• Genomics & Informatics
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• 제15권4호
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• 한국정보통신학회논문지
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• 제14권10호
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• pp.2365-2370
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• 2010

마이크로어레이를 기반으로 하는 암 분류 방법은 암 종류에 따라 다르게 발현되는 유전자 양상을 통계적으로 발견함으로써 정확한 암 분류에 기여할 수 있다. 따라서 현재의 마이크로어레이 기술을 이용해서 효과적으로 암을 분류하기 위해서는 특정 암과 밀접하게 관련이 있는 정보력 있는 유전자를 선택하는 과정이 필수적이다. 본 논문에서는 난소 암 마이크로어레이 데이터를 이용하여 암에 영향을 미치는 가장 다르게 발현할 가능성이 있는 표지 유전자를 추출할 수 있는 시스템을 고안하고, 다층퍼셉트론 분류기를 이용하여 기존의 마이크로어레이 시스템과 분류 성능을 비교분석하였다. 그 결과 ANOVA를 이용하여 선택된 표지 유전자를 포함하는 마이크로어레이 데이터 셋에서 98.61%의 향상된 분류 성능을 보였다.

• Journal of Digestive Cancer Reports
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• 제7권1호
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• pp.13-17
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• 2019

Stomach cancer is the fifth most common malignancy in the world. The incidence of stomach cancer is declining worldwide, however, gastric cancer still remains the third most common cause of cancer death. The tumor, node, and metastasis (TNM) staging system has been frequently used as a method for cancer staging system and the most important reference in cancer treatment. In 2016, the classification of gastric cancer TNM staging was revised in the 8^th American Joint Committee on Cancer (AJCC) edition. There are several modifications in stomach cancer staging in this edition compared to the 7^th edition. First, the anatomical boundary between esophagus and stomach has been revised, therefore the definition of stomach cancer and esophageal cancer has refined. Second, N3 is separated into N3a and N3b in pathological classification. Patients with N3a and N3b revealed distinct prognosis in stomach cancer, and these results brought changes in pathological staging. Several large retrospective studies were conducted to compare staging between the 7^th and 8^th AJCC editions including prognostic value, stage grouping homogeneity, discriminatory ability, and monotonicity of gradients globally. The main objective of this review is to evaluate the clinical and pathological implications of AJCC 8^th staging classification in the stomach cancer.

• Journal of Digestive Cancer Reports
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• 제7권1호
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• pp.8-12
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• 2019

Despite its declining incidence, gastric cancer is globally, still, the third most common cause of cancer-related mortality. Gastric cancer is a heterogeneous disease with diverse pathogenesis and molecular backgrounds. Therefore several systems have been proposed to aid in the classification of gastric adenocarcinoma based on the macroscopic, microscopic and anatomical features of the tumor. However, these classifications did not reflect the pathogenesis of the disease. Recently, genomic analysis has identified several subtypes of gastric adenocarcinoma and a detailed understanding of the molecular biology behind the neoplastic phenotype is possible to develop of more effective therapies. We will describe the existing various classification of gastric cancer and the recently introduced molecular biology and immunological classification.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.124-131
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• 2024

Breast cancer classification using ultrasound, while widely employed, faces challenges due to its relatively low predictive value arising from significant overlap in characteristics between benign and malignant lesions, as well as operator-dependency. To alleviate these challenges and reduce dependency on radiologist interpretation, the implementation of automatic breast cancer classification in ultrasound image can be helpful. To deal with this problem, we propose a semi-supervised deep learning framework for breast cancer classification. In the proposed method, we could achieve reasonable performance utilizing less than 50% of the training data for supervised learning in comparison to when we utilized a 100% labeled dataset for training. Though it requires more modification, this methodology may be able to alleviate the time-consuming annotation burden on radiologists by reducing the number of annotation, contributing to a more efficient and effective breast cancer detection process in ultrasound images.

• Journal of Multimedia Information System
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• 제3권3호
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• pp.97-102
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• 2016

Deep learning enables machines to have perception and can potentially outperform humans in the medical field. It can save a lot of time and reduce human error by detecting certain patterns from medical images without being trained. The main goal of this paper is to build the optimal model for breast mass classification by applying various methods that influence the performance of Convolutional Neural Network (CNN). Google's newly developed software library Tensorflow was used to build CNN and the mammogram dataset used in this study was obtained from 340 breast cancer cases. The best classification performance we achieved was an accuracy of 0.887, sensitivity of 0.903, and specificity of 0.869 for normal tissue versus malignant mass classification with augmented data, more convolutional filters, and ADAM optimizer. A limitation of this method, however, was that it only considered malignant masses which are relatively easier to classify than benign masses. Therefore, further studies are required in order to properly classify any given data for medical uses.

• 대한한의학회지
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• 제16권2호
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• pp.149-176
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• 1995

The results were as follow: 1. In classification of the virulence of medicines, it is the virulent animal that have a deadly poison and the rest is the animal of weak nor non-toxic. 2. In classification of the channel distribution, the most is the medicine that belongs to liver channel, the next are the stomach, lung, kidney and spleen channel. 3. In classification of four characters, the most parts are cool, common and warm medicine and there is a few that is hot and cooling. 4. In classification of five tastes, the most numerous tastes are sweet and salty and the next are acrid, bitter and sour tastes. 5. In classification of the medical action, there are few medicine of invigorating vital energy, tonic therapy and astringent and a great part of the medicine are regulating vital energy and blood, removing blood stasis and mass, clearing away heat-evil and eliminating sputum, calming the river to inhibit the wind-evil and pain control. 6. In classification of the application of cancer, the most numerous disease is the liver cancer and the next are stomach cancer, esophageal cancer, lung cancer, leukemia, uterine cancer,mastitis, brain tumor.

• Journal of Gastric Cancer
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• 제11권2호
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• pp.78-85
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• 2011

Since January of 2010, the seventh edition of UICC tumor node metastasis (TNM) Classification, which has recently been revised, has been applied to almost all cases of malignant tumors. Compared to previous editions, the merits and demerits of the current revisions were analyzed. Many revisions have been made for criteria for the classification of lymph nodes. In particular, all the cases in whom the number of lymph nodes is more than 7 were classified as N3 without being differentiated. Therefore, the coverage of the N3 was broad. Owing to this, there was no consistency in predicting the prognosis of the N3 group. By determining the positive cases to a distant metastasis as TNM stage IV, the discrepancy in the TNM stage IV compared to the sixth edition was resolved. In regard to the classification system for an esophagogastric (EG) junction carcinoma, it was declared that cases of an invasion to the EG junction should follow the classification system for esophageal cancer. A review of clinical cases reported from Asian patients suggests that it would be more appropriate to follow the previous editions of the classification system for gastric cancer. In addition, in the classification of the TNM stages in the overall cases, the discrepancy in the prognosis between the different stages and the consistency in the prognosis between the same TNM stages were achieved to a lesser extent as compared to that previously. Accordingly, further revisions are needed to develop a purposive classification method where the prognosis can be predicted specifically to each variable and the mode of the overall classification can be simplified.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5593-5596
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• 2014

Background: The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. Materials and Methods: The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Results: Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Conclusions: Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.