• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3255-3261
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• 2013

Background: The efficacy of erlotinib is controversial in patients with unknown EGFR mutational status. The aim of this study was to identify the clinicopathological factors that are predictive of erlotinob treatment outcomes for NSCLC patients with unknown EGFR mutational status. Materials and Methods: A retrospective analysis of 109 patients with advanced NSCLC who had previously failed at least one line of chemotherapy and received subsequent treatment with erlotinib (150 mg/day orally) was performed. A Cox proportional hazard model for univariate and multivariate analyses was used to identify the baseline clinical parameters correlating with treatment outcome, expressed in terms of hazard ratios (HRs) and 95% confidence intervals. Results: The median treatment duration was 15 weeks (range, 4-184). The disease control rate was 55%, including disease stability for ${\geq}3$ months for 40% of the patients. Median progression-free survival and median overall survival (OS) were 4.2 and 8.5 months, respectively. The Cox model indicated that an Eastern Cooperative Oncology Group performance status (ECOG PS) ${\geq}2$ (HR 3.82; p<0.001), presence of intra-abdominal metastasis (HR 3.42; p=0.002), 2 or more prior chemotherapy regimens (HR 2.29; p=0.021), and weight loss >5% (HR 2.05; p=0.034) were independent adverse prognostic factors for OS in NSCLC patients treated with erlotinib. Conclusions: This study suggests that NSCLC patients should be enrolled in erlotinib treatment after a first round of unsuccessful chemotherapy to improve treatment success, during which they should be monitored for intra-abdominal metastasis and weight loss.

• Journal of Microbiology and Biotechnology
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• v.34 no.5
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• pp.1164-1177
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• 2024

Esophageal squamous cell carcinoma (ESCC) is among the most common malignant tumors of the digestive tract, with the sixth highest fatality rate worldwide. The ESCC-related dataset, GSE20347, was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene co-expression network analysis was performed to identify genes that are highly correlated with ESCC. A total of 91 transcriptome expression profiles and their corresponding clinical information were obtained from The Cancer Genome Atlas database. A mitochondria-associated risk (MAR) model was constructed using the least absolute shrinkage and selection operator Cox regression analysis and validated using GSE161533. The tumor microenvironment and drug sensitivity were explored using the MAR model. Finally, in vitro experiments were performed to analyze the effects of hub genes on the proliferation and invasion abilities of ESCC cells. To confirm the predictive ability of the MAR model, we constructed a prognostic model and assessed its predictive accuracy. The MAR model revealed substantial differences in immune infiltration and tumor microenvironment characteristics between high- and low-risk populations and a substantial correlation between the risk scores and some common immunological checkpoints. AZD1332 and AZD7762 were more effective for patients in the low-risk group, whereas Entinostat, Nilotinib, Ruxolutinib, and Wnt.c59 were more effective for patients in the high-risk group. Knockdown of TYMS significantly inhibited the proliferation and invasive ability of ESCC cells in vitro. Overall, our MAR model provides stable and reliable results and may be used as a prognostic biomarker for personalized treatment of patients with ESCC.

• Journal of Hospice and Palliative Care
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• v.1 no.1
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• pp.30-38
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• 1998

Background : Hepatocellular carcinomoma is the 3rd most common malignancy and the 2nd most common cause of death in Korea. The prediction of life-expectancy in terminal cancer patients is a major problem for patients, families, and physicians. We would like to investigate the prognostic factors of hepatocellular carcinoma, and therefore contribute to the prediction of the survival time of patients with hepatocellular carcinoma. Methods : A total of 91 patients(male 73, female 18) with hepatocellular carcinoma who were admitted to the hospital between January and lune 1995 were entered into the study, and data were collected prospectively on 28 clinical parameters through medical obligation record. We surveyed an obligation and local district office records, and confirmed the surivival of patients till July, 1996. Using Cox-proportional hazard model, give the significant variables related to survival. These determined prognostic factors. Life regressional analysis was used, there were calculated predicted survival day based on combinations of the significant prognostic factors. Results : 1) Out of 91 patients, 73 were male, and 18 were female. The mean age was $56.7{\pm}10.6$ ears. During the study, except for 16 patients who could not follow up, out of 75 patients, the number of deaths was 57(76%) and the number of survivals was 18(24%). 2) Out of the 28 clinical parameters, the prognostic factors related to reduced survival rate were prothrombin time<40%(relative risk:10.8), weight loss(RR:4.4), past history of hypertension (RR:3.2), ascites(RR:2.8), hypocalcemia(RR:2.5)(P<0.001). 3) Out of five factors, the survival day is 1.7 in all of five, $4.2{\sim}10.0$ in four, $10.4{\sim}41.9$ in three, $29.5{\sim}118.1$ in two, $124.0{\sim}296.6$ in one, 724.0 in none. Conclusion : In hepatocellular carcinoma we found that the prognostic factors related to reduce survival rate were prolonged prothrombin time(<40%), weight loss, past history of hypertension, ascites, and hypocalcemia(<8.7mg/dl). The five prognostic factors enabled the prediction of life-expectancy in patients with hepatocellular carcinoma and may assist in managing patients with hepatocellular carcinomal.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5721-5727
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• 2014

