Lymph Node Metastasis

Brief Summary Lymph node metastasis (LNM) is a key factor influencing treatment decisions and prognosis in patients with gastric cancer. Lymphatic invasion (LI) is an important pathological predictor of LNM and a core component of the eCURA risk scoring system after endoscopic submucosal dissection (ESD) for early gastric cancer. However, whether LI has the same predictive value for LNM across different mismatch repair (MMR) statuses remains unclear. Compared with proficient mismatch repair (pMMR) gastric cancer, deficient mismatch repair (dMMR) gastric cancer has distinct molecular pathological features and an immune-enriched tumor microenvironment. In early gastric cancer, if LI is associated with a lower LNM risk in dMMR tumors than in pMMR tumors, existing LI-based eCURA risk assessment may overestimate LNM risk in patients with dMMR early gastric cancer and consequently affect decisions regarding additional surgery after ESD. Therefore, this study aims to systematically evaluate the impact of MMR status on the association between LI and LNM using upfront-surgery and post-ESD additional-surgery cohorts from our center, and to explore the potential clinical value of MMR status in refining eCURA-based risk stratification for early gastric cancer.

This single-arm, prospective feasibility study evaluates an Extended Reality (XR) headset-based preoperative surgical planning workflow that fuses 18F-FDG PET metabolic hotspots with CT anatomy on the OpVerse platform, in patients with non-small cell lung cancer (NSCLC) and supraclavicular or cervical lymph node metastasis (N3 disease) requiring lymph node dissection. Ten participants will undergo standard preoperative contrast-enhanced CT and whole-body PET. Synapse 3D software is used to segment key anatomic structures (clavicle, sternocleidomastoid, internal jugular vein, subclavian vessels, brachial plexus) and to project PET SUV hotspots onto the high-resolution CT model, yielding a patient-specific digital twin of functional tumor boundaries and at-risk neurovascular structures. Immediately prior to skin incision, the operating surgeon dons an XR head-mounted display (HoloLens via OpVerse) and registers the digital twin to the patient's neck using stable bony landmarks (clavicular head, sternal notch, mastoid). The surgeon plans the optimal incision and initial dissection trajectory, avoiding superficial veins and projecting the location of deep PET-positive nodes. The XR device is then removed, and the planned cervical or supraclavicular lymph node dissection is performed using standard surgical technique without further intraoperative XR guidance. The primary endpoint is a composite of safety and feasibility: absence of Grade ≥2 (Clavien-Dindo) phrenic nerve, brachial plexus, chyle leak, Horner syndrome, or major vascular injury through 30 days postoperatively, together with successful XR registration and incision planning. Secondary endpoints include incision planning accuracy, PET hotspot clearance rate, target registration error, operative time, estimated blood loss, and lymph node yield.

This study is a phase III, open label, multicentre, three-group, randomised clinical trial. The primary aim of this study is to determine whether intensive groin ultrasound monitoring (1) is effective and safe to replace invasive groin lymph node dissection (LND) to manage vulvar cancer, (2) decreases the morbidity associated with vulvar cancer surgery, and (3) is cost effective.

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.

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 for 12 months.

The purpose of this study is to determine how circulating tumor DNA (ctDNA), a sign of minimal residual disease (MRD), is detectable after surgery in patients with early HR+/HER2- breast cancer that has spread to 1-3 lymph nodes. Researchers aim to understand if ctDNA detection can identify patients at higher risk of recurrence and guide better treatment decisions. A key aspect is the inclusion of a dedicated cohort of African American/Black women, a group underrepresented in molecular residual disease (MRD) research despite experiencing more aggressive breast cancers. This study will correlate ctDNA results with treatment patterns (radiotherapy, systemic therapy) and outcomes (recurrence-free and overall survival) in both non-African American and African American participants.

