 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Liao Hongfan, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06302881
- Sponsor's protocol ID
- liaohongfan
- Status last verified
- Feb 2024
- First submitted
- 29 Feb 2024
- Last updated
- 12 Mar 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- One Novel Transfer Learning-based CLIP Model Combined With Self-attention Mechanism for Differentiating the Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma: a Multi-center Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT06302881
- Acronym / sponsor ID
- liaohongfan
- Conditions
- Pancreatic Ductal Adenocarcinoma
- Keywords
- Pancreatic ductal adenocarcinoma, Tumor stroma ratio, Contrastive language-image pretraining, Self-attention mechanism, Multi-modality feature fusion
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 207 participants (actual)
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical UniversityUniversity, hospital or other
- Study start
- Jan 2013 (actual)
- Primary completion
- Jul 2022 (actual)
- Study completion
- Mar 2024 (estimated)
- First posted
- 12 Mar 2024 (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 introduces a novel transfer learning-based contrastive language-image pretraining adapter (CLIP-adapter) model for predicting the tumor-stroma ratio (TSR) in pancreatic ductal adenocarcinoma (PDAC) using preoperative dual-phase CT images. The primary aim is to develop an efficient and accessible tool for risk stratification and personalized treatment planning.
Detailed description
The proposed novel Contrastive Language-Image Pretraining-Adapter (CLIP-adapter) model, leveraging transfer learning, framing CLIP and a self-attention mechanism for predicting TSR in PDAC, in order to exhibit high performance in distinguishing low and high TSR PDAC in the test cohort. We speculated the CLIP-adapter model outperformed single-phase models, specifically CLIP models based on arterial or venous phase images alone. The addition of a feature fusion module could enhance the model's differentiation capacity, emphasizing its superiority over single-phase models. Besides, the model we designed utilized both image and text information during network training, instead of focusing on images only. This underscores the importance of comprehensive assessment in PDAC imaging evaluation, with the potential to contribute to risk stratification and personalized treatment planning.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| low TSR and high TSR group | – | –The assessment of the tumor-stroma ratio (TSR) entailed measuring the percentage of tumor and stroma constituents. Based on earlier research, 5/5 was deemed as ideal threshold of TSR measurement. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- A total of 207 patients were chosen from three independent4 hospitals: the First Affiliated Hospital of Chongqing Medical University (FAHCQMU), Daping Hospital of Army Medical University (DPHAMU), and the Third Affiliated Hospital of Chongqing Medical University (TAHCQMU).
Key inclusion criteria (3 listed)
- patients with pathologically proven PDAC by surgical resection
- patients who underwent CT scan within a month before surgery
- observable pancreatic lesions on available images.
Key exclusion criteria (4 listed)
- any anti-cancer therapy before CT scanning
- conspicuous interference or significant motion distortions found on images
- partial clinical data
- patients with liver metastases or peritoneal carcinomatosis prior to surgical intervention.
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
The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
AUC=(Sensitivity+Specificity)-1
5 secondary outcomes
The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
The diagnostic positive predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
The diagnostic negative predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Mar 2024.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Ductal Adenocarcinoma, run by First Affiliated Hospital of Chongqing Medical University.
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 3 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: status not confirmed, as of the sponsor's last update on 12 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm. (time frame: 1 year). 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 12 Mar 2024.