• Asian Pacific Journal of Cancer Prevention
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• 제15권3호
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• pp.1481-1488
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• 2014

Background: To avoid performing axillary lymph node dissection (ALND) for non-sentinel lymph node (SLN)-negative patients with-SLN positive axilla, nomograms for predicting the status have been developed in many centers. We created a new nomogram predicting non-SLN metastasis in SLN-positive patients with invasive breast cancer and evaluated 14 existing breast cancer models in our patient group. Materials and Methods: Two hundred and thirty seven invasive breast cancer patients with SLN metastases who underwent ALND were included in the study. Based on independent predictive factors for non-SLN metastasis identified by logistic regression analysis, we developed a new nomogram. Receiver operating characteristics (ROC) curves for the models were created and the areas under the curves (AUC) were computed. Results: In a multivariate analysis, tumor size, presence of lymphovascular invasion, extranodal extension of SLN, large size of metastatic SLN, the number of negative SLNs, and multifocality were found to be independent predictive factors for non-SLN metastasis. The AUC was found to be 0.87, and calibration was good for the present Ondokuz Mayis nomogram. Among the 14 validated models, the MSKCC, Stanford, Turkish, MD Anderson, MOU (Masaryk), Ljubljana, and DEU models yielded excellent AUC values of > 0.80. Conclusions: We present a new model to predict the likelihood of non-SLN metastasis. Each clinic should determine and use the most suitable nomogram or should create their own nomograms for the prediction of non- SLN metastasis.

• 응용통계연구
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• 제32권4호
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• pp.619-630
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• 2019

이상지질혈증은 한국인의 대표적인 성인병이며 지속적인 관리가 필요한 만성질환이다. 또한 고혈압이나 당뇨병과 함께 심혈관계 질환의 위험 요인으로 잘 알려져 있다. 하지만 혈관 질환은 검사 없이는 질병 판단을 하기 어려운 것이 현실이다. 본 연구에서는 이상지질혈증의 인지와 예방을 위하여 관련된 위험 요인을 확인한다. 이들을 종합하여 시각화하면서 발병률 예측까지 가능한 통계적 도구 노모그램을 구축하였다. 데이터는 국민건강영양조사 6기, 7기 제1차년도 (2013-2016) 데이터를 사용하였다. 분석 순서로는 먼저 이상지질혈증의 총 12가지 위험 요인을 교차분석을 통해 확인하였다. 그리고 순수 베이지안 분류기를 이용하여 이상지질혈증에 대한 모형으로 노모그램을 구축하였다. 구축한 노모그램은 ROC 곡선과 Calibration plot을 사용하여 신뢰성을 검증하였다. 마지막으로 이전에 제시했던 로지스틱 노모그램과 본 연구에서 제안한 베이지안 노모그램을 비교하였다.

• 대한간호학회지
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• 제51권3호
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• pp.280-293
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• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• Communications for Statistical Applications and Methods
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• 제25권3호
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• pp.297-306
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• 2018

Cardiovascular disease (CVD) is the leading cause of death worldwide and has a high mortality rate after onset; therefore, the CVD management requires the development of treatment plans and the prediction of prevalence rates. In our study, age, income, education level, marriage status, diabetes, and obesity were identified as risk factors for CVD. Using these 6 factors, we proposed a nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model for CVD. The attributes for each factor were assigned point values between -100 and 100 by Bayes' theorem, and the negative or positive attributes for CVD were represented to the values. Additionally, the prevalence rate can be calculated even in cases with some missing attribute values. A receiver operation characteristic (ROC) curve and calibration plot verified the nomogram. Consequently, when the attribute values for these risk factors are known, the prevalence rate for CVD can be predicted using the proposed nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제23권3호
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• pp.276-285
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• 2020

Purpose: Non-alcoholic fatty liver disease (NAFLD) ranges in severity from simple steatosis to steatohepatitis. Early detection of NAFLD is important for preventing the disease from progressing to become an irreversible end-stage liver disease. We developed a nomogram that allows for non-invasive screening for NAFLD in obese children. Methods: Anthropometric and laboratory data of 180 patients from our pediatric obesity clinic were collected. Diagnoses of NAFLD were based on abdominal ultrasonographic findings. The nomogram was constructed using predictors from a multivariate analysis of NAFLD risk factors. Results: The subjects were divided into non-NAFLD (n=67) and NAFLD groups (n=113). Factors, including sex, body mass index, abdominal circumference, blood pressure, insulin resistance, and levels of aspartate aminotransferase, alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γGT), uric acid, triglycerides, and insulin, were significantly different between the two groups (all p<0.05) as determined using homeostatis model assessment of insulin resistance (HOMA-IR). In our multivariate logistic regression analysis, elevated serum ALT, γGT, and triglyceride levels were significantly related to NAFLD development. The nomogram was established using γGT, uric acid, triglycerides, HOMA-IR, and ALT as predictors of NAFLD probability. Conclusion: The newly developed nomogram may help predict NAFLD risk in obese children. The nomogram may also allow for early NAFLD diagnosis without the need for invasive liver biopsy or expensive liver imaging, and may also allow clinicians to intervene early to prevent the progression of NAFLD to become a more advanced liver disease.

