• Korean Journal of Radiology
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• v.25 no.9
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• pp.788-797
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• 2024

Objective: To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer. Materials and Methods: We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS. Results: We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685-9.051, P = 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644-8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and ultifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707-0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5-61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200-8.352, P = 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671-11.428, P = 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166-11.818, P < 0.001) were independently associated with DFS. Conclusion: Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.5
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• pp.45-58
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• 2020

This study was conducted to comprehend the spatial distribution characteristics, habitats and appearances of Hydropotes inermis by using the biotope mapping in Daebudo Island, Ansan-si. The result is base data to understand status and manage potential inhabitation of Hydropotes inermis in Daebudo Island through the Maximum Entropy model. The study used 105 traces from the primary investigation and 452 traces in the secondary investigation. The biotope types were distinquished Hydropotes inermis habitats largest from the order of natural forest (15.1%), natural coast (13.7%), marshy cultivated land (12.6%), and dry cultivated land (11.7%), and from the inhabitation trace results. Hydropotes inermis appearanced biotope types were the greatest in the order of cultivated land (49.73%) > forest (18.85%) > coast (7.00%) > grassland (6.28%). Since forests in Daebudo Island have low slope and altitude, it was concluded that Hydropotes inermis would live in most of the forests. A high number of Hydropotes inermis was found to appear in areas where the grassland is formed including cultivated lands (include unused paddies and fields) and marshy grasslands, which would result in direct damage of crops. According to the Maxent modeling analysis that used location information of Hydropotes inermis, the AUC value was 0.635 based on the ROC curve. In Daebudo Island, areas with over 0.635 potential inhabitation value are distributed all over the place, and it was concluded that each population would have a different scope of influence and home range. Hydropotes inermis living in Daebudo Island have high habitat suitability mainly around the cultivated lands near the roads, but due to the bare lands and roads, it is expected that their habitats would be fragmented and damaged, which would have a direct and indirect effect in maintaining the Hydropotes inermis population. Also, considering habitat disturbance, diverse methods for reducing damage including capturing some individuals within the limit that does not disperse Hydropotes inermis population in Daebudo Island must be carried out.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9611-9614
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• 2014

Purpose: To investigate the clinical value in lung cancer of a combination of four serum tumor markers, haptoglobin (Hp), carcinoembryonic antigen (CEA), neuron specific enolase (NSE) as well as the cytokeratin 19 fragment (CYFRA21-1). Materials and Methods: Serum Hp (with immune-turbidimetric method), CEA, NSE, CYFRA21-1 (with chemiluminescence method) level were assessed in 193 patients with lung cancer, 87 patients with benign lung disease and 150 healthy controls. Differences of expression were compared among groups, and joint effects of these tumor markers for the diagnosis of lung cancer were analyzed. Results: Serum tumor marker levels in patients with lung cancer were obviously higher than those with benign lung disease and normal controls (p<0.01). The sensitivities of Hp, CEA, NSE and CYFRA21-1 were 43.5%, 40.9%, 23.3% and 41.5%, with specificities of 90.7%, 99.2%, 97.9% and 97.9%. Four tumor markers combined together could produce a positive detection rate of 85.0%, significantly higher than that of any single test. With squamous carcinomas, the positive detection rates with Hp and CYFRA21-1 were higher than that of other markers. In the adenocarcinoma case, the positive detection rate of CEA was higher than that of other markers. For small cell carcinomas, the positive detection rate of NSE was highest. The area under receiver operating characteristic curve ($AUC^{ROC}$) of Hp in squamous carcinoma (0.805) was higher than in adenocarcinoma (0.664) and small cell carcinoma (0.665). Conclusions: Hp can be used as a new serum tumor marker for lung cancer. Combination detection of Hp, CEA, NSE and CYFRA21-1 could significantly improve the sensitivity and specificity in diagnosis of lung cancer, and could be useful for pathological typing.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7421-7426
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• 2013

Background: Colorectal cancer is the fourth most common cancer worldwide and the second leading cause of cancer-related death. FOLFOX is the most common regimen used in the first-line chemotherapy in advanced colorectal cancer, but only half of the patients respond to this regimen and we have almost no clue in predicting resistance in such first-line application. Methods: To explore the potential molecular biomarkers predicting the resistance of FOLFOX regimen as the first-line treatment in advanced colorectal cancer, we screened microRNAs in serum samples from drug-responsive and drug-resistant patients by microarrays. Then differential microRNA expression was further validated in an independent population by reverse transcription and quantitative real-time PCR. Results: 62 microRNAs expressing differentially with fold-change >2 were screened out by microarray analysis. Among them, 5 (miR-221, miR-222, miR-122, miR-19a, miR-144) were chosen for further validation in an independent population (N=72). Our results indicated serum miR-19a to be significantly up-regulated in resistance-phase serum (p=0.009). The ROC curve analysis showed that the sensitivity of serum miR-19a to discriminate the resistant patients from the response ones was 66.7%, and the specificity was 63.9% when the AUC was 0.679. We additionally observed serum miR-19a had a complementary value for cancer embryonic antigen (CEA). Stratified analysis further revealed that serum miR-19a predicted both intrinsic and acquired drug resistance. Conclusions: Our findings confirmed aberrant expression of serum miR-19a in FOLFOX chemotherapy resistance patients, suggesting serum miR-19a could be a potential molecular biomarker for predicting and monitoring resistance to first-line FOLFOX chemotherapy regimens in advanced colorectal cancer patients.

