• Journal of Chest Surgery
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• v.40 no.11
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• pp.745-751
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• 2007

Background: Mediastinoscopy is generally performed to confirm mediastinal lymph node metastasis in lung cancer patients. It still remains controversial whether mediastinoscopy should be performed in all patients with resectable non-small cell lung cancer (NSCLC). We studied the clinical value of mediastinoscopy in preoperative staging in NSCLC. Material and Method: We retrospectively studied 90 NSCLC patients who underwent radiological evaluation and mediastinoscopy followed by surgical resection from March 2002 to December 2004. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each evaluation method were assessed and compared. Result: Specificity, PPV, NPV, and accuracy of mediastinoscopy were superior to those of radiological evaluation, but there was no significant difference in sensitivity. The sensitivity of mediastinoscopy was 28.6% in 62 patients with radiological N0/1 disease and 72.7% in 28 patients with radiological N2/3 disease. Seven of eight patients in whom positive nodes were not detected by the mediastinoscopy had subcarinal lymph node metastasis. Conclusion: Considering its invasiveness, the difficulty to reach certain node stations, and its low sensitivity in radiological N0/1 disease, mediastinoscopy should be selectively performed in radiological N2/3 disease rather than in all radiological cancer stages.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.25-29
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• 2009

Purpose: Several radioactive agents were used for lymphoscintigraphy in breast cancer. But recently, due to the change of local radiopharmaceutical licensing policy and discontinuation of supplies from manufacturer, we could not use many radiocolloids such as $^{99m}Tc$-Antimony Trisulfide Colloid, $^{99m}Tc$-Tin Colloid, $^{99m}Tc$-Human Serum Albumin. So it is necessary to use the substitution radiopharmaceutical. Therefore, this study aims to evaluate the performance of substitution radiopharmaceutical ($^{99m}Tc$-Phytate) including the existing radiocolloids and to set up of the appropriate protocol in lymphoscintigraphy. Materials and Methods: For each radiocolloids ($^{99m}Tc$-Antimony Trisulfide Colloid (ASC), $^{99m}Tc$-Tin Colloid (TC), $^{99m}Tc$-Human Serum Albumin (HSA), $^{99m}Tc$-Phytate) were performed the particle size by Zeta Sizer (Nano-ZS) and we compared the radiolabeling time, procedure and acquisition time for each of the radiocolloids (total 200 patients). For the last time, we made an analysis of image for each of the radiocolloids with our previous report (SJ Jang et al, Korean Nucl Med Mol imaging Vol.41, No.6, Dec 2007). Results: The particle size of each radiocolloids showed A.S.C (50 nm), T.C (310 nm), H.S.A (10.8 nm), $^{99m}Tc$-Phytate (499 nm). The labeling and acquisition time for each of the radiocolloids showed no substantial difference. But there is difference of the labeling time for the A.S.C. because the ASC procedure need to boiling process. There were no significant differences among those radiocolloids (p>0.005) in the identification rate (IR), false negative rate (FNR), and negative predictive value (NPV). Conclusions: $^{99m}Tc$-labeled radiocolloids showed equivalent results in lymphoscintigraphy. Therefore, in this exam of each radiocolloids could be applied appropriate protocol in lymphoscintigraphy.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.