• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.2091-2095
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• 2013

Background: The $7^{th}$ TNM staging is the first authoritative standard for evaluation of effectiveness of treatment of gastric cancer worldwide. However, revision of pN classification within TNM needs to be discussed. In particular, the N3 sub-stage is becoming more conspicuous. Methods: Clinical data of 302 pN3M0 stage gastric cancer patients who received radical gastrectomy in Tianjin Medical University Cancer Institute and Hospital from January 2001 to May 2006 were retrospectively analyzed. Results: Location of tumor, depth of invasion, extranodal metastasis, gastric resection, combined organs resection, lymph node metastasis, rate of lymph node metastasis, negative lymph nodes count were important prognostic factors of pN3M0 stage gastric cancers. TNM stage was also associated with prognosis. Patients at T2N3M0 stage had a better prognosis than other sub-classification. T3N3M0 and T4aN3aM0 patients had equal prognosis which followed the T2N3M0. T4aN3bM0 and T4bN3aM0 had lower survival rate than the formers. T4bN3bM0 had worst prognosis. In multivariate analysis, TNM stage group and rate of lymph node metastasis were independent prognostic factors. Conclusions: The sub-stage of N3 may be useful for more accurate prediction of prognosis; it should therefore be applied in the TNM stage system.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Genomics & Informatics
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• 제20권2호
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• pp.23.1-23.9
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• 2022

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

• Korean Journal of Radiology
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• 제22권9호
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• pp.1514-1524
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• 2021

Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

• 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.

• 대한조선학회지
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• 제26권4호
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• pp.14-26
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• 1989

선박의 추진성능 추정을 위한 통계해석 기법을 연구하고 전산 프로그램을 만들었다. 조파저항계수의 추정식은 조파저항이론을 이용하여 스테이션 별 횡단면적계수의 곱으로 표현되도록 도출해 내었고, 이에 대한 회귀계수는 모형시험 결과를 회귀분석하여 얻었다. 형상계수, 반류비 및 추력감소율의 추정식들은 선체 주요지수, 스테이션 별 횡단면적계수 및 모형시험 결과들을 순순하게 회귀분석하여 얻었다. 통계해석은 여러가지 기술통계와 단계별 회귀분석 기법을 적절하게 이용하여 수행하였다. 추진성능 추정 프로그램은 저항계수, 추진계수, 프로펠러 단독효율 및 각종 척도효과 등을 모두 쉽게 수용할 수 있도록 다양하면서도 간결하게 만들었다.

• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.1803-1806
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• 2013

Purpose: Carbohydrate antigen (CA) 242 is inversely related to prognosis in many cancers. However, few data regarding CA 242 in esophageal cancer (EC) are available. The aim of this study was to determine the prognostic value of CA 242 and propose an optimum cut-off point in predicting survival difference in patients with esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was conducted of 192 cases. A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimum cuf-off point. Univariate and multivariate analyses were performed to evaluate prognostic parameters for survival. Results: The positive rate for CA 242 was 7.3% (14/192). The ROC curve for survival prediction gave an optimum cut-off of 2.15 (U/ml). Patients with CA 242 ${\leq}$ 2.15 U/ml had significantly better 5-year survival than patients with CA 242 >2.15 U/ml (45.4% versus 22.6%; P=0.003). Multivariate analysis showed that differentiation (P=0.033), CA 242 (P=0.017), T grade (P=0.004) and N staging (P<0.001) were independent prognostic factors. Conclusions: Preoperative CA 242 is a predictive factor for long-term survival in ESCC, especially in nodal-negative patients. We conclude that 2.15 U/ml may be the optimum cuf-off point for CA 242 in predicting survival in ESCC.

• 한국멀티미디어학회논문지
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• 제13권5호
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• pp.722-729
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• 2010

