Showing posts with label Sentinel lymph node. Show all posts
Showing posts with label Sentinel lymph node. Show all posts

Thursday, 31 July 2025

Diagnostic Procedure: Mapping Sentinel Lymph Nodes among Patients with Skin Melanoma |Chapter 1 | Disease and Health Research: New Insights Vol. 2

 

Lymph drainage from the skin varies from patient to patient, sometimes even when the tumor is in the same location in two different individuals. The pathway of lymph vessels that collect and drain lymph is unpredictable, as are the locations of SLNs. Therefore, clinical predictions of SLN locations are unreliable and imprecise. Preoperative lymphoscintigraphy with small particle radio colloids allows visualization of all these lymph vessels draining lymph to SLNs. Careful diagnostics will enable the localization of all true SLNs, even when these lymph nodes are sometimes outside the usual lymph basin. The network of lymphatic vessels in the skin has been studied for centuries. In 1984, Sappey published an atlas of lymphatic vessels. The discovery of lymphoscintigraphy around 1950 renewed interest in verifying the pathways of lymphatic vessels in patients with skin melanoma. Lymphoscintigraphy quickly became an indispensable technique for mapping sentinel lymph nodes. Detecting sentinel lymph nodes is essential for conducting adequate diagnostics, appropriately staging patients, and administering necessary therapy. Any lymph node that removes lymph from the tumor site is considered a sentinel lymph node. Because lymphatic arteries can pass through that cluster of lymph nodes and empty into other lymph nodes, it might not even be the closest to the tumor location. Patients with cutaneous melanoma have their sentinel lymph nodes mapped with lymphoscintigraphy. It entails administering intradermal injections of a radiocolloid in the region of the excisional biopsy site or in close proximity to the melanoma site.  The location of all lymphatics is marked with a marker or tattooed with a dot on the skin above the node. Radiocolloid particles used for sentinel identification are in the size range of 5-50 nanometers. These particles easily enter lymphatic capillaries, and their entry is facilitated by exercise or massage of that body part. The lymphatic drainage of the skin varies from patient to patient, sometimes even when the tumor is in the same location in two different individuals. Therefore, careful diagnostics will enable the location of all true sentinel lymph nodes. Careful diagnostics will enable the localization of all true SLNs, even when these lymph nodes are sometimes outside the usual lymph basin. This is crucial for accurate staging of patients with melanoma.

 

Author(s) Details

Marko Vlacic
University Clinical Center of Vojvodina, Clinic for Plastic and Reconstructive Surgery, Novi Sad, Republic of Serbia.

Mirela Eric
Department of Anatomy, University of Novi Sad, Medical Faculty, Novi Sad, Republic of Serbia.

 

Mladen Jovanovic
University Clinical Center of Vojvodina, Clinic for Plastic and Reconstructive Surgery, Novi Sad, Republic of Serbia.

 

Sandra Jelcic
University Clinical Center of Vojvodina, Clinic for Physical Medicine and Rehabilitation, Novi Sad, Republic of Serbia.

 

Ivan Besenji
Institute of Cardiovascular Diseases of Vojvodina, Sremska Kamenica, Republic of Serbia.

 

Ivana Smiljanic
University Clinical Center of Vojvodina, Emergency Centre, Novi Sad, Republic of Serbia.

 

Please see the book here:- https://doi.org/10.9734/bpi/dhrni/v2/973

Saturday, 19 February 2022

Sentinel Lymph Node (SLN) Metastases in Breast Carcinoma Whole Slide Image (WSI) through Densenet Deep Learning Network: An Approach towards Clinical Management and Treatment | Chapter 20 | Issues and Developments in Medicine and Medical Research Vol. 5

 This research proposes a novel sentinel lymph metastasis categorization methodology that will aid pathologists in making quick and accurate diagnoses. DenseNet-161, a CNN-based image classification model for classifying breast lymph node metastases from WSI images, was discussed in this study. Breast cancer has the intention of spreading throughout the body. Locally, cancer cells spread by infecting healthy tissue nearby. It can also spread throughout an area by infecting nearby lymph nodes, tissues, or organs. The CNN model learns the features from the training data at first. Following that, after successfully fitting the training data, it attempts to generalise and generate correct predictions for new data that it has not seen before. A model that overfits the training data is referred to as overfitting. Even after applying the thresholding pre-processing technique, the noise persists, necessitating additional pre-processing before training the model. Furthermore, data-augmentation will considerably improve the accuracy by expanding the dataset size.


Author(S) Details

Rajasekaran Subramanian
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

R. Devika Rubi
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Abhay Krishna Kasavaraju
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Samayk Jain
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Swathi Guptha
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Suraj Raghavendra Pingali
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

View Book:- https://stm.bookpi.org/IDMMR-V5/article/view/5913