An observational study in Pancreatic Tumor, sponsored by First Affiliated Hospital of Chongqing Medical University. Completed at 1 site in China. Open to participants aged 18 Years to 100 Years. Per ClinicalTrials.gov, last updated 2024-10-15.
Sponsored by First Affiliated Hospital of Chongqing Medical University · Observational
The MVIT-MLKA model, with its complex architecture combining CNNs and Transformers, excels in image feature extraction and capturing long-range dependencies. This gives it strong adaptability and robustness in lesion detection and classification tasks. Compared to traditional machine learning methods and other deep learning models, MVIT-MLKA not only performs better in terms of accuracy, sensitivity, and specificity but also helps reduce inter-observer variability, enhancing diagnostic consistency among physicians.
Although the model showed slight fluctuations in performance on external datasets, it still outperforms other models overall and holds significant potential for clinical applications. With further optimization to improve its generalization capabilities, MVIT-MLKA could become a powerful tool for diagnosing benign and malignant lesions, providing more consistent and accurate support in clinical practice.
Accurate differentiation between benign and malignant pancreatic lesions is critical for patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography images to predict benign and malignant pancreatic lesions. This retrospective study across three medical centers constituted a training cohort, an internal testing cohort, and an external validation cohorts. A novel hybrid model, Multi-Scale Large Kernel Attention with Mobile Vision Transformer (MVIT-MLKA), integrating CNN and Transformer architectures, was developed to classify pancreatic lesions. We compared the model's performance with traditional machine learning and deep learning methods. Moreover, we evaluated radiologists' diagnostic accuracy with and without the optimal model assistance.The MVIT-MLKA model demonstrated superior performance for predicting pancreatic lesions, outperforming traditional models and standard CNNs and Transformers. Radiologists assisted by the MVIT-MLKA model showed significant improvements in diagnostic performance compared to those without model assistance, with notable increases in both accuracy and sensitivity. Model interpretability was enhanced through Grad-CAM visualization, effectively highlighting key lesion areas.The MVIT-MLKA model effectively differentiates between benign and malignant pancreatic lesions, surpassing traditional methods and enhancing radiologist performance. This suggests that integrating advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies in clinical practices.
3,235 studies on the registry are indexed under Pancreatic Neoplasms; 899 are open to participants now.
This study's enrollment of 864 is above the median of 200 across 620 observational studies indexed under Pancreatic Neoplasms.
Browse Pancreatic Neoplasms studies →First Affiliated Hospital of Chongqing Medical University is the lead sponsor of 95 studies on the registry; 42 are open to participants now.
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all people were selected from three medical centers in chongqing province
All patients with malignant pancreatic lesions had confirmed histopathology according to the 8th edition of the American Joint Committee on Cancer TNM staging system [25]; Lesions were classified as benign if they had either histopathologic confirmation or demonstrated benign characteristics with stability over at least one year of follow-up on CT or MRI imaging; (2) Patients underwent preoperative abdominal contrast-enhanced CT scans; (3) No anti-tumor treatment was conducted before the CT scan
Exclusion Criteria:
(1) Patients with significant motion artifacts or other imaging issues; (2) A time gap of one month or more between the CT scan and subsequent surgery; (3) Tumors less than 10 mm in maximum diameter.
Benign Lesion Group: This cohort includes patients diagnosed with benign pancreatic lesions, such as pancreatic cysts or neuroendocrine tumors. These patients typically do not require aggressive treatments like surgery or chemotherapy and are managed with regular monitoring and non-invasive interventions. Histopathological confirmation or stability over a minimum of one year of follow-up without progression is used to classify lesions as benign. Malignant Lesion Group: This cohort comprises patients diagnosed with malignant pancreatic lesions, such as pancreatic ductal adenocarcinoma (PDAC). These patients often require more aggressive treatment options, including surgery, chemotherapy, and radiotherapy. The malignancy of the lesions is confirmed through histopathological analysis, and the cohort focuses on cases with clear evidence of tumor growth and progression.
Procedure: Whipple procedure
Typically used for treating pancreatic cancer, particularly tumors located in the head of the pancreas.
overall survival time
The main outcome measure in this study was overall survival (OS), calculated from the date of the initial surgery to the date of death from any cause or the last follow-up.
Time frame: 1 year
Plan to share: No — We intend to make the IPD available to qualified researchers upon request. This will be subject to ethical approval and adherence to relevant data protection regulations.
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This study is completed, as verified in Oct 2024. You cannot join it, but the record below documents what was studied.
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First Affiliated Hospital of Chongqing Medical University