This is a single-arm, open-label, multicenter, exploratory clinical trial designed to systematically evaluate the efficacy, safety, and patient benefit of ultrasound-guided irreversible electroporation (IRE) ablation for recurrent or metastatic cervical lymph node disease in patients with thyroid cancer after prior curative thyroid surgery and neck dissection. A total of 85 participants will be enrolled. After providing written informed consent, participants will enter a screening period of up to 28 days. During screening, baseline imaging of target lymph nodes will be performed (contrast-enhanced ultrasound or computed tomography), and patient-reported and clinician-reported assessments will be completed, including quality of life, pain, neck appearance, and, when applicable, voice-related outcomes. Laboratory testing and immunology samples will also be collected. Eligible participants will undergo the first IRE ablation on Day 0 under ultrasound guidance. Acute pain will be assessed using the Numeric Rating Scale (NRS) at 0, 4, 8, 24, 48, and 72 hours after the procedure, and all adverse events and device deficiencies will be recorded. The first imaging re-assessment will be performed at Day 30 (±7 days). If residual enhancement suggests incomplete ablation, one salvage IRE ablation may be performed within 14 days. After confirmation of no need for salvage ablation or after completion of salvage ablation, participants will enter follow-up. Follow-up visits will occur every 3 months starting from Month 3 after the first (or salvage) ablation and will continue until 24 months or until imaging progression, withdrawal, death, or loss to follow-up, whichever occurs first. Imaging assessments will be performed at each follow-up visit. At 12 months, the volume reduction rate (VRR), complete disappearance rate, and recurrence rate of treated lymph nodes will be assessed. Patient-reported outcomes (ThyPRO-39, EQ-5D-5L, neck appearance satisfaction visual analog scale) and clinician-reported scar assessment (Vancouver Scar Scale) will be repeated at Months 1, 3, 6, and 12, with the Voice Handicap Index-10 collected as needed. Laboratory tests (blood count, biochemistry, electrolytes) and immunology samples will be collected every 3 months. The primary efficacy endpoint is the lymph node volume reduction rate at 12 months after a single IRE ablation. Secondary efficacy endpoints include 12-month lymph node volume reduction rate after single and/or salvage ablation, complete disappearance rate and recurrence rate at 12 months, volume reduction rate at 12 months for lesions located in high-risk anatomical areas, progression-free survival and overall survival, and improvements in quality of life and cosmetic outcomes. Safety endpoints include the incidence of adverse events and serious adverse events graded by NCI CTCAE v5.0, device-related serious adverse events, acute pain tolerability (NRS area under the curve and the proportion of participants with NRS ≥4 of sustained duration), laboratory abnormalities, and changes in voice-related outcomes. Exploratory endpoints include longitudinal changes in immune cell subsets, immune checkpoint and inhibitory molecule expression, and serum cytokine/chemokine profiles. Adverse events will be followed from the last IRE procedure (including salvage ablation) through 12 months.

The project aims to evaluate the value of the new LN-RADS scales for lymph node classification in CT and MR and to compare this method with two other methods RECIST 1.1 and Node-RADS. The main tested system in the study is LN-RADS, the comparators are RECIST 1.1 and Node-RADS criteria. Lymph nodes are a key diagnostic and therapeutic element in oncology. Despite the technological progress, the detection of neoplastic changes in the lymph nodes is of low effectiveness, which results from the imperfection of the criteria used. Currently, the most widely used criterion is the RECIST 1.1 guideline developed in the 1990s, according to which the lymph node dimension in the short axis with a cut-off point of 10 mm is decisive. Lymph nodes smaller than 10 mm across are considered normal. It is a criterion with a high error rate, both due to the false-negative diagnoses (with small metastases below 10 mm) and false-positive diagnoses (in the case of inflammatory lymphadenopathy). A particular disadvantageous situation is when the metastatic nodes and their transverse dimension is less than 10 mm, because they are treated as healthy nodes and the degree of the disease advancement is underestimated. As a result, the patient is not treated properly - no complete lymphadenectomy, no radiotherapy to the area of these nodes or insufficient systemic treatment. In all cases, underestimating the stage of the neoplastic diseases increases the risk of the recurrence. LN-RADS accounts small metastases in nodes about 3 mm in size, thus about 20% more metastatic nodes may be detected compared to RECIST 1.1 method. This means that currently, according to RECIST 1.1 rules, approx. 20% of patients have missed nodal metastases and consequently receive insufficient treatment resulting in relapse. Previous studies have shown that RECIST 1.1 shows a high level of underestimation of metastatic nodes. The Node-RADS system, as the second comparator next to RECIT 1.1, is a fairly new system moving towards the structural assessment of lymph nodes, but proposed arbitrarily, without hard evidence for its effectiveness. Despite the publication of the Node-RADS system in a medical journal, it is not validated. The Node-RADS has numerous limitations and weaknesses that reduce its value.

This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.

