scholarly journals Prognostic Significance of Lymph Node Examination by the OSNA Method in Lung Cancer Patients—Comparison with the Standard Histopathological Procedure

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2611
Author(s):  
Josef Vodicka ◽  
Martin Pesta ◽  
Vlastimil Kulda ◽  
Katerina Houfkova ◽  
Bohuslava Vankova ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Prognostic Significance ◽  
Nucleic Acid Amplification ◽  
Lymph Node Status ◽  
Cytokeratin 19 ◽  
Lung Cancer Patients ◽  
Node Status ◽  
Osna Method

The aim of the study was to compare the prognostic significance of lymph node status of patients with lung cancer analyzed by three different methods: hematoxylin and eosin (H&E), immunohistochemistry of cytokeratin 19 (IHC CK19), and One-Step Nucleic Acid Amplification (OSNA). The clinical relevance of the results was evaluated based on relation to prognosis; the disease-free interval (DFI) and overall survival (OS) were analyzed. During radical surgical treatment, a total of 1426 lymph nodes were obtained from 100 patients, creating 472 groups of nodes (4–5 groups per patient) and examined by H&E, IHC CK19 and OSNA. The median follow-up was 44 months. Concordant results on the lymph node status of the H&E, IHC CK19 and OSNA examinations were reported in 78% of patients. We recorded shorter OS in patients with positive results provided by both OSNA and H&E. The study demonstrated a higher percentage of detected micrometastases in lymph nodes by the OSNA method. However, the higher sensitivity of the OSNA, with the cut-off value 250 copies of mRNA of CK19/µL, resulted in a lower association of OSNA positivity with progress of the disease compared to H&E. Increasing the cut-off to 615 copies resulted in an increase in concordance between the OSNA and H&E, which means that the higher cut-off is more relevant in the case of lung tumors.

scholarly journals Total Tumor Load of mRNA Cytokeratin 19 in the Sentinel Lymph Node as a Predictive Value of Axillary Lymphadenectomy in Patients with Neoadjuvant Breast Cancer

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Karla B. Peña ◽  
Amillano Kepa ◽  
Alba Cochs ◽  
Francesc Riu ◽  
David Parada ◽  
...  
Keyword(s):  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Nodes ◽  
Nucleic Acid Amplification ◽  
Cell Group ◽  
Lymph Node Status ◽  
Cytokeratin 19 ◽  
Axillary Lymph ◽  
Axillary Lymphadenectomy ◽  
Tumor Load

Although sentinel lymph node biopsy (SLNB) has proved to be able to diagnose axillary lymph node status safely and reliably, there is still not enough evidence to suggest that it can be used in patients who have undergone neoadjuvant chemotherapy (NAC) for lymph node-sparing surgery. The present study used molecular approaches to determine whether SLNB can be reliably used in patients who have been treated with NAC before SLN surgery, and whether the total tumor load of the SLN can be used as a predictive factor in axillary lymphadenectomy (ALD). We used one-step nucleic acid amplification (OSNA) to analyze a total of 111 consecutive patients who presented operable invasive breast carcinomas and who had been treated with NAC. SLN was positive in 55 patients and the identification rate was 100%. In 9 of these 55 patients, ALD showed that other lymph nodes were also involved. In all of the other 46 patients, the only lymph node to be identified as positive was SLN. Metastasis was not found in any of the axillary lymph nodes in the isolated tumor cell group. The total tumor load, defined as the amount of cytokeratin 19 mRNA copy numbers in all positives SLN (copies/µL), showed three risk groups related to the possibility of positive non-sentinel nodes. OSNA is a diagnostic technique that is highly sensitive, specific, and reproducible and it can be used to analyze sentinel lymph nodes after NAC. Total tumor load may be able to help predict additional metastases in axillary lymphadenectomy.

scholarly journals From Raw Pixels to Recurrence Image for Deep Learning of Benign and Malignant Mediastinal Lymph Nodes on Computed Tomography

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Mediastinal Lymph Node ◽  
Survival Rates ◽  
Primary Lung Cancer ◽  
Lymph Node Status ◽  
Mediastinal Lymph Nodes

<div>Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudo-labeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging. </div><div><br></div>

Clinicopathologic Significance of False-Positive Lymph Node Status on FDG-PET in Lung Cancer

2020 ◽  
Author(s):  
Hideki Endoh ◽  
Ryohei Yamamoto ◽  
Akihiro Ichikawa ◽  
Satoshi Shiozawa ◽  
Nobuhiro Nishizawa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
False Positive ◽  
Fdg Pet ◽  
Positive Lymph Node ◽  
Lymph Node Status ◽  
Node Status ◽  
Positive Lymph Node Status
Sign in / Sign up

Export Citation Format

Share Document