• Tuberculosis and Respiratory Diseases
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• v.78 no.3
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• pp.180-189
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• 2015

Accurate lymph node staging of lung cancer is crucial in determining optimal treatment plans and predicting patient outcome. Currently used lymph node maps have been reconciled to the internationally accepted International Association for the Study of Lung Cancer (IASLC) map published in the seventh edition of TNM classification system of malignant tumours. This article provides computed tomographic illustrations of the IASLC nodal map, to facilitate its application in day-to-day clinical practice in order to increase the appropriate classification in lung cancer staging.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• Journal of Chest Surgery
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• v.26 no.6
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• pp.463-469
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• 1993

From May 1986 to May 1992, 72 patients were diagnosed and operated for primary lung cancer, among them 65 patients were clinically evaluated at the department of Thoracic & Cardiovascular Surgery, Masan Koryo General Hospital. 1. There were 52 males 13 females[M:F=4:1], and 5th, 6th decade of life[72%] was peak incidence. 2. The preoperative diagnosis and its positive rate were sputum cytology 35%, bronchoscopy 47%, pleural effusion cytology 80%, and pleural biopsy 50%. 3. The classification histologic types were squamous cell cancer 71%, adenocarcinoma 17%, undifferentiated cell carcinoma 4.6%, and staging classification were Stage I 31%, Stage II 22%, Stage IIIa 26%, and Stage IIIb 20%. 4. The operative methods were lobectomy 52%, pneumonectomy 36%, and open biopsy 12%, and operability was 89%, resectability was 88%. 5. The postoperative complications developed 13 patients[22%], and operative mortality was 5%. 6. The overall actuarial survival rate was 1year 70%, 2year 42%, 3year 32%, 4year 26%, and 5year 22%, according to Stage 5year survival rate was Stage I 37%, Stage II 22%, Stage IIIa 3year 12%, Stage IIIb 2year 23%. And according to operative method lobectomy 23%, pneumonectomy 19%.

• Journal of Gastric Cancer
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• v.22 no.4
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• pp.273-305
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• 2022

Gastric cancer (GC) is one of the most common lethal malignant neoplasms worldwide, with limited treatment options for both locally advanced and/or metastatic conditions, resulting in a dismal prognosis. Although the widely used morphological classifications may be helpful for endoscopic or surgical treatment choices, they are still insufficient to guide precise and/or personalized therapy for individual patients. Recent advances in genomic technology and high-throughput analysis may improve the understanding of molecular pathways associated with GC pathogenesis and aid in the classification of GC at the molecular level. Advances in next-generation sequencing have enabled the identification of several genetic alterations through single experiments. Thus, understanding the driver alterations involved in gastric carcinogenesis has become increasingly important because it can aid in the discovery of potential biomarkers and therapeutic targets. In this article, we review the molecular classifications of GC, focusing on The Cancer Genome Atlas (TCGA) classification. We further describe the currently available biomarker-targeted therapies and potential biomarker-guided therapies. This review will help clinicians by providing an inclusive understanding of the molecular pathology of GC and may assist in selecting the best treatment approaches for patients with GC.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.347-352
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• 2021

In this paper, we propose a method for finding feature subsets that are effective for classification in an input dataset by using a multi-agent reinforcement learning method. In the field of machine learning, it is crucial to find features suitable for classification. A dataset may have numerous features; while some features may be effective for classification or prediction, others may have little or rather negative effects on results. In machine learning problems, feature selection for increasing classification or prediction accuracy is a critical problem. To solve this problem, we proposed a feature selection method based on reinforced learning. Each feature has one agent, which determines whether the feature is selected. After obtaining corresponding rewards for each feature that is selected, but not by the agents, the Q-value of each agent is updated by comparing the rewards. The reward comparison of the two subsets helps agents determine whether their actions were right. These processes are performed as many times as the number of episodes, and finally, features are selected. As a result of applying this method to the Wisconsin Breast Cancer, Spambase, Musk, and Colon Cancer datasets, accuracy improvements of 0.0385, 0.0904, 0.1252 and 0.2055 were shown, respectively, and finally, classification accuracies of 0.9789, 0.9311, 0.9691 and 0.9474 were achieved, respectively. It was proved that our proposed method could properly select features that were effective for classification and increase classification accuracy.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.160-165
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• 2018

Breast cancer remains the second leading cause of cancer death among woman, worldwide, despite advances in identifying novel targeted therapies and the development of treating strategies. Classification of clinical subtypes (ER+, PR+, HER2+, and TNBC (Triple-negative)) increases the complexity of breast cancers, which thus necessitates further investigation. Mouse models used in breast cancer research provide an essential approach to examine the mechanisms and genetic pathway in cancer progression and metastasis and to develop and evaluate clinical therapeutics. In this review, we summarize tumor transplantation models and genetically engineered mouse models (GEMMs) of breast cancer and their applications in the field of human breast cancer research and anti-cancer drug development. These models may help to improve the knowledge of underlying mechanisms and genetic pathways, as well as creating approaches for modeling clinical tumor subtypes, and developing innovative cancer therapy.

