• Title, Summary, Keyword: Cancer prediction

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Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population

  • Kim, Sung Han;Kim, Sohee;Joung, Jae Young;Kwon, Whi-An;Seo, Ho Kyung;Chung, Jinsoo;Nam, Byung-Ho;Lee, Kang Hyun
    • Cancer Research and Treatment
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    • v.50 no.4
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    • pp.1194-1202
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    • 2018
  • Purpose The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model. Materials and Methods A total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model's performance was evaluated using the C-statistic and Hosmer-Lemeshow type chi-square statistics. Results The risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001). Conclusion This model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies.

Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

  • Faradmal, Javad;Soltanian, Ali Reza;Roshanaei, Ghodratollah;Khodabakhshi, Reza;Kasaeian, Amir
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.14
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    • pp.5883-5888
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    • 2014
  • Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

Identification of Heterogeneous Prognostic Genes and Prediction of Cancer Outcome using PageRank (페이지랭크를 이용한 암환자의 이질적인 예후 유전자 식별 및 예후 예측)

  • Choi, Jonghwan;Ahn, Jaegyoon
    • Journal of KIISE
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    • v.45 no.1
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    • pp.61-68
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    • 2018
  • The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.

What is the Most Suitable Time Period to Assess the Time Trends in Cancer Incidence Rates to Make Valid Predictions - an Empirical Approach

  • Ramnath, Takiar;Shah, Varsha Premchandbhai;Krishnan, Sathish Kumar
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.8
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    • pp.3097-3100
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    • 2015
  • Projections of cancer cases are particularly useful in developing countries to plan and prioritize both diagnostic and treatment facilities. In the prediction of cancer cases for the future period say after 5 years or after 10 years, it is imperative to use the knowledge of past time trends in incidence rates as well as in population at risk. In most of the recently published studies the duration for which the time trend was assessed was more than 10 years while in few studies the duration was between 5-7 years. This raises the question as to what is the optimum time period which should be used for assessment of time trends and projections. Thus, the present paper explores the suitability of different time periods to predict the future rates so that the valid projections of cancer burden can be done for India. The cancer incidence data of selected cancer sites of Bangalore, Bhopal, Chennai, Delhi and Mumbai PBCR for the period of 1991-2009 was utilized. The three time periods were selected namely 1991-2005; 1996-2005, 1999-2005 to assess the time trends and projections. For the five selected sites, each for males and females and for each registry, the time trend was assessed and the linear regression equation was obtained to give prediction for the years 2006, 2007, 2008 and 2009. These predictions were compared with actual incidence data. The time period giving the least error in prediction was adjudged as the best. The result of the current analysis suggested that for projections of cancer cases, the 10 years duration data are most appropriate as compared to 7 year or 15 year incidence data.

HE4 as a Serum Biomarker for ROMA Prediction and Prognosis of Epithelial Ovarian Cancer

  • Chen, Wen-Ting;Gao, Xiang;Han, Xiao-Dian;Zheng, Hui;Guo, Lin;Lu, Ren-Quan
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.1
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    • pp.101-105
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    • 2014
  • Background and Purpose: Human epididymis protein 4 (HE4) has been suggested to be a novel biomarker of epithelial ovarian cancer (EOC). The present study aimed to evaluate and compare HE4 with the commonly used marker, carbohydrate antigen 125 (CA125), in prediction and therapy-monitoring of EOC. Patients and Methods: Serum HE4 concentrations from 123 ovarian cancer patients and 174 controls were measured by Roche electrochemiluminescent immunoassay (ECLIA). Risk of ovarian malignancy algorithm (ROMA) values were calculated and assessed. In addition, the prospects of HE4 detection for therapy-monitoring were evaluated in EOC patients. Results: The ROMA score could classify patients into high- and low-risk groups with malignancy. Indeed, lower serum HE4 was significantly associated with successful surgical therapy. Specifically, 38 patients with EOC exhibited a greater decline of HE4 compared with CA125. In contrast, elevation of HE4 better predicted recurrence (of 46, 11 patients developed recurrence, and with it increased HE4 serum concentrations) and a poor prognosis than CA125. Conclusions: This study suggests that serum HE4 levels are closely associated with outcome of surgical therapy and disease prognosis in Chinese EOC patients.

Development of a Medial Care Cost Prediction Model for Cancer Patients Using Case-Based Reasoning (사례기반 추론을 이용한 암 환자 진료비 예측 모형의 개발)

  • Chung, Suk-Hoon;Suh, Yong-Moo
    • Asia pacific journal of information systems
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    • v.16 no.2
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    • pp.69-84
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    • 2006
  • Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

Disease Prediction Using Ranks of Gene Expressions

  • Kim, Ki-Yeol;Ki, Dong-Hyuk;Chung, Hyun-Cheol;Rha, Sun-Young
    • Genomics & Informatics
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    • v.6 no.3
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    • pp.136-141
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    • 2008
  • A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

Self-Assembled Nanoparticles of Bile Acid-Modified Glycol Chitosans and Their Applications for Cancer Therapy

  • Kim Kwangmeyung;Kim Jong-Ho;Kim Sungwon;Chung Hesson;Choi Kuiwon;Kwon Ick Chan;Park Jae Hyung;Kim Yoo-Shin;Park Rang-Won;Kim In-San;Jeong Seo Young
    • Macromolecular Research
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    • v.13 no.3
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    • pp.167-175
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    • 2005
  • This review explores recent works involving the use of the self-assembled nanoparticles of bile acid-modified glycol chitosans (BGCs) as a new drug carrier for cancer therapy. BGC nanoparticles were produced by chemically grafting different bile acids through the use of l-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC). The precise control of the size, structure, and hydrophobicity of the various BGC nanoparticles could be achieved by grafting different amounts of bile acids. The BGC nanoparticles so produced formed nanoparticles ranging in size from 210 to 850 nm in phosphate-buffered saline (PBS, pH=7.4), which exhibited substantially lower critical aggregation concentrations (0.038-0.260 mg/mL) than those of other low-molecular-weight surfactants, indicating that they possess high thermodynamic stability. The SOC nanoparticles could encapsulate small molecular peptides and hydrophobic anticancer drugs with a high loading efficiency and release them in a sustained manner. This review also highlights the biodistribution of the BGC nanoparticles, in order to demonstrate their accumulation in the tumor tissue, by utilizing the enhanced permeability and retention (EPR) effect. The different approaches used to optimize the delivery of drugs to treat cancer are also described in the last section.

Classification of Genes Based on Age-Related Differential Expression in Breast Cancer

  • Lee, Gunhee;Lee, Minho
    • Genomics & Informatics
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    • v.15 no.4
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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.