Clinical trial NCT06302881: Differentiating Tumor-stroma Ratio in Pancreatic Ductal Adenocarcinoma
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
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Pancreatic Ductal Adenocarcinoma
Keywords: Pancreatic ductal adenocarcinoma
Tumor stroma ratio
Contrastive language-image pretraining
Self-attention mechanism
Multi-modality feature fusion
Lead sponsor: First Affiliated Hospital of Chongqing Medical University
Sponsor class: OTHER
First posted: 2024-03-12
Enrollment: 207
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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).

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.

Eligibility criteria: 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.

Primary outcomes:
measure: The diagnostic AUC value of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
Secondary outcomes:
measure: The diagnostic accuracy of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
measure: The diagnostic sensitivity of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
measure: The diagnostic specificity of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
measure: The diagnostic positive predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
measure: The diagnostic negative predictive value of pancreatic ductal adenocarcinoma with deep learning algorithm.
timeFrame: 1 year
Other outcomes:
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