• Korean Journal of Clinical Laboratory Science
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• v.43 no.2
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• pp.48-56
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• 2011

Gastrointestinal stromal tumor (GIST) is a mesenchymal tumor and is associated with a specific immunophenotype index. It is very important to identify the specific immunophenotype and the diagnosis for the treatment GIST patients. Ninety two cases of GIST analyzed in this study were immuno-stained for c-kit, DOG1, CD34, PKC-${\theta}$, PDGFR-${\alpha}$. The rate of positive staining and statistical significance were then compared. In addition, the GISTs were analyzed as followings: very low risk, low risk, intermediate risk and high risk according to tumor size and nuclear division, and later correlated with clinical parameters. The results of the GIST positive stainings were: DOG1 (95.7%), PKC-${\theta}$ (90.2%), PDGFR-${\alpha}$ (88.0%), c-kit (87.0%) and CD34 (71.7%). Only DOG1 staining showed a statistical significance of p<0.05. It was identified in the classification system of histologic risk that staining expression of DOG1, PKC-${\theta}$, PDGFR-${\alpha}$ were significantly increased as histologic risk increases (p<0.05). However, clinical parameters such as age and sex of patients have no correlations with the classification system of histologic risk (p>0.05). Therefore, in this study, the expression of DOG1 showed statistical significance and DOG1, PKC-${\theta}$, PDGFR-${\alpha}$ staining increased significantly as the histologic risk increases in histologic classification system. Taken together, the DOG1 staining should be very effective for the diagnosis of GIST patients.

• Journal of Gastric Cancer
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• v.11 no.2
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• pp.94-100
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• 2011

Purpose: The 7th AJCC tumor node metastasis (TNM) staging system modified the classification of the lymph node metastasis widely compared to the 6th edition. To evaluate the prognostic predictability of the new TNM staging system, we analyzed the survival rate of the gastric cancer patients assessed by the 7th staging system. Materials and Methods: Among 2,083 patients who underwent resection for gastric cancer at the department of surgery, Hanyang Medical Center from July 1992 to December 2009, This study retrospectively reviewed 5-year survival rate (5YSR) of 624 patients (TanyN3M0: 464 patients, TanyNanyM1: 160 patients) focusing on the number of metastatic lymph node and distant metastasis. We evaluated the applicability of the new staging system. Results: There were no significant differences in 5YSR between stage IIIC with more than 29 metastatic lymph nodes and stage IV (P=0.053). No significant differences were observed between stage IIIB with more than 28 metastatic lymph nodes and stage IV (P=0.093). Distinct survival differences were present between patients who were categorized as TanyN3M0 with 7 to 32 metastatic lymph nodes and stage IV. But patients with more than 33 metastatic lymph nodes did not show any significant differences compared to stage IV (P=0.055). Among patients with TanyN3M0, statistical significances were seen between patients with 7 to 30 metastatic lymph nodes and those with more than 31 metastatic lymph nodes. Conclusions: In the new staging system, modifications of N classification is mandatory to improve prognostic prediction. Further study involving a greater number of cases is required to demonstrate the most appropriate cutoffs for N classification.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.3
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• pp.251-257
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• 2023

Backgrounds/Aims: This is a retrospective analysis of whether the 8th edition American Joint Committee on Cancer (AJCC) was a significant improvement over the 7th AJCC distal extrahepatic cholangiocarcinoma classification. Methods: In total, 111 patients who underwent curative resection of mid-distal bile duct cancer from 2002 to 2019 were included. Cases were re-classified into 7th and 8th AJCC as well as clinicopathological univariate and multivariate, and Kaplan-Meier survival curve and log rank were calculated using R software. Results: In patient characteristics, pancreaticoduodenectomy/pylorus preserving pancreaticoduodenectomy had better survival than segmental resection. Only lymphovascular invasion was found to be significant (hazard ratio 2.01, p = 0.039) among all clinicopathological variables. The 8th edition AJCC Kaplan Meier survival curve showed an inability to properly segregate stage I and IIA, while there was a large difference in survival probability between IIA and IIB. Conclusions: The 8th distal AJCC classification did resolve the anatomical issue with the T stage, as T1 and T3 showed improvement over the 7th AJCC, and the N stage division of the N1 and N2 category was found to be justified, with poorer survival in N2 than N1. Meanwhile, in TMN staging, the 8th AJCC was able differentiate between early stage (I and IIA) and late stage (IIB and III) to better explain the patient prognosis.