Staging

The GFAOP propose a simple and reproducible staging according to clinical, biological and radiological data. Develop standardized but different therapeutic recommendations based on the availability or lack of radiation therapy in the pilot unites who will adapt these recommendations.

Staging pancreatic cancer (PC) presents a clinical challenge. Triphasic whole body CT is the primary imaging method in diagnosing, staging and during follow up. Conventional PET/CT with 18F-labelled fluorodeoxyglucose (18F-FDG) has its limitations and therefore has a secondary role in imaging pancreatic cancer patients. These conventional imaging methods are good in detecting primary tumors and distant metastasis but poor in detecting local lymph node metastasis. A new PET tracer, fibroblast activation protein inhibitor (FAPI), targets FAP, a protein overexpressed in cancer-associated fibroblasts. It presents a potential new PET imagining tool. The objective of this prospective diagnostic study is to evaluate the diagnostic efficacy of 18F-FAPI-74 PET/CT in patients with PC. The aim is to evaluate the sensitivity and specificity of 18F-FAPI-74 in detection of local lymph node metastasis and distant metastasis in patient level in patients with PC in primary staging and when suspected recurrence. 100 patients with PC are enrolled on whom PET/CT studies are performed with the novel 18F-FAPI-74 tracer. The data will be collected between 2024-2026.

Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.

Ultrasound and magnetic resonance imaging (MRI) technologies have become essential tools for the diagnosis and preoperative staging assessment of bladder cancer. However, establishing a precise system suitable for clinical application remains a significant challenge, and accurate prediction of bladder cancer staging is not yet possible. Therefore, exploring a system for the precise diagnosis of preoperative staging of bladder cancer using ultrasound contrast enhancement or a combination of ultrasound contrast enhancement and MRI is of great importance. The applicant has previously demonstrated the accuracy of multimodal data analysis in the diagnosis and prediction of malignant tumors. Preliminary results from this study have shown that ultrasound contrast enhancement has a high diagnostic efficacy for the preoperative staging assessment of bladder cancer, with a high area under the curve (AUC) of 0.88, sensitivity of 83.3%, specificity of 92.5%, and accuracy of 90.8%. In this project, the applicant intends to further expand the sample size and, based on the combination of ultrasound contrast enhancement and MRI, develop a precise diagnostic system for the preoperative staging of bladder cancer to aid clinical treatment decisions.