View clinical trials related to Lymphatic Metastasis.
Filter by: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.
Aim of the study will be to investigate if Endoscopic Ultrasound (EUS) with elastography can be purposed between the routine staging examinations in patients with pancreatic adenocarcinoma without distant metastasis for the staging of lymph nodes status ("N" in TNM classification) - in RESECTABLE pancreatic cancer the investigators will evaluate the concordance with EUS elastography and histological findings of lymph nodes obtained during surgery, in order to assess the sensibility, specificity and the positive and negative predictive value of EUS with elastography, the disease-free survival, the percentage of metastatic patients and the overall survival (in patients with or without metastatic lymph nodes). - in "BORDERLINE resectable" and UNRESECTABLE non-metastatic ("advanced" locally") disease, the investigators will evaluate if the malignant lymph nodes samples during EUS with elastography and fine needle aspiration (FNA) will be related to a decreased survival. Secondary aim will be to register the prognosis (in terms of survival) of the patients with para-aortic and mediastinal pathological lymph nodes (related to a decreases survival in some series in literature)
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 aim of the feasibility study is to evaluate whether SPIO-MRI and a magnetometer could be a potential substitute to the routine dual technique in pre-and intraoperative SLN localization. Secondary, to explore if SPIO-MRI could predict lymph node status in comparison to histopathological analysis.
This study is an open label, prospective, experimental, randomised clinical trial. The primary aim of this study is to determine whether it is feasible to randomise vulvar cancer patients into one of two treatment arms:1) surgical groin node dissection (as delivered though either a sentinel node biopsy or inguinofemoral lymph node dissection (IFL), or 2) serial high-resolution bilateral groin ultrasound surveillance and clinical examination every 2 months.
Existing models do poorly when it comes to quantifying the risk of Lymph node metastases (LNM). This study generated elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these for LNM in patients with T1 esophageal squamous cell carcinoma.
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.
Lung cancer patients undergoing upfront surgery, highly benefit from a systematic lymph node dissection in the mediastinum and in the surgical specimens. The latter is performed by the pathologist. Developing a standardized technique to dissect the lobectomy specimen has the potential of maximizing the retrieval of all N1 stations lymph nodes. The investigators believe that the adoption of such technique will improve lung cancer staging and identify a higher number of patients that qualify for adjuvant therapies.