• Journal of Gastric Cancer
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• v.13 no.3
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• pp.129-135
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• 2013

Gastric cancer is the second leading cause of cancer-related deaths worldwide. In advanced and metastatic gastric cancer, the conventional chemotherapy with limited efficacy shows an overall survival period of about 10 months. Patient specific and effective treatments known as personalized cancer therapy is of significant importance. Advances in high-throughput technologies such as microarray and next generation sequencing for genes, protein expression profiles and oncogenic signaling pathways have reinforced the discovery of treatment targets and personalized treatments. However, there are numerous challenges from cancer target discoveries to practical clinical benefits. Although there is a flood of biomarkers and target agents, only a minority of patients are tested and treated accordingly. Numerous molecular target agents have been under investigation for gastric cancer. Currently, targets for gastric cancer include the epidermal growth factor receptor family, mesenchymal-epithelial transition factor axis, and the phosphatidylinositol 3-kinase-AKT-mammalian target of rapamycin pathways. Deeper insights of molecular characteristics for gastric cancer has enabled the molecular classification of gastric cancer, the diagnosis of gastric cancer, the prediction of prognosis, the recognition of gastric cancer driver genes, and the discovery of potential therapeutic targets. Not only have we deeper insights for the molecular diversity of gastric cancer, but we have also prospected both affirmative potentials and hurdles to molecular diagnostics. New paradigm of transdisciplinary team science, which is composed of innovative explorations and clinical investigations of oncologists, geneticists, pathologists, biologists, and bio-informaticians, is mandatory to recognize personalized target therapy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1693-1698
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• 2015

The heterogeneous nature of lung cancer has become increasingly apparent since introduction of molecular classification. In general, advanced lung cancer is an aggressive malignancy with a poor prognosis. Activating alterations in several potential driver oncogenic genes have been identified, including EGFR, ROS1 and ALK and understanding of their molecular mechanisms underlying development, progression, and survival of lung cancer has led to the design of personalized treatments that have produced superior clinical outcomes in tumours harbouring these mutations. In light of the tsunami of new biomarkers and targeted agents, next generation sequencing testing strategies will be more appropriate in identifying the patients for each therapy and enabling personalized patients care. The challenge now is how best to interpret the results of these genomic tests, in the context of other clinical data, to optimize treatment choices. In genomic era of cancer treatment, the traditional one-size-fits-all paradigm is being replaced with more effective, personalized oncologic care. This review provides an overview of lung cancer genomics and personalized treatment.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1661-1664
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• 2013

Recently there have been numerous advances in understanding the genetic basis of cancer which have resulted in more appropriate treatments. In this paper we describe the experience of the Burzynski Clinic, involved in treatment of numerous patients based on personalized approach using novel combinations for difficult-to-treat malignancies, with gynecological cancers. This retrospective study was conducted by extracting data from Burzynski Clinic's medical records and comprehensive review. Among the advanced refractory ovarian cancers cases (N=33), an objective response (OR) was found in 42.4%. We anticipate that with improved technology and novel therapeutics this rate will increase and adverse events will be reduced.

• Korean Journal of Head & Neck Oncology
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• v.39 no.1
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• pp.1-9
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• 2023

Technological advancement in human genome analysis and ICT (information & communication technologies) brought 'precision medicine' into our clinical practice. Precision medicine is a novel medical approach that provides personalized treatments tailored to each individual by precisely segmenting patient populations, based on robust data including a person's genetic information, disease information, lifestyle information, etc. Precision medicine has a potential to be applied to treating a range of tumors, in addition to non-small cell lung cancer, in which precision oncology has been actively practiced. In this article, we are reviewing precision medicine in head and neck cancer (HNC) with focus on tumor agnostic biomarkers and treatments such as NTRK, MSI-H/dMMR, TMB-H and BRAF V600E, all of which were recently approved by U.S. Food and Drug Administration (FDA).

• Smart Media Journal
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• v.2 no.4
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• pp.34-40
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• 2013

Rapid sequencing of individual genomes with next generation sequencer opens new horizon to biology and personalized medicine. The analyzed sequences help to check several genomic abnormality, genomic expression, epigenomic phenotypes, gene annotation after assembly of their reads. Several trials integrating genomic information and clinical data will assist disease diagnostics and clinical treatments. To have a large step towards individualized medicine, development of smart interface linking specialized sequence data to the public is necessary.

