Clinical Research Directory

Development and Validation of RCC Predicting Model With Emulated-target Trial

This single-center study utilizes real-world data (2012-2024) from 4700 renal cell carcinoma (RCC) patients at Peking University Third Hospital to: (1) Develop and validate a prognostic prediction model specifically for RCC patients, including those with venous tumor thrombus (VTT); (2) Compare the performance of this new model against existing RCC prediction models in both the overall RCC cohort and the VTT subgroup; (3) Employ an emulated target trial (ETT) methodology to evaluate whether risk-stratified treatment based on the prediction model (grouping patients as high/medium/low risk) improves survival outcomes (Overall Survival, Recurrence-Free Survival) and health economic outcomes (Quality-Adjusted Life-Years, Incremental Cost-Effectiveness Ratio), compared to non-stratified treatment group.

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)

This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.

Colorectal Cancer Dataset in Xijing Hospital From 2011

To compare the differences of clinical pathological, treatment and prognosis in the guided subgroups in colorectal cancer, the investigator enrolled all the colorectal cancer patients who underwent surgery and were hospitalized in the Xijing hospital.

AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors

Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.

Prognostic Significance of ctDNA in HL

Specific somatic mutations using ctDNA will be analyzed in predefined subgroups of cHL (e.g., age \<60 and ≥ 60 years, EBV). These mutations will be correlated with response to the treatment in the first line, in the relapse, during brentuximab vedotin and/or nivolumab treatment. Circulating tumor DNA will be correlated with the extent of tumor mass and chemo/radiotherapy.