• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2895-2899
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• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4199-4202
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• 2012

Objective: The aim of our study is to compare an ovarian malignancy prediction model based on age and four sonographic findings (OMPS1) with a new model called OMPS2 which differs just by adding serum CA125 measurement to (OMPS1). Methods: In a cross sectional comparative study OMPS1 was validated in 830 operated ovarian masses within a 3 years period (2006-2009). Logistic regression analysis was used to construct OMPS2 based on OMPS1 adding serum CA125 findings. The area under the curve for two models was compared in 411 patients. Results: OMPS2 was calculated as follows: OMPS1 + 1.444 (if serum CA125= 36-200) or 3.842 (if serum CA125 is more than 200). AUC of OMPS2 was increased to 84.3% (CI 95% 78.1- 89.8) in comparison to OMPS1 with AUC of 78.1% (CI 95% 71.8-84.5). Conclusion: Our second model is more accurate in prediction of ovarian malignancy, compared with our first model.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4913-4916
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• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6187-6190
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• 2012

We aimed to investigate DNA repair gene expression of response to chemotherapy among gastric patients, and roles in the prognosis of gastric cancer. A total of 209 gastric cancer patients were included in this study between January 2007 and December 2008, all treated with chemotherapy. Polymorphisms were detected by real time PCR with TaqMan probes, and genomic DNA was extracted from peripheral blood samples. The overall response rate was 61.2%. The median progression and overall survivals were 8.5 and 18.7 months, respectively. A significant increased treatment response was found among patients with XPG C/T+T/T or XRCC1 399G/A+A/A genotypes, with the OR (95% CI) of 2.14 (1.15-4.01) and 1.75 (1.04-3.35) respectively. We found XPG C/T+T/T and XRCC1 399 G/A+A/A were associated with a longer survival among gastric cancer patients when compared with their wide type genotypes, with HRs and 95% CIs of 0.49 (0.27-0.89) and 0.56 (0.29-0.98) respectively. Selecting specific chemotherapy based on pretreatment genotyping may be an innovative strategy for further studies.

• Toxicological Research
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• v.35 no.4
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• pp.331-341
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• 2019

Cancer is one of the common causes of death with a high degree of mortality, worldwide. In many types of cancers, if not all, sex-biased disparities have been observed. In these cancers, an individual's sex has been shown to be one of the crucial factors underlying the incidence and mortality of cancer. Accumulating evidence suggests that differentially expressed genes and proteins may contribute to sex-biased differences in male and female cancers. Therefore, identification of these molecular differences is important for early diagnosis of cancer, prediction of cancer prognosis, and determination of response to specific therapies. In the present review, we summarize the differentially expressed genes and proteins in several cancers including bladder, colorectal, liver, lung, and nonsmall cell lung cancers as well as renal clear cell carcinoma, and head and neck squamous cell carcinoma. The sex-biased molecular differences were identified via proteomics, genomics, and big data analysis. The identified molecules represent potential candidates as sex-specific cancer biomarkers. Our study provides molecular insights into the impact of sex on cancers, suggesting strategies for sex-biased therapy against certain types of cancers.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9439-9444
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• 2014

Purpose: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. Materials and Methods: We used two publicly available RNA-Seq datasets from normal prostate tissue and prostate cancer. Putative lncRNAs were predicted using the biological technology, then specific lncRNAs of prostate cancer were found by differential expression analysis and co-expression network was constructed by the weighted gene co-expression network analysis. Results: A total of 1,080 lncRNA transcripts were obtained in the RNA-Seq datasets. Three genes (PCA3, C20orf166-AS1 and RP11-267A15.1) showed a significant differential expression in the prostate cancer tissues, and were thus identified as prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. Conclusions: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.141-158
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• 2019

