Differentiating Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma

Pancreatic Ductal Adenocarcinoma Clinical Trial

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06302881
• Other study ID #: liaohongfan
• Status: Active, not recruiting
• Start date: January 2013
• Est. completion date: March 2024

Study information

• Verified date: February 2024
• Source: First Affiliated Hospital of Chongqing Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. The primary aim is to develop an efficient and accessible tool for risk stratification and personalized treatment planning.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 207
• Est. completion date: March 2024
• Est. primary completion date: July 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

1. patients with pathologically proven PDAC by surgical resection

2. patients who underwent CT scan within a month before surgery

3. observable pancreatic lesions on available images.

Exclusion Criteria:

1. any anti-cancer therapy before CT scanning

2. conspicuous interference or significant motion distortions found on images

3. partial clinical data

4. patients with liver metastases or peritoneal carcinomatosis prior to surgical intervention.

Related Conditions & MeSH terms

• Adenocarcinoma
• Pancreatic Ductal Adenocarcinoma

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
First Affiliated Hospital of Chongqing Medical University

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm.	AUC=(Sensitivity+Specificity)-1	1 year
Secondary	The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year
Secondary	The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year
Secondary	The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year
Secondary	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.	1 year
Secondary	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.	1 year