The assessment of lymph node status is of crucial importance in gynaecological malignancies. Indeed, the prognosis and adjuvant treatment regimens are strongly influenced by the presence of nodal involvement. Systematic extensive lymphadenectomies are often performed for staging, diagnosis of skip metastases and to define the radiation field when radiotherapy treatments are required. Nevertheless, these can lead to significant short-term and long-term lymphatic complications, which are difficult to justify if the lymph nodes are free from metastasis. To avoid unnecessary comprehensive procedures in early-stage cancers, evaluation of the sentinel lymph node has acquired a valuable role even if limitations are still present (rate of frozen section false negative, "empty packets" and mapping failure). The introduction of an intra-operative non-invasive imaging technique capable of describing the presence and characteristics of lymph nodes could help in (1) eliminating the risk of empty packet, (2) orienting the intraoperative decision while avoiding the drawbacks of frozen section (time and resources, partial destruction of the tissue material), (3) orientate the pathological section if frozen section is used. To date, technological advancements have paved the way for enhanced intra-operative assessment of cancerous organs and lesions. Over the past decade, the evolution of robotic surgery combined with advancements in image-guided surgery techniques has led to the introduction of ultrasound probes designed specifically for intraoperative ultrasound during robotic surgery (RIOUS). Apart from the conventional rigid laparoscopic probes, which can be inserted through an accessory trocar, there are robotic probes tailored to fit device arms, and drop-in flexible probes that are becoming increasingly relevant in the scientific panorama. Notably, the latter drop-in probes feature a rigid segment designed for compatibility with robotic graspers, leveraging the dexterity and rotational manoeuvrability inherent to robotic surgery. Such probes, already proven effective in evaluating liver and kidney lesions as well as tumour margins, hold promise for intraoperative lymph node assessments due to the possibility of reaching difficult anatomical spaces thanks to the robotic-assisted movements

This study evaluates the safety and effectiveness of MPB-2043, a superparamagnetic iron oxide (SPIO) contrast agent, for enhancing MRI in detecting metastatic lymph nodes in head and neck cancer. The study compares four doses of MPB-2043 (0.5 mg/kg, 1 mg/kg, 2 mg/kg, and 3 mg/kg) and assesses the optimal timing for post-dose imaging using T1/T2/T2\*-weighted sequences to improve the accuracy of nodal staging.

(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it. It includes: 1.Automatically identifying the lymph nodes and the primary tumor. 2.Analyzing MRI images of the lymph node and surrounding area. 3.Using MRI scans before and after chemotherapy to track changes in the lymph node. (II) AI Model for Predicting Lymph Node Metastasis We created an AI model that predicts whether a lymph node in a specific area has cancer. This model uses a combination of the primary tumor's pathology and MRI images of both the tumor and lymph node. It also tracks changes in the lymph node over time. The model includes: 1.Analyzing the tumor's pathology to identify specific lymphatic structures. 2.Using MRI scans to predict the likelihood of metastasis in a single lymph node. 3.Examining MRI scans before and after chemotherapy to help determine if the lymph node has metastasized. (III) Verifying and Analyzing the Benefits of the AI Model We are testing the AI model to see how well it works and its potential benefits, including: 1.Checking if the AI can correct past diagnoses of recurrent lymph nodes in nasopharyngeal cancer, which could help guide treatment plans for radiotherapy. 2.Testing the model using biopsy results from head and neck cancer patients to see if it can accurately detect negative lymph nodes. 3.Running clinical trials to test the AI model's safety and effectiveness in guiding radiation treatment for upper neck and single lymph node areas in nasopharyngeal cancer. 4.Analyzing the economic benefits of using the AI model in radiation treatment for nasopharyngeal cancer.

The present study is designed to evaluate the role of simultaneous multi-modal sonographic tools-Ultrasound (US), Color Doppler Ultrasound (CDU), and Ultrasound Elastography (UE)-in determining the nature (benign or malignant) of axillary lymph nodes (ALNs) in patients with primary breast cancer. It aims to compare the diagnostic indices of each modality individually against their combined performance. Specifically, the study will assess whether adding UE to conventional gray-scale US and CDU improves diagnostic accuracy.

To evaluate the clinical outcomes of 2-year lymph node metastasis rate, disease-free survival, overall survival, and health-related quality of life in the patients with cT1N0M0 oral squamous cell carcinoma, who receive primary lesion resection combined with elective neck dissection or primary lesion resection only.

The goal of this observational study is to develop an artificial intelligence model to transform unstained lymph node tissue slice images directly into stained images. The main questions it aims to answer are: Can the virtual staining model generate hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) images suitable for clinical diagnosis from unstained paraffin-embedded lymph node slice images, including those from breast axillary lymph nodes and other tumor lymph nodes? Can the virtual staining model generate H\&E and IHC images suitable for clinical diagnosis from unstained frozen sentinel lymph node slice images from breast cancer patients? Researchers will retrospectively collect paraffin-embedded lymph node slices from tumor patients and prospectively collect frozen sentinel lymph node slices from breast cancer patients.

The goal of this observational study is to develop an artificial intelligence model based on pathology and magnetic resonance imaging (MRI) images to predict the metastatic status of non-sentinel lymph nodes in patients with breast cancer sentinel lymph node metastasis. The main questions it aims to answer are: Can an artificial intelligence model based on MRI images of breast cancer patients predict the non-sentinel lymph node metastatic status in patients with breast cancer sentinel lymph node metastasis? Can an artificial intelligence model based on intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastasis status in patients with sentinel lymph node metastasis from breast cancer? Can artificial intelligence models based on preoperative MRI and intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastatic status in patients with sentinel lymph node metastasis from breast cancer? Researchers will retrospectively and prospectively collect preoperative MRI and intraoperative sentinel lymph node section images from breast cancer patients.

