Number of lymph nodes harvested from a mediastinal lymphadenectomy: Results of the randomized, prospective ACOSOG Z0030 trial

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 7555-7555
Author(s):  
M. S. Allen ◽  
G. E. Darling ◽  
P. A. Decker ◽  
J. B. Putnam ◽  
R. A. Malthaner ◽  
...  

7555 Background: Lymph node status is a major determinant of stage and survival in patients with lung cancer; however, little information is available about the yield of a mediastinal lymphadenectomy done at the time of pulmonary resection. Methods: The ACOSOG Z0030 trial is a prospective, randomized trial of mediastinal lymph node sampling versus complete mediastinal lymphadenectomy during an operation for early stage lung cancer. Total enrollment from July 1999 to February 2004 was 1,111 patients, of which 1,023 were eligible and/or evaluable. There were 524 patients who underwent complete mediastinal lymph node resection after randomization to this arm that were declared eligible and/or evaluable with lymph node data available. The number of lymph nodes examined from each station was collected beginning in January 2002. Prospectively collected data from these patients was analyzed to determine the number of lymph nodes obtained. Results: Median age was 67 (range 37–87) and 267 (52%) were men. Histology was squamous cell in 141 (27%), adenocarcinoma in 227 (44%), large cell in 22 (4%), bronchoavelolar in 32 (6%) and other non-small cell in 99 (19%). There were 317 right sided cancers and 207 left sided cancers. For lymphadenectomy for cancers in the right lung the yield from station 2R was a median of 2 lymph nodes (range 1 to 15), station 4R was 2 (1 –17), station 7 was 2 (1–24), station 8 was 1 (1–5), station 9 was 1 (1–6) and station 10R was 1 (1–10). For lymphadenectomy for cancers on the left side the yield from station 2L was 2 (1–4), station 4L was 1 (1–12), station 5 was 2 (1–18), station 6 was 2 (1–11), station 7 was 2 (1–16), station 8 was 1 (1–3), station 9 was 1 (1–8) and 10L was 2 (1–12). The total number of lymph nodes or fragments obtained for right sided cancers was a median of 13.5 (range 1 to 56) and for left sided tumors 15 (range 4 to 81). Conclusions: Although high variability exists in the actual number of lymph nodes obtained from various nodal stations, a complete mediastinal lymphadenectomy should obtain one or more lymph nodes from each mediastinal station. Adequate mediastinal lymphadenectomy should include exploration and remove of lymph nodes from stations 2R, 4R, 7, 8, and 9 for right sided cancers and stations 4L, 5, 6, 7, 8 and 9 for left sided cancers. No significant financial relationships to disclose.

2020 ◽  
Author(s):  
Tuan Pham

<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>


Open Medicine ◽  
2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhiguo Wang ◽  
Chunmeng Jiang

AbstractEUS is a useful tool for diagnosis of mediastinal diseases. EUS-FNA plays an important role in staging of lung cancer and in tissue acquisition in patients with mediastinal masses. In this review, the following issues will be addressed: EUS-FNA and EBUS-TBNA, metastatic mediastinal lymph nodes diagnosed by EUS, EUS in assessment of mediastinal lymph node status for staging of lung cancer, mediastinal lymphoma diagnosed by EUS, sarcoidosis and tuberculosis diagnosed by EUS.


2020 ◽  
Author(s):  
Tuan Pham

<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>


2022 ◽  
Vol 270 ◽  
pp. 271-278
Author(s):  
Marcus Taylor ◽  
Matthew Evison ◽  
Bethan Clayton ◽  
Stuart W Grant ◽  
Glen P Martin ◽  
...  

2017 ◽  
Vol 104 (6) ◽  
pp. 1805-1814 ◽  
Author(s):  
Seth B. Krantz ◽  
Waseem Lutfi ◽  
Kristine Kuchta ◽  
Chi-Hsiung Wang ◽  
Ki Wan Kim ◽  
...  

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