scholarly journals Construction of a survival prediction model for high-and low -grade UTUC after tumor resection based on “SEER database”: a multicenter study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mengmeng Wang ◽  
Xin Ren ◽  
Ge Wang ◽  
Xiaomin Sun ◽  
Shifeng Tang ◽  
...  
Keyword(s):  
Tumor Resection ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prediction ◽  
Low Grade ◽  
Seer Database ◽  
Discriminative Ability ◽  
Internal Validation ◽  
The Difference ◽  
Good Calibration

Abstract Background There are differences in survival between high-and low-grade Upper Tract Urothelial Carcinoma (UTUC). Our study aimed to develop a nomogram to predict overall survival (OS) of patients with high- and low-grade UTUC after tumor resection, and to explore the difference between high- and low-grade patients. Methods Patients confirmed to have UTUC between 2004 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. The UTUCs were identified and classified as high- and low-grade, and 1-, 3- and 5-year nomograms were established. The nomogram was then validated using the Chinese multicenter dataset (patients diagnosed in Shandong, China between January 2010 and October 2020). Results In the high-grade UTUC patients, nine important factors related to survival after tumor resection were identified to construct nomogram. The C index of training dataset was 0.740 (95% confidence interval [CI]: 0.727–0.754), showing good calibration. The C index of internal validation dataset was 0.729(95% CI:0.707–0.750). On the other hand, Two independent predictors were identified to construct nomogram of low-grade UTUC. The C index was 0.714 (95% CI: 0.671–0.758) for the training set,0.731(95% CI:0.670–0.791) for the internal validation dataset. Encouragingly, the nomogram was clinically useful and had a good discriminative ability to identify patients at high risk. Conclusion We constructed a nomogram and a corresponding risk classification system predicting the OS of patients with an initial diagnosis of high-and low-grade UTUC.

scholarly journals Construction of a Survival Prediction Model for High-and Low-Grade Upper Tract Urothelial Carcinoma After Tumor Resection Based on "SEER Database": A Retrospective and Multicenter Study

2021 ◽  
Author(s):  
Changgang Sun ◽  
Mengmeng Wang ◽  
Xin Ren ◽  
Ge Wang ◽  
Xiaomin Sun ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Tumor Resection ◽  
Upper Tract Urothelial Carcinoma ◽  
Validation Dataset ◽  
Survival Prediction ◽  
Low Grade ◽  
Seer Database ◽  
Discriminative Ability ◽  
Internal Validation ◽  
Upper Tract

Abstract Background: There are difffferences in survival between high-and low-grade Upper Tract Urothelial Carcinoma (UTUC). Our study aimed to develop a nomogram to predict overall survival (OS) of patients with high- and low- grade UTUC after tumor resection, and to explore the difffference between high-and low-grade patients. Methods: Patients confifirmed to have UTUC between 2004 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. The UTUCs were identifified and classifified as high- and low-grade, and 1-, 3-and 5-year nomograms were established. The nomogram was then validated using the Chinese multicenter dataset (patients diagnosed in Shandong, China between January 2010 and October 2020). Findings: In the high-grade UTUC patients, nine important factors related to survival after tumor resection were identifified to construct nomogram. The ability of the model to distinguish between UTUC grades was verifified using two datasets (internal validation dataset, C index(95% CI):0.729[0.707-0.754];Chinese multicenter validation dataset: C index(95% CI):0.763[0.656-0.869]).On the other hand, Two independent predictors were identifified to construct nomogram of low-grade UTUC. The C index was 0.714 (95% CI: 0.671-0.758) for the training set,0.731(95% CI:0.670-0.791) for the internal validation dataset, and 0.825 (95% CI:0.689-1.00) for the Chinese multicenter dataset. Encouragingly, the nomogram was clinically useful and had a good discriminative ability to identify patients at high risk. Interpretation: We constructed a nomogram and a corresponding risk classifification system predicting the OS of patients with an initial diagnosis of high-and low-grade UTUC.

scholarly journals DIALYSIS START TIMING: DEVELOPMENT AND VALIDATION OF START SCORING SCALE

