Computed Tomography and Mediastinoscopy in the Assessment of Resectability of Lung Cancer

1989 ◽  
Vol 30 (2) ◽  
pp. 169-173 ◽  
Author(s):  
S. Lähde ◽  
K. Hyrynkangas ◽  
J. Merikanto ◽  
R. Pokela ◽  
K. Jokinen ◽  
...  

In order to assess the potential of computed tomography (CT) of the mediastinum and mediastinoscopy in the staging of lung cancer, 125 patients were examined. Of these, 104 underwent thoracotomy, at which there was no evidence of mediastinal tumour involvement in 79 while 25 patients had signs of tumour spread. The sensitivity and specificity of CT were 87.0 per cent and 95.8 per cent, respectively, in the detection of direct tumour extension with a mediastinal mass. When lymph node enlargement was the sole finding, CT did not provide any differentiation between benign and malignant lymphadenopathy. The mediastinal involvement was inaccessible on mediastinoscopy in 18 cases (72%). Despite the surperior sensitivity of CT it was often difficult to determine whether direct tumour infiltratin of mediastinal structures had occurred. It was concluded that CT is necessary for screening the entire mediastinum and, when it reveals no evidence of mediastinal tumour spread, mediastinoscopy will yield no further information. Mediastinoscopy will help to correctly identify accessible mediastinal lymph node involvement of the superior mediastinum and to define the mediastinal tumour invasion in doubtful cases.

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>


CHEST Journal ◽  
2003 ◽  
Vol 123 (2) ◽  
pp. 442-451 ◽  
Author(s):  
Annette Fritscher-Ravens ◽  
Karl H. Bohuslavizki ◽  
Lars Brandt ◽  
Christoph Bobrowski ◽  
Christian Lund ◽  
...  

2020 ◽  
Vol 66 (9) ◽  
pp. 1210-1216
Author(s):  
Augusto Carbonari ◽  
Lucio Rossini ◽  
Fabio Marioni ◽  
Marco Camunha ◽  
Mauro Saieg ◽  
...  

SUMMARY OBJECTIVE: To evaluate the value of EBUS-TBNA in the diagnosis of lung and mediastinal lesions. METHODS: Prospective cohort study that included 52 patients during a 2-year period (2016 to 2018) who underwent EBUS-TBNA. RESULTS: Among the 52 individuals submitted to the procedure, 22 (42.31%) patients were diagnosed with locally advanced lung cancer (N2 or N3 lymph node involvement). EBUS-TBNA confirmed the diagnosis of metastases from other extrathoracic tumors in the mediastinum or lung in 5 patients (9.61%), confirmed small cell lung cancer in 3 patients (5.76%), mediastinal sarcoidosis in 1 patient (1.92%), and reactive mediastinal lymph node in 8 patients (15.38%); insufficient results were found for 3 patients (5.76%). Based on these results, EBUS-TBNA avoided further subsequent surgical procedures in 39 of 52 patients (75%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 86%, 100%, 100%, 77%, and 90%, respectively. No major complications were observed. CONCLUSIONS: EBUS-TBNA is a safe, effective, and valuable method. This technique can significantly reduce the rate of subsequent surgical procedures required for the diagnosis of lung and mediastinal lesions.


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