Differentiating Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Pancreatic Ductal Adenocarcinoma

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 Type: Observational
• Enrollment (Actual): 207

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

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).

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm.	AUC=(Sensitivity+Specificity)-1	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year
The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year
The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.	The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.	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.	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.	1 year

Collaborators and Investigators

• Sponsor: First Affiliated Hospital of Chongqing Medical University

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2013
• Primary Completion (Actual): July 1, 2022
• Study Completion (Estimated): March 1, 2024

Study Registration Dates

• First Submitted: February 29, 2024
• First Submitted That Met QC Criteria: March 7, 2024
• First Posted (Actual): March 12, 2024

Study Record Updates

• Last Update Posted (Actual): March 12, 2024
• Last Update Submitted That Met QC Criteria: March 7, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: Pancreatic ductal adenocarcinoma; Tumor stroma ratio; Contrastive language-image pretraining; Self-attention mechanism; Multi-modality feature fusion
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Carcinoma; Neoplasms, Glandular and Epithelial; Adenocarcinoma
• Other Study ID Numbers: liaohongfan

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No