The Diagnostic Yield and Complications of Percutaneous Needle Aspiration Biopsy for the Intrathoracic Lesions (경피적 폐생검의 진단성적 및 합병증)
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- Tuberculosis and Respiratory Diseases
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- v.43 no.6
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- pp.916-924
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- 1996
Bacground : Percutaneous needle aspiration biopsy (PCNA) is one of the most frequently used diagnostic methcxJs for intrathoracic lesions. Previous studies have reponed wide range of diagnostic yield from 28 to 98%. However, diagnostic yield has been increased by accumulation of experience, improvement of needle and the image guiding systems. We analysed the results of PCNA performed for one year to evaluate the diagnostic yield, the rate and severity of complications and factors affecting the diagnostic yield. Method : 287 PCNAs undergone in 236 patients from January, 1994 to December, 1994 were analysed retrospectively. The intrathoracic lesions was targeted and aspirated with 21 - 23 G Chiba needle under fluoroscopic guiding system. Occasionally, 19 - 20 G Biopsy gun was used for core tissue specimen. The specimen was requested for microbiologic, cytologic and histopathologic examination in the case of obtained core tissue. Diagnostic yields and complication rate of benign and malignant lesions were ca1culaled based on patients' chans. The comparison for the diagnostic yields according to size and shape of the lesions was analysed with chi square test (p<0.05). Results : There are 19.9% of consolidative lesion and 80.1% of nodular or mass lesion, and the lesion is located at the right upper lobe in 26.3% of cases, the right middle lobe in 6.4%, the right lower lobe 21.2%, the left upper lobe in 16.8%, the left lower lobe in 10.6%, and mediastinum in 1.3%. The lesion distributed over 2 lobes is as many as 17.4% of cases. There are 74 patients with benign lesions, 142 patients with malignant lesions in final diagnosis and confirmative diagnosis was not made in 22 patients despite of all available diagnostic methods. 2 patients have lung cancer and pulmonary tuberculosis concomittantly. Experience with 236 patients showed that PCNA can diagnose benign lesions in 62.2% (42 patients) of patients with such lesions and malignant lesions in 82.4% (117 patients) of patients. For the patients in whom the first PCNA failed to make diagnosis, the procedure was repeated and the cumulative diagnostic yield was increased as 44.6%, 60.8%, 62.2% in benign lesions and as 73.4%, 81.7%, 82.4% in malignant lesions through serial PCNA. Thoracotomy was performed in 9 patients with benign lesions and in 43 patients with malignant lesions. PCNA and thoracotomy showed the same pathologic result in 44.4% (4 patients) of benign lesions and 58.1% (25 patients) of malignant lesions. Thoracotomy confirmed 4 patients with malignat lesions against benign result of PCNA and 2 patients with benign lesions against malignant result of PCNA. There are 1.0% (3 cases) of hemoptysis, 19.2% (55 cases) of blood tinged sputum, 12.5% (36 cases) of pneumothorax and 1.0% (3 cases) of fever through 287 times of PCNA. Hemoptysis and blood tinged sputum didn't need therapy. 8 cases of pneumothorax needed insertion of classical chest tube or pig-tail catheter. Fever subsided within 48 hours in all cases. There was no difference between size and shape of lesion with diagnostic yield. Conclusion: PCNA shows relatively high diagnostic yield and mild degree complications but the accuracy of histologic diagnosis has to be improved.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used