Objectives: An adverse outcome pathway is a biological pathway that disturbs homeostasis and causes toxicity. It is a conceptual framework for organizing existing biological knowledge and consists of the molecular initiating event, key event, and adverse output. The AOP concept provides intuitive risk identification that can be helpful in evaluating the carcinogenicity of chemicals and in the prevention of cancer through the assessment of chemical carcinogenicity predictions. Methods: We reviewed various papers and books related to the application of AOPs for the prevention of occupational cancer. We mainly used the internet to search for the necessary research data and information, such as via Google scholar(http://scholar.google.com), ScienceDirect(www.sciencedirect.com), Scopus(www.scopus. com), NDSL(http: //www.ndsl.kr/index.do) and PubMed(http://www.ncbi.nlm.nih.gov/pubmed). The key terms searched were "adverse outcome pathway," "toxicology," "risk assessment," "human exposure," "worker," "nanoparticle," "applications," and "occupational safety and health," among others. Results: Since it focused on the current state of AOP for the prediction of toxicity from chemical exposure at work and prospects for industrial health in the context of the AOP concept, respiratory and nanomaterial hazard assessments. AOP provides an intuitive understanding of the toxicity of chemicals as a conceptual means, and it works toward accurately predicting chemical toxicity. The AOP technique has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment. AOP can be applied to the assessment of chemical carcinogenicity along with efforts to understand the effects of chronic toxic chemicals in workplaces. Based on these predictive tools, it could be possible to bring about a breakthrough in the prevention of occupational and environmental cancer. Conclusions: The AOP tool has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment and has been widely used in the field of chemical risk assessment and the evaluation of carcinogenicity at work. It will be a useful tool for prediction, and it is possible that it can help bring about a breakthrough in the prevention of occupational and environmental cancer.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.87-96
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• 2023

Recently, research on predicting the treatment results of diseases using deep learning technology is also active in the medical community. However, small patient data and specific deep learning algorithms were selected and utilized, and research was conducted to show meaningful results under specific conditions. In this study, in order to generalize the research results, patients were further expanded and subdivided to derive the results of a study predicting mortality after lung cancer diagnosis for men and women in their 80s, 90s, and 100s. Using AutoML, which provides large-scale medical information and various deep learning algorithms from the Health Insurance Review and Assessment Service, five algorithms such as Decision Tree, Random Forest, Gradient Boosting, XGBoost, and Logistic Registration were created to predict mortality rates for 84 months after lung cancer diagnosis. As a result of the study, men in their 80s and 90s had a higher mortality prediction rate than women, and women in their 100s had a higher mortality prediction rate than men. And the factor that has the greatest influence on the mortality rate was analyzed as the treatment period.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.15-21
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• 2023

Purpose: The purpose of this study is to compare the performance of the anisotropic analytical algorithm (AAA) and portal dose image prediction (PDIP) for patient-specific quality assurance based on electronic portal imaging device, and to evaluate the clinical feasibility of portal dosimetry using AAA. Subjects and methods: We retrospectively selected a total of 32 patients, including 15 lung cancer patients and 17 liver cancer patients. Verification plans were generated using PDIP and AAA. We obtained gamma passing rates by comparing the calculated distribution with the measured distribution and obtained MLC positional difference values. Results: The mean gamma passing rate for lung cancer patients was 99.5% ± 1.1% for 3%/3 mm using PDIP and 90.6% ± 5.8% for 1%/1 mm. Using AAA, the mean gamma passing rate was 98.9% ± 1.7% for 3%/3 mm and 87.8% ± 5.2% for 1%/1 mm. The mean gamma passing rate for liver cancer patients was 99.9% ± 0.3% for 3%/3 mm using PDIP and 96.6% ± 4.6% for 1%/1 mm. Using AAA, the mean gamma passing rate was 99.6% ± 0.5% for 3%/3 mm and 89.5% ± 6.4% for 1%/1 mm. The MLC positional difference was small at 0.013 mm ± 0.002 mm and showed no correlation with the gamma passing rate. Conclusion: The AAA algorithm can be clinically used as a portal dosimetry calculation algorithm for patientspecific quality assurance based on electronic portal imaging device.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.52-59
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• 2008

In the management of colo-retal and anal cancer, accurate staging, treatment evaluation, early detection of recurrence are main clinical problems. F-18 FDG PET (PET/CT) has been reported as useful in the management of colo-rectal and anal cancer because that PET has high diagnostic performance comparing to conventional studies. In case of liver metastases, for confirmation of no extrahepatic metastases, in case of high risk of metastasis, for avoiding unnecessary operation, PET (PET/CT) is expected more useful. In anal cancer, PET is expected useful in lymph node staging. For the early prediction of chemotherapy or radiation therapy effect PET has been reported as useful, also. In early detection of recurrence by PET, cost-benefit advantages has been suggested, also. PET/CT is expected to have higher diagnostic performance than PET alone.