Objectives: To evaluate the performance of clustering methods used in the prognostic assessment of categorical clinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognostic nomogram for clinical decisions. Materials and Methods: A total of 332 newly diagnosed HCC patients treated with hepatic resection during 2006-2009 were enrolled. Patients were regularly followed up at outpatient clinics. Clustering methods including the Average linkage, k-modes, fuzzy k-modes, PAM, CLARA, protocluster, and ROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI). Then the clustering variable, age group, tumor size, number of tumor and vascular invasion were studied in a multivariable Cox regression model. A prognostic nomogram was constructed for clinical decisions. Results: The ROCK was best in both the overlapping and non-overlapping cases performed to assess the prognostic value of platelet-based indices. Patients with categorical platelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCC recurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0.01). Tumor size, number of tumor and blood vessel invasion were also associated with high risk of HCC recurrence (all p< 0.01). The nomogram well predicted HCC patient survival at 3 and 5 years. Conclusions: A cluster of platelet-based indices combined with other clinical covariates could be used for prognosis evaluation in HCC.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.2
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• pp.35-43
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• 1984

The purpose of this paper is to develop a mathematical model which can be used to compute the position of the free surface due to water level fluctuations in the canal and the transient pressure distributions along the canal lining. The diagnostic equation has been solved by the point successive over-relaxation method, and the linearized prognostic equation has been solved by the implicit Lax-Wendroff scheme. Four different cases in the simulation conditions are presented for both permeable and impermeable canal lining to predict the transient seepage surface development.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1193-1196
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• 2016

Background: Colorectal cancer (CRC) is the commonest malignancy in the lower gastrointestinal tract in both men and women. It is the third leading cause of cancer-dependent death in the world. In Iran the incidence of colorectal cancer has increased during the last 25 years. Materials and Methods: In this article we analyzed the survival of 447 colorectal patients of Taleghani hospital in Tehran using parametric competing-risks models. The cancers of these patients were diagnosed during 1985 - 2012 and followed up to 2013. The purpose was to assess the association between survival of patients with colorectal cancer in the presence of competing-risks and prognostic factors using parametric models. The analysis was carried out using R software version 3.0.2. Results: The prognostic variables included in the model were age at diagnosis, tumour site, body mass index and sex. The effect of age at diagnosis and body mass index on survival time was statistically significant. The median survival for Iranian patients with colorectal cancer is about 20 years. Conclusions: Survival function based on Weibull model compared with Kaplan-Meier survival function is smooth. Iranian data suggest a younger age distribution compared to Western reports for CRC.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.47-53
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• 2000

Saemangeum coastal area is being constructed the 33km sea dike and 40,000ha reclamation area. The purpose of this study is to find the residual circulations in spring before and after the dike construction by a robust diagnostic and prognostic numerical model. Heat flux at the sea surface in May was adopted on the basis of the daily inflow of solar radiation at the earth surface, assuming an average atmospheric transmission and no clouds, as a function of latitude and time of year(George L.P.,J. E. William,1990). The discharge from the Geum, the Mankyung and the Dongjin rivers was adopted on the basis of experience formula of river flow in May(The M. of C.,Korea, 1993). Water temperature and salinity along the open boundaries are obtained from the results of field observations. The results of spring of the residual flow in the Saemangeum coastal area by a prognostic numerical model lead to the following conclusions: Water temperature in spring is the highest, salinity is the lowest and density is the lowest at the upper layer near the coast after the dike construction. The flow pattern at the upper layer during spring is anti-clockwise circulation between Wi and Shinsi islands. The flow pattern at the lower layer is clockwise circulation between Wi and Shinsi islands.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Korean Journal of Head & Neck Oncology
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• v.38 no.1
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• pp.17-24
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• 2022

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques. Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient's survival. Conclusion: Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient's survival with a considerable level of accuracy.