• 가정∙방문간호학회지
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• 제30권1호
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• pp.48-58
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• 2023

Purpose: The purpose of this study was to construct a nomogram for predicting difficulty in peripheral intravenous cannulation (DPIVC) for adult inpatients. Methods: This study conducted a secondary analysis of data from the intravenous cannulation cohort by intravenous specialist nurses at a tertiary hospital in Seoul. Overall, 504 patients were included; of these, 166 (32.9%) patients with failed cannulation in the first intravenous cannulation attempt were included in the case group, while the remaining 338 patients were included in the control group. The nomogram was built with the identified risk factors using a multiple logistic regression analysis. The model performance was analyzed using the Hosmer-Lemeshow test, area under the curve (AUC), and calibration plot. Results: Five factors, including vein diameter, vein visibility, chronic kidney disease, diabetes, and chemotherapy, were risk factors of DPIVC. The nomogram showed good discrimination with an AUC of 0.81 (95% confidence interval: 0.80-0.82) by the sample data and 0.79 (95% confidence interval: 0.74-0.84) by bootstrapping validation. The Hosmer-Lemeshow goodness-of-fit test showed a p-value of 0.694, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of DPIVC. Conclusion: This nomogram can be used in clinical practice by nurses to predict DPIVC probability. Future studies are required, including those on factors possibly affecting intravenous cannulation.

• 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.

• Radiation Oncology Journal
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• 제36권2호
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• pp.139-146
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• 2018

Purpose: IBTR! 2.0 nomogram is web-based nomogram that predicts ipsilateral breast tumor recurrence (IBTR). We aimed to validate the IBTR! 2.0 using an external data set. Materials and Methods: The cohort consisted of 2,206 patients, who received breast conserving surgery and radiation therapy from 1992 to 2012 at our institution, where wide surgical excision is been routinely performed. Discrimination and calibration were used for assessing model performance. Patients with predicted 10-year IBTR risk based on an IBTR! 2.0 nomogram score of <3%, 3%-5%, 5%-10%, and >10% were assigned to groups 1, 2, 3, and 4, respectively. We also plotted calibration values to observe the actual IBTR rate against the nomogram-derived 10-year IBTR probabilities. Results: The median follow-up period was 73 months (range, 6 to 277 months). The area under the receiver operating characteristic curve was 0.607, showing poor accordance between the estimated and observed recurrence rate. Calibration plot confirmed that the IBTR! 2.0 nomogram predicted the 10-year IBTR risk higher than the observed IBTR rates in all groups. High discrepancies between nomogram IBTR predictions and observed IBTR rates were observed in overall risk groups. Compared with the original development dataset, our patients had fewer high grade tumors, less margin positivity, and less lymphovascular invasion, and more use of modern systemic therapies. Conclusions: IBTR! 2.0 nomogram seems to have the moderate discriminative ability with a tendency to over-estimating risk rate. Continued efforts are needed to ensure external applicability of published nomograms by validating the program using an external patient population.

• Asian Pacific Journal of Cancer Prevention
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• 제16권9호
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• pp.3773-3777
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• 2015

Background: This study aimed to establish a nomogram by combining clinicopathologic factors with overall survival of stage IA-IIB cervical cancer patients after complete resection with pelvic lymphadenectomy. Materials and Methods: This nomogram was based on a retrospective study on 1,563 stage IA-IIB cervical cancer patients who underwent complete resection and lymphadenectomy from 2002 to 2008. The nomogram was constructed based on multivariate analysis using Cox proportional hazard regression. The accuracy and discriminative ability of the nomogram were measured by concordance index (C-index) and calibration curve. Results: Multivariate analysis identified lymph node metastasis (LNM), lymph-vascular space invasion (LVSI), stromal invasion, parametrial invasion, tumor diameter and histology as independent prognostic factors associated with cervical cancer survival. These factors were selected for construction of the nomogram. The C-index of the nomogram was 0.71 (95% CI, 0.65 to 0.77), and calibration of the nomogram showed good agreement between the 5-year predicted survival and the actual observation. Conclusions: We developed a nomogram predicting 5-year overall survival of surgically treated stage IA-IIB cervical cancer patients. More comprehensive information that is provided by this nomogram could provide further insight into personalized therapy selection.

• Journal of Breast Disease
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• 제6권2호
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• pp.52-59
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• 2018

Purpose: This study aimed to determine whether clinicopathological factors are potentially associated with successful breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NAC) and develop a nomogram for predicting successful BCS candidates, focusing on those who are diagnosed with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative tumors during the pre-NAC period. Methods: The training cohort included 239 patients with an HR-positive, HER2-negative tumor (${\geq}3cm$), and all of these patients had received NAC. Patients were excluded if they met any of the following criteria: diffuse, suspicious, malignant microcalcification (extent >4 cm); multicentric or multifocal breast cancer; inflammatory breast cancer; distant metastases at the time of diagnosis; excisional biopsy prior to NAC; and bilateral breast cancer. Multivariate logistic regression analysis was conducted to evaluate the possible predictors of BCS eligibility after NAC, and the regression model was used to develop the predicting nomogram. This nomogram was built using the training cohort (n=239) and was later validated with an independent validation cohort (n=123). Results: Small tumor size (p<0.001) at initial diagnosis, long distance from the nipple (p=0.002), high body mass index (p=0.001), and weak positivity for progesterone receptor (p=0.037) were found to be four independent predictors of an increased probability of BCS after NAC; further, these variables were used as covariates in developing the nomogram. For the training and validation cohorts, the areas under the receiver operating characteristic curve were 0.833 and 0.786, respectively; these values demonstrate the potential predictive power of this nomogram. Conclusion: This study established a new nomogram to predict successful BCS in patients with HR-positive, HER2-negative breast cancer. Given that chemotherapy is an option with unreliable outcomes for this subtype, this nomogram may be used to select patients for NAC followed by successful BCS.