View clinical trials related to Lymphatic Metastasis.
Filter by:Lymph node metastasis (LNM) is a major prognostic factor in intrahepatic cholangiocarcinoma (ICC), and accurate preoperative prediction of the presence or absence of LNM has significant clinical implications in determining treatment strategy. Despite this, there are currently no reliable biomarkers established to detect LNM in ICC. This study seeks to develop a liquid biopsy assay that can accurately detect LNM before treatment in ICC patients.
This is a single center, prospective and observational study conducted in three stages to predict the NSCLC lymph node metastasis based on ctDNA/specific methylation molecular features combined with PET-CT imaging features and intervention study.
In our prior research, a risk scoring model for the occurrence of lymph node metastasis in patients who underwent radical gastrectomy for gastric cancer was established. To further validate this scoring model, a prospective study has been designed with the aim of prospectively assessing the model's clinical applicability.
The goal of this retrospective cohort analysis is to discover the impact of the involvement extent of tumor-draining lymph nodes (TDLNs) in patients who received neoadjuvant immunochemotherapy. The main question it aims to answer is the role of TDLN in the prediction of treatment effectiveness. Researches will compare tumor-draining lymph nodes metastasis (mTDLNs) group and non-draining lymph nodes metastasis (mNDLNs) group to see whether different metastatic patterns of mediastinal lymph nodes can indicate the treatment effectiveness.
The object of this study is to develop a model for prediction of lymph node metastasis among intrahepatic cholangiocarcinoma (ICC) patients. Intrahepatic cholangiocarcinoma is the second most common kind of primary liver cancer, accounting for approximately 10%-15%. There is a lack of agreement regarding the necessity of performing lymph node dissection (LND) in patients with ICC. Currently, the percentage of LND is below 50%, and the rate of sufficient LND (≥6) has plummeted to less than 20%. Consequently, a large proportion of patients are unable to acquire LN status, which hinders the following systematic treatment strategies after surgery:. Therefore, our objective is to construct a LN metastasis model utilizing machine learning techniques, including patients' clinical data and pathology information, with the goal of offering a reference for patients who have not undergone LND or have had inadequate LND.
The goal of this diagnostic test is to prospectively test the performance of pre-developed artificial intelligence (AI) diagnostic model for detecting pathological lymph node metastasis (LNM) of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests. Investigators will compare the diagnostic performance (sensitivity, specificity, etc.) of the AI model and routine pathological report issued by pathologists, to see if the AI model can improve the clinical workflow of pathological evaluation of LNM in prostate cancer in the real world.
This study involved a comprehensive analysis of 256 PTC patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH) and 499 patients from The Cancer Genome Atlas. DNA-based next-generation sequencing (NGS) and single-cell RNA sequencing (scRNA-seq) were employed to capture genetic alterations and TME heterogeneity. A deep learning multimodal model was developed by incorporating matched histopathology slide images, genomic, transcriptomic, immune cells data to predict LNM and disease-free survival (DFS).
This early-phase study will examine Vusolimogene Oderparepvec, a genetically modified oncolytic viral strain of the herpes simplex type 1 (HSV-1) virus, with potential oncolytic, immunostimulating and antineoplastic activities. Upon administration, vusolimogene oderparepvec specifically targets, infects and replicates in tumor cells and does not infect healthy cells. This results in tumor cell lysis and the release of virus particles which infect and replicate within nearby tumor cells, resulting in tumor cel death. The immune system is activated by the released tumor-associated antigens (TAAs) from the tumor cells creating an anti-tumor immune response against the tumor cells, thereby further killing the tumor cells. The virus itself also elicits a tumor-specific systemic immune and cytotoxic T-lymphocyte (CTL) response, thereby killing nearby non-infected tumor cells.
The goal of this observational study is to learn about the the pattern of metastasis of the No.253 lymph node in colorectal cancer. The main questions it aims to answer are: 1. What are the risk factors for metastasis to the No.253 lymph node? 2.What is the prognosis for patients with metastasis to the No.253 lymph node? Patients with descending colon cancer, sigmoid colon cancer, and rectal cancer who undergo curative surgery with dissection of the No.253 lymph node are included in this study
Gastric cancer is the third leading cause of morbidity and mortality among malignant tumors in China, and less than 30% of patients can be cured by surgery. Liver metastasis, retroperitoneal lymph node metastasis and peritoneal metastasis are the most common metastatic sites of gastric cancer, which are also the important causes of death. Improve the conversion of oligonucleotides transfer patients resection rate, prolonged progression-free survival of these patients, is an important direction to improve survival of patients with advanced gastric cancer; This study was a prospective, single-arm, multi-center clinical study. We plan to treat patients with gastric cancer/gastroesophageal junction adenocarcinoma with liver and/or retroperitoneal lymph node metastasis only with XELOX regimen + fruquinitinib + sintilimab for 4-6 cycles before surgery/ablation conversion therapy to achieve tumor-free status as far as possible. To explore the value of conversion therapy in patients with intrahepatic oligometastasis of gastric cancer.