예측 문제를 해결하기 위한 데이타마이닝 기법은 다양한 분야에서 주목받고 있다. 이것에 대한 한 예로 컴퓨터-기반의 질병의 예측 혹은 진단은 CDSS(Clinical Decision support System)에서 가장 중요한 요소이기도 하다. 이러한 예측 문제를 해결하기 위해서 RBF커널 같은 비선형 커널을 사용한 SVM이 가장 널리 사용되고 있는데, 이는 비선형 SVM이 어떠한 다른 분류기법보다 정확한 성능을 보이기 때문이다. 하지만 비선형 SVM을 사용한 경우에는 모델내부를 시각화하는 일이 어려워서 예측결과에 대한 직관적인 이해가 힘들고, 의학 전문가들은 이러한 비선형 SVM의 사용을 기피하고 있는 실정이다. Nomogram은 SVM을 시각화하기 위해 제안된 기법이다. 하지만 이는 선형 SVM의 경우에만 사용이 가능하고. 이 문제를 해결하기 위해서 LRBF 커널이 제안된 바 있다. LRBF 커널은 기존의 RBF 커널을 사용한 SVM과 대등한 결과를 보이면서도 예측결과의 선형적 분석도 가능하게 한다. 본 논문에서는 노모그램(Nomogram)과 LRBF 커널을 사용한 SVM이 통합되어 있는 예측 툴 VRIFA를 제안한다. 이 툴은 사용자와 상호작용하며 비선형 SVM 모델의 내부구조를 데이타의 각 속성별로 보여주는 방법으로 사용자가 예측결과를 직관적으로 이해하도록 도와준다. VRIFA는 Nomogram기반의 피쳐선택(feature selection) 기능도 포함하고 있는데, 이 기능은 예측결과에 부정적인 영향을 끼치거나 중복된 연관성을 보이는 속성을 제거함으로써 모델의 정확도를 높이는 데 기여한다. 그리고 데이터에 포함된 클래스의 비율이 한 쪽으로 치우쳐져 있는 경우에는 ROC 곡선 넓이(AUC)를 예측결과를 평가하기 위한 측도로 사용할 수 있다. 이 툴은 컴퓨터-기반의 질병 예측 혹은 질병의 위험 요소 분석에 대해 연구하는 연구자들에게 유용하게 사용될 것으로 전망하는 바이다.

• Asian Pacific Journal of Cancer Prevention
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• 제14권2호
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• pp.929-934
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• 2013

Aim: To analyze the significance of different clinical factors for prognostic prediction in diffuse large B-cell lymphoma (DLBCL) patients. Methods: Two hundred and twenty-seven DLBCL patients were retrospectively reviewed. Patients were managed with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) regimen or rituximab plus the CHOP (RCHOP) regimen. Results: Lactate dehydrogenase (LDH), ${\beta}2$-microglobulin (${\beta}2$-M), B symptoms, Ann Arbor stage and genetic subtypes were statistically relevant in predicting the prognosis of the overall survival (OS). In the CHOP group, the OS in patients with germinal center B-cell-like (GCB)(76.2%) was significantly higher than that of the non-GCB group (51.9%, P=0.032). With RCHOP management, there was no statistical difference in OS between the GCB (88.4%) and non-GCB groups (81.9%, P=0.288). Conclusion: Elevated LDH and ${\beta}2$-M levels, positive B symptoms, Ann Arbor stage III/IV, and primary nodal lymphoma indicate an unfavorable prognosis of DLBCL patients. Patients with GCB-like DLBCL have a better prognosis than those with non-GCB when treated with the CHOP regimen. The RCHOP treatment with the addition of rituximab can improve the prognosis of patients with DLBCL.

• Annals of Clinical Neurophysiology
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• 제6권1호
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• pp.26-30
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• 2004

Background: Electrophysiologic study accurately predicts the degree of degenerated motor axons but cannot give precise information on the type of injury that occurred in Bell's palsy. Because of these limitation for prognostic prediction in Bell's palsy, we evaluated divergence of electrophysiological time course for the purpose of presuming the type of injury in Bell's palsy. Methods: We did bilateral facial nerve conduction studies in 103 Bell's palsy patients, who visited to Han-Gang sacred heart hospital from 1998 to 2001. We compared the CMAP amplitude of disease site with that of normal site and suggested that decremental CMAP amplitude ratio (percentage) as a degree of denervation of affected facial nerve. Then we demonstrated the time course of denervation percentage. After defining normal range of CMAP amplitude difference from normal control group, we also evaluated if distinct time course of early minimal denervation is present. Results: Our results show that time course of the denervation in early stage of Bell's palsy reflect various injury type such as axonotmesis, neurotmesis or other unidentified type. We cannot identify the distinct time course of early minimal denervation. Conclusions: The time course as well as the maximal value of denervation are the best prognostic guidelines in Bell' s palsy. So repeated serial electrophysiologic test are inevitable to assess prognosis. As an another topic, early minimal denervation for prognostic prediction deserve to be evaluated as a future work up for prognostic prediction.