View clinical trials related to Lymphatic Metastasis.
Filter by:The objective of this research is to investigate the clinical outcomes of modified surgical techniques such as omitting the cervical linea alba suture in transthoracic endoscopic thyroidectomy. Furthermore, the study requires the collection of normal thyroid tissues, benign and malignant thyroid tumors, and lymph nodes to further clarify the mechanisms associated with the initiation, progression, metastasis, and recurrence of thyroid cancer.
This study is a retrospective exploratory trial conducted at a single center, aiming to develop and validate a preoperative lymphatic metastasis model for cervical cancer using artificial intelligence deep learning. The model is trained using preoperative imaging and postoperative pathological findings of cervical cancer patients, with the goal of enhancing the accuracy of lymphatic metastasis prediction through preoperative imaging and offering insights for treatment decisions.
This clinical trial evaluates the use of an imaging scan (18F-rhPSMA-7.3 positron emission tomography [PET]/magnetic resonance imaging [MRI]) for identifying patients who are at risk of having their disease spread to the lymph nodes in those undergoing radical prostatectomy for prostate cancer that has not spread to other parts of the body (localized). Prostate specific membrane antigen (PSMA) PET/computed tomography (CT) has emerged as an option to stage newly diagnosed high risk prostate cancer patients. PSMA PET/CT has demonstrated improved diagnostic accuracy for identifying metastasis. PET is procedure in which a small amount of radioactive glucose (sugar) is injected into a vein, and a scanner is used to make detailed, computerized pictures of areas inside the body where the glucose is used. Because cancer cells often use more glucose than normal cells, the pictures can be used to find cancer cells in the body. MRI is procedure in which radio waves and a powerful magnet linked to a computer are used to create detailed pictures of areas inside the body. These pictures can show the difference between normal and diseased tissue. This study may help researchers learn whether 18F-rhPSMA-7.3 PET/ MRI may improve predicting which patients are at risk of lymph node metastases and who are suitable candidates for pelvic lymph node dissection in patients with localized high-risk prostate cancer undergoing radical prostatectomy.
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 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.