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.

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

Based on the evidence summarized in the introduction, the clinician hypothesize that the detection of the presence and expression of HPV-DNA, certain miRNAs, and a certain mutational profile in the tissues and biological fluids of these patients, may have important prognostic and diagnostic value not only in HPV-related OPSCCs but also in HPV+ occult T. Accordingly, this study aims to aim to better characterize their potential as biomarkers and to detect the possibility of their their use to implement the sensitivity and specificity of radiological methodologies (PET-CT and MRI), already in use in clinical practice, for monitoring disease progression in this specific subgroup. Finally, by using the collected material to generate organoids and Patient Derived Xenograft (PDX), the study also aims to identify possible new molecular drugs, which could solve the problem of resistance to radiochemotherapy.

Lymph node metastases are a strong prognostic predictor for pancreatic cancer. Para-aortic lymph nodes (PALN) are the final nodes for periampullary cancers before the cancer cells enter the systemic lymphatic circulation. Some consider these nodes to be regional lymph nodes and dissect them as a part of a routine lymphadenectomy for pancreatic cancer. Others argue that metastases to these nodes represent systemic disease and recommend that radical surgery including extended lymphadenectomy should be abandoned. The aim of this study is to define the incidence and clinical consequences of PALN metastasis in patients submitted to a tentative curative resection for carcinoma of the head of the pancreas by systematically resecting paraaortic lymph nodes. Primary outcome 1\) To determine incidence of PALN metastasis in patients submitted to a tentative curative resection Secondary outcomes 1. To determine prognosis of patients with PALN metastasis after a curative resection 2. To determine incidence of metastasis in reginal lymph nodes in patients submitted to a tentative curative resection. 3. To determine prognosis of patients with metastasis in regional lymph nodes in patients submitted to a tentative curative resection. 4. To address the question of how to optimize the frozen section analyses of PALN as related to the final pathology report. 300 patients are planned to be included in the trial.

Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.

This is a clinical trial from Eastern Cooperative Thoracic Oncology Project (ECTOP), numbered as ECTOP-1018. The goal of this clinical trial is to confirm the theraputic effect of selective lymph node dissection for cT1N0M0 invasive non-small cell lung cancer with CTR\>0.5 located in the apical segment. The main questions it aims to answer are: The 5-year overall survival of patients having cT1N0M0 invasive non-small cell lung cancer with CTR\>0.5 located in the apical segment; The post-operative lymph node metastasis and recurrence-free survival. Participants will receive selective lymph node dissection as the surgical procedure.

Background: Lung cancer is the leading cause of cancer related death worldwide. More than 80% of all lung tumors are Non-Small Cell Lung Cancers (NSCLC). Lymph node staging has a prognostic value and is crucial to establish the optimal treatment strategy in individual patients. It remains unknown whether dissecting the intrapulmonary lymph nodes (stations 13 and 14) is necessary for accurate staging and prognostication. Although suggested by several guidelines, these peripheral lymph nodes are not routinely examined in clinical routine for several reasons. Moreover, the prognostic significance of the visceral pleural invasion is controversial. Some studies showed a negative impact on OS and DFS in patients with histologic proved visceral pleura invasion. The mechanism to explain this negative effect is not fully understood. Given that the visceral pleura is very rich in lymphatic vessels, with an intercommunicating "network" arranged over the lung surface and penetrating into the lung parenchyma to join the bronchial lymph vessels with drainage to the various hilar nodes, we assume that the worse OS and DFS observed in these patients could be explained with the presence of metastatic lymph nodes (Station 13-14) that are not routinely examined. Methods: This is a prospective, multicenter study based on ad-hoc created prospectively database. The incidence of N1 lymph node metastasis overall and the incidence of metastasis to the different lymph node stations (Hilar 10/11, Lobar 12, Sublobar 13/14) will be calculated by dividing the number of the respective events by the patient years separately. To investigate the association between visceral pleural invasion and the presence of metastatic lymph nodes univariate and multivariate logistic regression models will be fitted to the data. Discussion: The primary outcome is to investigate the incidence of N1 metastases (especially stations 12,13,14) and his relationship with visceral pleural invasion. The secondary outcomes is to evaluate the impact of N1 metastases and/or visceral pleural invasion on long-term outcomes (OS and DFS) along with incidence and pattern of recurrence. DFS is defined as the time of surgical intervention to tumor recurrence or death, and OS is defined as the time of surgical intervention to death