• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.999-1009
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• 2004

As a new approach to the diagnosis of cancers, bioinformatics attracts great interest these days. Machine teaming techniques have produced valuable results, but the field of medicine requires not only highly accurate classifiers but also the effective analysis and interpretation of them. Since gene expression data in bioinformatics consist of tens of thousands of features, it is nearly impossible to represent their relations directly. In this paper, we propose a method composed of a feature selection method and genetic programming. Rank-based feature selection is adopted to select useful features and genetic programming based arithmetic operators is used to generate classification rules with features selected. Experimental results on Lymphoma cancer dataset, in which the proposed method obtained 96.6% test accuracy as well as useful classification rules, have shown the validity of the proposed method.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.357-364
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• 1997

The early detection of breast cancer is clearly a key ingredient for reducing breast cancer mortality. Microcalcification is the only visible feature of the DCIS's(ductal carcinoma in situ) which consist 15 ~ 20% of screening-detected breast cancer. Therefore, the analysis of the shapes and distributions of microcalcifications is very significant for the early detection. The automatic detection procedures have b(:on the concern of digital image processing for many years. We proposed here one efficient method which is essentially statistical pattern classification accelerated by one representative feature, correlation coefficient. We compared the results by this additional feature with results by a simple gray level thresholding. The average detection rate was increased from 48% by gray level feature only to 83% by the proposed method The performances were evaluated with TP rates and FP counts, and also with Bayes errors.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1124-1134
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• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.137-144
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• 2000

Clustered microcalcifications in X-ray mammograms are an important sign for the diagnosis of breast cancer. A shape-based method, which is based on the morphological features of clustered microcalcifications, is proposed for classifying clustered microcalcifications into benign or malignant categories. To verify the effectiveness of the proposed shape features, clinical mammograms were used to compare the classification performance of the proposed shape features with those of conventional textural features, such as the spatial gray-leve dependence method and the wavelet-based method. Image features extracted from these methods were used as inputs to a three-layer backpropagation neural network classifier. The classification performance of features extracted by each method was studied by using receiver operating-characteristics analysis. The proposed shape features were shown to be superior to the conventional textural features with respect to classification accuracy.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1203-1211
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• 2022

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

• Journal of Gastric Cancer
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• v.18 no.3
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• pp.209-217
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• 2018

Although the incidence of gastroesophageal junction (GEJ) adenocarcinoma has been increasing worldwide, no standardized surgical strategy for its treatment has been established. This study aimed to provide an update on the surgical treatment of GEJ adenocarcinoma by reviewing previous reports and propose recommended surgical approaches. The Siewert classification is widely used for determining which surgical procedure is used, because previous studies have shown that the pattern of lymph node (LN) metastasis depends on tumor location. In terms of surgical approaches for GEJ adenocarcinoma, a consensus was reached based on two randomized controlled trials. Siewert types I and III are treated as esophageal cancer and gastric cancer, respectively. Although no consensus has been reached regarding the treatment of Siewert type II, several retrospective studies suggested that the optimal treatment strategy includes paraaortic LN dissection. Against this background, a Japanese nationwide prospective trial is being conducted to determine the proportion of LN metastasis in GEJ cancers and to identify the optimal extent of LN dissection in each type.

• BMB Reports
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• v.53 no.12
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• pp.664-669
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• 2020

Breast cancer is one of the most frequently diagnosed cancers. Although biomarkers are continuously being discovered, few specific markers, rather than classification markers, representing the aggressiveness and invasiveness of breast cancer are known. In this study, we used samples from canine mammary tumors in a comparative approach. We subjected 36 fractions of both canine normal and mammary tumor plasmas to high-performance quantitative proteomics analysis. Among the identified proteins, LCAT was selectively expressed in mixed tumor samples. With further MRM and Western blot validation, we discovered that the LCAT protein is an indicator of aggressive mammary tumors, an advanced stage of cancer, possibly highly metastatic. Interestingly, we also found that LCAT is overexpressed in high-grade and lymph-node-positive breast cancer in silico data. We also demonstrated that LCAT is highly expressed in the sera of advanced-stage human breast cancers within the same classification. In conclusion, we identified a possible common plasma protein biomarker, LCAT, that is highly expressed in aggressive human breast cancer and canine mammary tumor.

• Korean Journal of Head & Neck Oncology
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• v.33 no.2
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• pp.9-15
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• 2017

The recently released the $8^{th}$ edition of the American Joint Committee on Cancer (AJCC) Staging Manual introduces significant modifications from the prior $7^{th}$ edition. In this paper, the contents of the new changes in the decision of cancer of the head and neck is summarized except changes in staging of skin and thyroid cancer. In addition to the 8th edition, 1) Addition of extracapsular involvement in metastatic lymph nodes (N category) 2) Oral cancer T classification change, 3) Staging of the pharyngeal cancer was divided into 3 chapters: high-risk human papilloma virus (HR-HPV) associated oropharyngeal cancer (OPC), non HR-HPV associated OPC and hypopharynx cancer (HPC), and nasopharynx cancer (NPC) 4) Changes in T and N classification in NPC, 5) In the case of cancer of unknown primary, P16-positive case is defined as HR-HPV related OPC, and EBV-positive case is defined as NPC. The process that led to these changes highlights the need to collect high-fidelity cancer registry-level data that can be used to confirm prognostic observations identified in institutional data sets. Clinicians will continue to use the latest information for patient care, including scientific content of the 8th Edition Manual. All newly diagnosed cases through December $31^{st}$ 2017 should be staged with the 7th edition. The time extension will allow all partners to develop and update protocols and guidelines and for software vendors to develop, test, and deploy their products in time for the data collection and implementation of the 8th edition in 2018. The 8th edition strikes a balance between a personalized, complex system and a more general, simpler one that maintains the user-friendliness and worldwide acceptability of the traditional TNM staging paradigm.

• Journal of Gastric Cancer
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• v.23 no.4
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• pp.561-573
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• 2023

Purpose: This study aimed to compare the long-term functional and patient-reported outcomes between intra-corporeal delta-shaped gastroduodenostomy and gastrojejunostomy after laparoscopic distal gastrectomy for gastric cancer. Materials and Methods: We retrospectively reviewed clinicopathological data from 616 patients who had undergone laparoscopic distal gastrectomy for stage I gastric cancer between January 2015 and September 2020. Among them, 232 patients who had undergone delta-shaped anastomosis and another 232 who had undergone Billroth II anastomosis were matched using propensity scores. Confounding variables included age, sex, body mass index, physical status classification, tumor location, and T classification. Postoperative complications, nutritional outcomes, endoscopic findings, and quality of life (QoL) were compared between the 2 groups. Results: No significant differences in postoperative complications or nutritional parameters between the two groups were observed. Annual endoscopic findings revealed more residual food and less bile reflux in the delta group (P<0.001) than in the Billroth II group. Changes of QoL were significantly different regarding emotional function, insomnia, diarrhea, reflux symptoms, and dry mouth (P=0.007, P=0.002, P=0.013, P=0.001, and P=0.03, respectively). Among them, the delta group had worse insomnia, reflux symptoms, and dry mouth within three months postoperatively. Conclusions: Long-term nutritional outcomes and QoL were comparable between the delta and Billroth II groups. However, more residual food and worse short-term QoL regarding insomnia, reflux symptoms, and dry mouth were observed in the delta group. Longer fasting time before endoscopic evaluation and short-term symptom management would have been helpful for the delta group.