• Bulletin of the Korean Chemical Society
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• v.31 no.8
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• pp.2163-2169
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• 2010

G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are known to be targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are mainly classified as agonists and antagonists. The purpose of this study is to develop a Bayesian classification model, that can predict a compound as either human GPCR agonist or antagonist. Total 6627 compounds experimentally determined as either GPCR agonists or antagonists covering all the classes of GPCRs were gathered to comprise the dataset. This model distinguishes GPCR agonists from GPCR antagonists by using chemical fingerprint, FCFP_6. The model revealed distinctive structural characteristics between agonistic and antagonistic compounds: in general, 1) GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic amines. This model showed very good discriminative ability in general, with pretty good discriminant statistics for the training set (accuracy: 90.1%) and a good predictive ability for the test set (accuracy: 89.2%). Also, receiver operating characteristic (ROC) plot showed the area under the curve (AUC) to be 0.957, and Matthew's Correlation Coefficient (MCC) value was 0.803. The quality of our model suggests that it could aid to classify the compounds as either GPCR agonists or antagonists, especially in the early stages of the drug discovery process.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7463-7466
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• 2015

Aims: To analyse the predictors of recurrence, disease free survival and overall survival in cases with endometrial cancer. Materials and Methods: A total of 152 women diagnosed with endometrial cancer were screened using a prospectively collected database including age, smoking history, menopausal status, body mass index, CA125, systemic disorders, tumor histology, tumor grade, lymphovascular space invasion, tumor diameter, cervical involvement, myometrial invasion, adnexal metastases, positive cytology, serosal involvement, other pelvic metastases, type of surgery, fertility sparing approach to assess their ability to predict recurrence, disease free survival and overall survival. Results: In ROC analyses tumor diameter was a significant predictor of recurrence (AUC:0.771, P<0.001). The optimal cut off value was 3.75 with 82% sensitivity and 63% specificity. In correlation analyses tumor grade (r=0.267, p=0.001), tumor diameter (r=0.297, p<0.001) and the serosal involvement (r=0.464, p<0.001) were found to significantly correlate with the recurrence. In Cox regression analyses when some different combinations of variables included in the model which are found to be significantly associated with the presence of recurrence, tumor diameter was found to be a significant confounder for disease free survival (OR=1.2(95 CI,1.016-1.394, P=0.031). On Cox regression for overall survival only serosal involvement was found to be a significant predictor (OR=20.8 (95 % CI 2.4-179.2, P=0.006). In univariate analysis of tumor diameter > 3.75 cm and the recurrence, there was 14 (21.9 %) cases with recurrence in group with high tumor diameter where as only 3 (3.4 %) cases group with smaller tumor size (Odds ratio:7.9 (95 %CI 2.2-28.9, p<0.001). Conclusions: Although most of the significantly correlated variables are part of the FIGO staging, tumor diameter was also found to be predictor for recurrence with higher values than generally accepted.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1113-1125
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• 2013

Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5349-5353
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• 2014

Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (${\geq}22days$, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (${\geq}61days$ old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors. The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3111-3116
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• 2015

Background: This study was designed to investigate the value of CEA and CA199 in predicting the treatment response to palliative chemotherapy for advanced gastric cancer. Materials and Methods: We studied 189 patients with advanced gastric cancer who received first-line chemotherapy, measured the serum CEA and CA199 levels, used RECIST1.1 as the gold standard and analyzed the value of CEA and CA199 levels changes in predicting the treatment efficacy of chemotherapy. Results: Among the 189 patients, 80 and 94 cases had increases of baseline CEA (${\geq}5ng/ml$) and CA199 levels (${\geq}27U/ml$), respectively. After two cycles of chemotherapy, 42.9% patients showed partial remission, 33.3% stable disease, and 23.8% progressive disease. The area under the ROC curve (AUC) for CEA and CA199 reduction in predicting effective chemotherapy were 0.828 (95%CI 0.740-0.916) and 0.897 (95%CI 0.832-0.961). The AUCs for CEA and CA199 increase in predicting progression after chemotherapy were 0.923 (95%CI 0.865-0.980) and 0.896 (95%CI 0.834-0.959), respectively. Patients who exhibited a CEA decline ${\geq}24%$ and a CA199 decline ${\geq}29%$ had significantly longer PFS (log rank p=0.001, p<0.001). With the exception of patients who presented with abnormal levels after chemotherapy, changes of CEA and CA199 levels had limited value for evaluating the chemotherapy efficacy in patients with normal baseline tumor markers. Conclusions: Changes in serum CEA and CA199 levels can accurately predict the efficacy of first-line chemotherapy in advanced gastric cancer. Patients with levels decreasing beyond the optimal critical values after chemotherapy have longer PFS.