• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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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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• 제34권5호
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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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• 제1권1호
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• pp.30-38
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• 1998

배경: 간암은 우리나라에 흔한 암으로 암등록 자료($1991{\sim}1992$)에 의하면 암발생율 3위, 암에 의한 사망 원인 중 2위를 차지한다. 암환자에서 환자의 생존기간을 예측하는 것은 환자의 진료에서 환자 자신이나 가족, 의료진에게 매우 중요하다고 생각된다. 본 연구는 간암 환자에서 환자의 생존 기간을 예측할 수 있는 예후 인자를 찾아 간암 환자의 진료에 도움이 되고자 하였다. 방법: 1995년 1월부터 6월 사이에 연세대학교 의과대학 부속 영동세브란스 병원에 간암으로 입원한 환자 91명(남자 73명, 여자 18명)을 대상으로 의무기록을 통해 입원 당시 임상적인 특성 28가지를 조사하였으며, 의무기록과 동사무소 기록을 가지고 1996년 7월 31일까지 추적하여 생존 여부를 확인하였다. Cox proportional hazard model을 이용하여 임상적 특성 중 사망위험도를 높이는 유의한 변수를 얻은 후 이를 예후 인자로 삼았다. 이것을 life regression analysis을 통해 예후 인자 각각이 존재할 때의 생존 기간 및 동반된 예후 인자 갯수에 따른 생존 기간을 예측하였다. 결과: 1) 원발성 간암 91명 중 남자가 73명(80.2%), 여자가 18명(19.8%)이며, 평균 연령은 $56.7{\pm}10.6$세이었고, 추적 불가능한 사람 16명을 제외한 75%명중 그 기간 사이에 사망한 사람이 57명(76%), 생존한 사람이 18명(24%)이었다. 2) 임상적인 특성 중 프로트롬빈 시간(prothrombin time) 40% 미만(RR: relative risk. 10.8), 체중감소(RR. 4.4), 고혈압의 과거력(RR. 3.2), 복수(RR. 2.8), 저칼슘혈증(RR. 2.5)인 경우가 환자의 사망위험도의 유의한 예후 인자였다(P<0.01). 3) 사망위험도 예후 인자 5가지가 모두 있는 경우는 생존 기간이 1.7일, 4가지만 있는 경우는 $4.2{\sim}10.0$일, 3가지만 있는 경우는 $10.4{\sim}41.9$일, 2가지만 있는 경우는 $29.5{\sim}118.1$일, 1가지만 있는 경우는 $124.0{\sim}296.6$일, 모두 없는 경우는 724.0일이었다. 결론: 간암 환자에서 프로트롬빈 시간의 연장(<40%), 체중감소 고혈압의 과거력, 복수, 저칼슘혈증(<8.7mg/dl) 등의 순으로 높은 사망위험도를 예측하게 하는 유의한 인자임을 알 수 있었고, 동반된 예후인자의 갯수로써 생존 기간을 예측할 수 있을 것으로 생각된다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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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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• 제16권18호
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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.

• 대한토목학회논문집
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• 제4권2호
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• pp.35-43
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• 1984

본 연구(硏究)의 목적(目的)은 개수로(開水路)의 수위(水位)의 변동(變動)에 따른 자유(自由) 지하수면(地下水面)과 개수로(開水路)에 작용(作用)하는 부정압력(不定壓力)의 분포(分布)를 계산(計算)할 수 있는 수치(數値) 모형(模型)을 개발(開發)하는데 있다. Diagnostic Eq.은 Point SOR 방법에 의해서, 그리고 Prognostic Eq.은 Implicit Lax-Wendroff 방법에 의하여 해석하였다. Simulation 조건들에서 지하수(地下水) 침투면(浸透面)의 변화를 예측(豫測)하기 위하여 투수성(透水性) 및 불투수성개수로(不透水性開水路)에 대하여 네가지 다른 경우에 대한 결과(結果)를 나타내었다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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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.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권10호
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• pp.397-402
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• 2019

암 환자에게 적절한 치료계획을 제공하기 위해 암의 진행양상 또는 환자의 생존 기간 등에 해당하는 환자의 예후를 정확히 예측하는 것은 생물정보학 분야에서 다루는 중요한 도전 과제 중 하나이다. 많은 연구에서 암 환자의 유전자 발현량 데이터를 이용하여 환자의 예후를 예측하는 기계학습 모델들이 많이 제안되어 오고 있다. 유전자 발현량 데이터는 약 17,000개의 유전자에 대한 수치값을 갖는 고차원의 수치형 자료이기에, 기존의 연구들은 특징 선택 또는 차원 축소 전략을 이용하여 예측 모델의 성능 향상을 도모하였다. 그러나 이러한 접근법은 특징 선택과 예측 모델의 훈련이 분리되어 있어서, 기계학습 모델은 선별된 유전자들이 생물학적으로 어떤 관계가 있는지 알기가 어렵다. 본 연구에서는 유전자 발현량 데이터를 이미지 형태로 변환하여 예후 예측이 효과적으로 특징 선택 및 예후 예측을 수행할 수 있는 기법을 제안한다. 유전자들 사이의 생물학적 상호작용 관계를 유전자 발현량 데이터에 통합하기 위해 Node2Vec을 활용하였으며, 2차원 이미지로 표현된 발현량 데이터를 효과적으로 학습할 수 있도록 합성곱 신경망 모델을 사용하였다. 제안하는 모델의 성능은 이중 교차검증을 통해 평가되었고, 유전자 발현량 데이터를 그대로 이용하는 기계학습모델보다 우월한 예후 예측 정확도를 가지는 것이 확인되었다. Node2Vec을 이용한 유전자 발현량의 새로운 이미지 표현법은 특징 선택으로 인한 정보의 손실이 없어 예측 모델의 성능을 높일 수 있으며, 이러한 접근법이 개인 맞춤형 의학의 발전에 이바지할 것으로 기대한다.

• 대한두경부종양학회지
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• 제38권1호
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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.