• Nuclear Medicine and Molecular Imaging
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• v.41 no.1
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• pp.30-41
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• 2007

Purpose: Bone metastasis in breast cancer patients are usually assessed by conventional Tc-99m methylene diphosphonate whole-body bone scan, which has a high sensitivity but a poor specificity. However, positron emission tomography with $^{18}F-2-deoxyglucose$ (FDG-PET) can offer superior spatial resolution and improved specificity. FDG-PET/CT can offer more information to assess bone metastasis than PET alone, by giving a anatomical information of non-enhanced CT image. We attempted to evaluate the usefulness of FDG-PET/CT for detecting bone metastasis in breast cancer and to compare FDG-PET/CT results with bone scan findings. Materials and Methods: The study group comprised 157 women patients (range: $28{\sim}78$ years old, $mean{\pm}SD=49.5{\pm}8.5$) with biopsy-proven breast cancer who underwent bone scan and FDG-PET/CT within 1 week interval. The final diagnosis of bone metastasis was established by histopathological findings, radiological correlation, or clinical follow-up. Bone scan was acquired over 4 hours after administration of 740 MBq Tc-99m MDP. Bone scan image was interpreted as normal, low, intermediate or high probability for osseous metastasis. FDG PET/CT was performed after 6 hours fasting. 370 MBq F-18 FDG was administered intravenously 1 hour before imaging. PET data was obtained by 3D mode and CT data, used as transmission correction database, was acquired during shallow respiration. PET images were evaluated by visual interpretation, and quantification of FDG accumulation in bone lesion was performed by maximal SUV(SUVmax) and relative SUV(SUVrel). Results: Six patients(4.4%) showed metastatic bone lesions. Four(66.6%) of 6 patients with osseous metastasis was detected by bone scan and all 6 patients(100%) were detected by PET/CT. A total of 135 bone lesions found on either FDG-PET or bone scan were consist of 108 osseous metastatic lesion and 27 benign bone lesions. Osseous metastatic lesion had higher SUVmax and SUVrel compared to benign bone lesion($4.79{\pm}3.32$ vs $1.45{\pm}0.44$, p=0.000, $3.08{\pm}2.85$ vs $0.30{\pm}0.43$, p=0.000). Among 108 osseous metastatic lesions, 76 lesions showed as abnormal uptake on bone scan, and 76 lesions also showed as increased FDG uptake on PET/CT scan. There was good agreement between FDG uptake and abnormal bone scan finding (Kendall tau-b : 0.689, p=0.000). Lesion showed increased bone tracer uptake had higher SUVmax and SUVrel compared to lesion showed no abnormal bone scan finding ($6.03{\pm}3.12$ vs $1.09{\pm}1.49$, p=0.000, $4.76{\pm}3.31$ vs $1.29{\pm}0.92$, p=0.000). The order of frequency of osseous metastatic site was vertebra, pelvis, rib, skull, sternum, scapula, femur, clavicle, and humerus. Metastatic lesion on skull had highest SUVmax and metastatic lesion on rib had highest SUVrel. Osteosclerotic metastatic lesion had lowest SUVmax and SUVrel. Conclusion: These results suggest that FDG-PET/CT is more sensitive to detect breast cancer patients with osseous metastasis. CT scan must be reviewed cautiously skeleton with bone window, because osteosclerotic metastatic lesion did not showed abnormal FDG accumulation frequently.

• Journal of Korean Society of Forest Science
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• v.29 no.1
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• pp.20-53
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• 1976

For the purpose of detecting the capability of the trees to resist air pollution and of determining the tree species best suited for purification of polluted air, particularly with regard to $SO_2$ contamination, six following ornamental tree species were selected as experimental materials: i.e., Hibiscus syriacus L., Ginkgo biloba L., Forsythia koreana Nak., Syringa dilatata Nak., Larix leptolepis Gordon, and Pinus rigida Miller. The susceptiblities of the trees were observed and analyzed on the basis of area ratio of smoke injury spots to the total leaf area. The results of the experiments are as follows: I. The Susceptibilities to Sulfur Dioxide. (1) The decreasing order of tolerance to $SO_2$ by species was as follows: 1. Hibiscus syriacus 2. Ginkgo biloba, 3. Forsythia koreana, 4. Syringa dilatata, 5. Larix leptolepis, and 6. Pinus rigida. In general, Hibiscus syriacus and Ginkgo biloba can be grouped as the most resistant and Larix leptolepis and Pinus rigida as the least resistant and Forsythia koreana and Syringa dilatata as of intermediate resistance. (2) The sulfur content of the leaves treated by $SO_2$ increased in proportion to the increase of the concentration of the fumigation. The content in the coniferous species proved to be less than that of the broad-leaved species, but Ginkgo biloba proved to contain as much sulfur as broad-leaved species. (3) The earlier-stage leaves fumigated in June with the $SO_2$ concentration up-to-l-ppm showed that sulfur content increases in proportion to the increase of the concentration of the fumigation, but the difference between concentration was not so significant. (4) The later-stage leaves fumigated in October showed higher sulfur content than the earlier stage leaves, and a wider range of difference in sulfur content was detected among different concentrations. The limit of fumigation resulting in culmination of sulfur absoption in broad-leaved species, such as Syringa dilatata, Hibiscus syriacus, and Forsythia koreana proved to be around 0.6 ppm. (5) Due to the sprouting ability and the adventitious bud formation, the recovery from $SO_2$ fumigation was prominent in Hibiscus syriacus, Syringa dilatata, and Forsythia koreana. (6) The differences in the smoke spot color were recognized by species: namely, dirt-brown in Syringa dilatata, brilliant yellowish-brown in Pinus rigida and Ginkgo biloba, whitish-yellow in Hibiscus syriacus and reddish-brown in Forsythia koreana. (7) The leaf margins proved to be most susceptible, and the leaf bases of the mid-rib most tolerant. In both Ginkgo biloba and Larix leptolepis, the younger leaves were more resistant to $SO_2$ than the older ones. II. The ulfur Content of the Leaves of the Ornamental Trees Growing in the City of Seoul. (1) The sulfur contents in the leaves of the Seoul City ornamental trees showed a remarkably higher value than those of the leaves in the non-polluted areas. The sulfur content of the leaves in the non-polluted area proved to be in the following descending order: Salix pseudo-lasiogyne Leveille, Ginkgo biloba L., Alianthus altissima swingle, Platanus orientalis L., and Populus deltoides Marsh. (2) In respect to the sulfur contents in the leaves of the ornamental trees in the city of Seoul, the air pollution proved to be the worst in the areas of Seoul Railroad Station, the Ahyun Pass, and the Entrance to Ewha Womans University. The areas of Deogsu Palace, Gyeongbog Palace, Changdeog Palace, Changgyeong Park and the Hyehwa Intersection were least polluted, and the areas of the East Gate, the Ulchi Intersection and the Seodaemun Intersection are in the intermediate state.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.