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

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06302881

Enrollment

207

Registered

2024-03-12

Start date

2013-01-31

Completion date

2024-03-31

Last updated

2024-03-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pancreatic Ductal Adenocarcinoma

Keywords

Pancreatic ductal adenocarcinoma, Tumor stroma ratio, Contrastive language-image pretraining, Self-attention mechanism, Multi-modality feature fusion

Brief 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.

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

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.

Design outcomes

Primary

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

Secondary

Measure	Time frame	Description
The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year	The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year	The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.
The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.	1 year	The diagnostic specificity 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 positive 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	The diagnostic negative predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.

Outcome results