• Korea Journal of Hospital Management
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• v.26 no.2
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• pp.56-67
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• 2021

Background: Regular doctor visits are vital for hypertension patients, especially for who have never received hypertension medication or non-pharmacologic therapy. This study purposed to study factors affecting outpatient visits for patients diagnosed with hypertension. Methods: This study included 59,009 respondents with hypertension over 30 from 2019 Community Health Survey data. Outpatient visits were defined by having hypertension treatments such as medication or non-pharmacologic therapy. Logistic regression was used to examine the factors affecting outpatient visits using SAS ver. 9.3. Results: 57,081(96.73%) patients with hypertension were identified as those having a outpatient visit for hypertension treatments, whereas 1,928(3.27%) patients did not have visits. Patient's characteristics such as gender, age, periods of hypertension, education level, perception of the blood pressure, hypertension management education, place of living, body mass index, depression and diabetes were found to have statistically significant relationship with the outpatient visits. Practical Implications: There is a need to select patients with high blood pressure who are unlikely to visit for hypertension treatments based on the study results. For those, establishing a personalized management plan such as health education and counseling programs will be helpful for the successful implementation of national chronic disease management program.

• Journal of Korean Traditional Oncology
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• v.17 no.2
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• pp.1-16
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• 2012

Background : Integrative cancer treatment is a holistic approach embracing body, mind, and spirit incorporating conventional treatments of surgery, chemotherapy, radiation and personalized complementary treatments. Wheel Balance Therapy (WBT) of East-West Cancer Center(EWCC), Dunsan Oriental Hospital of Daejeon University was developed to balance out all factors involved in cancer care based on the traditional theories of oriental medicine. Objective : This work aims to analytically review literatures on WBT and its related components. Methods : Literatures published from January 1st, 1990 to April 30th, 2011 were reviewed focusing on 4 main components of WBT; herbal medicine, immune activation, anti-cancer diet, and breathing/meditation. Data were retrieved from medical search engines and electronic data bases including Pubmed, Research Information sharing Service (RISS), Korean-studies Information Service System (KISS), China National Knowledge Infrastructure (CNKI), and Korea's National Digital Library (KNDL). Results : In this review, EWCC's most commonly prescribed formulas are explored. The composition of the formulas, their use in clinical settings as well as the background studies and other therapeutic efficacies are explained. Information on incorporating anti-cancer dietary support and breathing and meditation techniques, other therapies practiced as part of the center's integrative cancer care are also covered. Conclusion : WBT based on holistic theories of oriental medicine embracing body, mind, and spirit is expected to further contribute in promotion of cancer patients' quality of life and prolonged survival time.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.175-182
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• 2011

This study proposes CAOPI(Computer Aided Organ Prediction Index) system based on APACHE II(Acute Physiology And Chronic Health Evaluation) for classifying disease severity and predicting the conditions of patients' major organs. The existing ICU disease severity evaluation is mostly about calculating risk scores using patients' data at certain points, which has limitations on making precise treatments. CAOPI system is designed to provide personalized treatments by classifying accurate severity degrees of emergency patients, predicting patients' mortality rate and scoring the conditions of certain organs.

• Journal of Gastric Cancer
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• v.19 no.4
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• pp.375-392
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• 2019

Preoperative chemo- and radiotherapeutic strategies followed by surgery are currently a standard approach for treating locally advanced gastric and esophagogastric junction cancer in Western countries. However, in a large number of cases, the tumor is extremely resistant to these treatments and the patients are exposed to unnecessary toxicity and delayed surgical therapy. The current clinical trials evaluating the combination of preoperative systemic therapies with modern targeted and immunotherapeutic agents represent a unique opportunity for identifying predictive biomarkers of response to select patients that would benefit the most from these treatments. However, it is of utmost importance that these potential biomarkers are corroborated by extensive preclinical and translational research. The aim of this review article is to present the most promising biomarkers of response to classic chemotherapeutic, anti-HER2, antiangiogenic, and immunotherapeutic agents that can be potentially useful for personalized preoperative systemic therapies in gastric cancer patients.

• 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.