Clinical Research Directory

Clinical Translation Research on a Multi-omics Breast Cancer Distant Metastasis Prediction Model Empowered by Artificial Intelligence

Breast cancer is one of the most common malignancies in women worldwide, and distant metastasis is the main cause of poor prognosis and death. This project aims to construct a precision prediction system based on multi-omics (digital pathology, immunohistochemistry, proteomics, gene sequencing) and artificial intelligence to predict distant and organ-specific metastasis (bone, lung, liver, brain) in breast cancer, analyze its mechanisms, and provide new solutions for precision medicine.

Prospective Cohort Study on the Relationship Between Exosomal Peroxiredoxin 1 (PRDX1) and Postoperative Liver Metastasis

The occurrence of liver metastasis after curative surgery for resectable colorectal cancer is an important cause of death for patients. Accurately identifying high-risk patients for metastasis and intervening in them has important clinical significance. The pathological examination of surgical specimens failed to fully utilize valuable specimen information and accurately predict liver metastasis; The biomarkers secreted by tumors are metabolized in the liver through the portal vein, especially the particles such as extracellular vesicles secreted by tumors, which are ultimately diluted in peripheral blood and cannot be effectively detected. Our research group extracted an average of 11.25ml of blood (named blood derived from portal vein branch specimens, sdBlood for short) from 8 colorectal cancer radical surgery specimens. Compared with peripheral blood, protein mass spectrometry analysis revealed a significant increase in exosome proteins such as peroxidized redox protein 1 (PRDX1), which are highly correlated with metastasis. This project innovatively uses sdBlood, which has been overlooked by routine pathological examination, to detect the exosomal protein PRDX1 in sdBlood, which is significantly higher than the peripheral blood concentration. A prospective cohort study was established, including 252 patients with pathologically confirmed colorectal cancer after radical surgery. The incidence and time of liver metastasis were followed up and observed. Cox regression statistical analysis was used to determine the correlation between this marker and metastasis and determine its critical value, providing a basis for clinical diagnosis and treatment.

Comparation of Endoscopic Biopsy and Postoperative Specimen for the Molecular Diagnosis of Gastric Cancer

This study aims to test the hypothesis that the diagnosis for histological type, histological grade, LAUREN type, HER-2 expression, MSI/dMMR status, and EBV status in gastric cancer is at least as reliable when performed on endoscopic biopsy specimens as on surgical resection specimens.

Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT

This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. By quantifying the model's diagnostic performance on an independent test set-using AUC, sensitivity, specificity, positive/negative predictive values, and decision-curve analysis-we will establish a decision-support tool that can be seamlessly integrated into clinical PACS, thereby reducing staging errors, refining surgical planning, and improving patient outcomes.

Assessment of Women's Sexual Quality of Life After Benign Adnexal Surgery Using vNOTES Approach

Transvaginal Natural Orifice Transluminal Endoscopic Surgery (vNOTES) is a recent innovation in minimally invasive surgery which has already proven its non-inferiority to conventional abdominal laparoscopy (CAL) for hysterectomy in terms of efficiency and safety. However, the investigators note a lack of medical literature and no specific randomized controlled trial (RCT) assessing women's sexual function after vNOTES for benign adnexal surgery. The aim of this RCT is to confirm the non-inferiority of the vNOTES approach for benign adnexal pathology compared to CAL on women's sexual function. Secondary outcomes will evaluate vNOTES's efficiency, morbidity and postoperative complications compared to CAL for benign adnexal surgery. The relationship between adnexal mass morcellation/aspiration and the quality of the histological analysis on surgical specimens will also be evaluated as secondary outcome.

Optimising Renal Tumour Management Through Artificial Intelligence Modules

The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are: 1. whether the AI module can accurately provide tumor-related information such as Benign or malignant, subtypes, grading, stage, etc. by learning from preoperative CT images. 2. whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor. 3. whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival. Participants who complete a CT(usually Contrast-enhanced CT, CECT) examination and undergo radical or partial nephrectomy will carry out active surveillance and record postoperative survival data for 5 years.

Blood Specimen Collection For Laboratory Assay Research

To collect lab data from capillary and venous blood specimens for use in analytical research studies to support the development and validation of laboratory procedures.

Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are: 1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally. 2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants. Participants will: 1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them. 2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.

Decade-Long Insights Into Transperineal Prostate Biopsy in a West China Population

Prostate cancer (PCa) remains one of the most prevalent malignancies affecting men globally. The disease spectrum of PCa ranges from indolent tumors, which may require minimal to no intervention, to aggressive, potentially lethal forms. The complexity of PCa underscores the critical need for precise diagnostics, as early and accurate detection is key to improving patient outcomes and tailoring appropriate treatment strategies. There have been remarkable advancements in biopsy technology. Significant strides in magnetic resonance imaging (MRI), especially with the establishment of the Prostate Imaging Reporting and Data System (PI-RADS), have substantially improved the accuracy of PCa detection. The combination of MRI with traditional biopsy methods, including MRI-targeted biopsy (MRI-TBx) and systematic biopsy (SBx), also marks a major advancement in the field. Despite advances in PCa detection, the need for improving diagnostics and in-depth assessment of the latest PBx techniques through extensive, longitudinal studies remains critical. Besides, based on the world health organization (WHO) classification, PCa includes a range of pathological forms beyond the commonly known acinar adenocarcinoma. However, the prevalence and demographic distribution of non-adenocarcinoma types, as well as the characteristics of patients with these rarer forms, remain unclear. Leveraging PBx records spanning over 10 years and involving 10,038 cases, this study aims to shed light on temporal trends in PBx positivity, the evolving clinical profiles of PCa patients, and the differences in clinicopathological characteristics of PCa between Western and Asian populations.

AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

Development of a Predictive Model for the Risk of Metastatic Disease in PPGLs, a Retrospective Cohort Study

Phaeochromocytomas and paragangliomas (PPGLs) are tumours of the adrenal medulla and extra-adrenal sympathetic nervous system, some which can become metastatic. It is a very rare disease and the tumours are often detected late. Approximately 50 % of the tumours are caused by germline genetic variants screening programmes are recommended for patients and their family members; however, they are not yet well-targeted with respect to individual prognosis. In this study the investigatorscaim to characterize the genotype-phenotype associations in all Danish patients (n=400) diagnosed with PPGLs who have been followed in tertiary centres using medical records and national registries. To this end novel immunohistochemical, genetic, and epigenetic biomarkers in tumour tissues samples from biobank material (blood samples and tumour tissue) will be investigated to develop a comprehensive predictive algorithm for disease prognosis. The study will provide a clinical tool for an improved targeted screening program and subsequently prevention of disease development.

Application of Radiomics in Breast Cancer

Little is known about the correlation between ultrasound characteristics (conventional, elastography and contrast enhanced ultrasound (CEUS) )and pathological prognostic factors in breast cancer. The aim of this study was to explore the correlation between ultrasound characteristics and pathological prognostic factors using radiomics.

Evaluation of Methodological and Technical DEvelopmeNts to Improve the Quality and Comfort of Magnetic Resonance Imaging Exam on Voluntary Subjects

The EDEN study will make it possible to evaluate one or more development phases (from design to validation through optimization) of a method or a technique on voluntary subjects, in normal or pathological condition.

Adjuvant Radiotherapy in Patients With Early Endometrial Cancer

To compare the effect of vaginal brachytherapy as adjuvant treatment after operation when compared to pelvic external beam radiotherapy in patients with early endometrial cancer based on moderate risk molecules classification.