2018 ◽  
Vol 20 (2) ◽  
pp. 47-60 ◽  
Author(s):  
A. Yu. Zemchenkov ◽  
R. P. Gerasimchuk ◽  
A. B. Sabodash ◽  
K. A. Vishnevskii ◽  
G. A. Zemchenkov ◽  
...  
Keyword(s):  
Decision Making ◽  
Mortality Risk ◽  
Mortality Prediction ◽  
Validation Dataset ◽  
Optimal Time ◽  
Survival Prediction ◽  
Diabetes Status ◽  
The Difference ◽  
Validation Set ◽  
And Gender

Aim.The optimal time for initiating of chronic dialysis remains unknown. The scale for mortality risk assessment could help in decision-making concerning dialysis start timing.Methods.We randomly divided 1856 patients started dialysis in 2009–2016 into developmental and validation group (1:1) to create and validate scoring system «START» predicting mortality risk at dialysis initiation in order to fi nd unmodifi able and modifi able factors which could help in the decision-making of dialysis start. In the series of univariate regression models in the developmental set, we evaluated the mortality risk linked with available parameters: age, eGFR, serum phosphate, total calcium, hemoglobin, Charlson comorbidity index, diabetes status, urgency of start (turned to be signifi cant) and gender, serum sodium, potassium, blood pressure (without impact on survival). Similar hazard ratios were converted to score points.Results.The START score was highly predictive of death: C-statistic was 0.82 (95% CI 0.79–0.85) for the developmental dataset and 0.79 (95% CI 0.74–0.84) for validation dataset (both p < 0.001). On applying the cutoff between 7–8 points in the developmental dataset, the risk score was highly sensitive 81.1% and specifi c 67.9%; for validation dataset, the sensitivity was 78.9%, specifi city 67.9%. We confi rmed the similarity in survival prediction in the validation set to developmental set in low, medium and high START score groups. The difference in survival between three levels of START-score in validation set remained similar to that of developmental set: Wilcoxon = 8.78 (p = 0.02) vs 15.31 (p < 0.001) comparing low–medium levels and 25.18 (p < 0.001) vs 39.21 (p < 0.001) comparing medium–high levels.Conclusion.Developed START score system including modifi able factors showed good mortality prediction and could be used in dialysis start decision-making. 

Surgery for insular low-grade glioma: predictors of postoperative seizure outcome

2014 ◽  
Vol 120 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Tamara Ius ◽  
Giada Pauletto ◽  
Miriam Isola ◽  
Giorgia Gregoraci ◽  
Riccardo Budai ◽  
...  
Keyword(s):  
Seizure Control ◽  
Tumor Resection ◽  
Low Grade Glioma ◽  
Seizure Outcome ◽  
Low Grade ◽  
Class Iii ◽  
The Difference ◽  
Drug Resistant Epilepsy ◽  
Postoperative Seizure ◽  
The Impact

Object Although a number of recent studies on the surgical treatment of insular low-grade glioma (LGG) have demonstrated that aggressive resection leads to increased overall patient survival and decreased malignant progression, less attention has been given to the results with respect to tumor-related epilepsy. The aim of this investigation was to evaluate the impact of volumetric, histological, and intraoperative neurophysiological factors on seizure outcome in patients with insular LGG. Methods The authors evaluated predictors of seizure outcome with special emphasis on both the extent of tumor resection (EOR) and the tumor's infiltrative pattern quantified by computing the difference between the preoperative T2- and T1-weighted MR images (ΔVT2T1) in 52 patients with preoperative drug-resistant epilepsy. Results The 12-month postoperative seizure outcome (Engel class) was as follows: seizure free (Class I), 67.31%; rare seizures (Class II), 7.69%; meaningful seizure improvement (Class III), 15.38%; and no improvement or worsening (Class IV), 9.62%. Poor seizure control was more common in patients with a longer preoperative seizure history (p < 0.002) and higher frequency of seizures (p = 0.008). Better seizure control was achieved in cases with EOR ≥ 90% (p < 0.001) and ΔVT2T1 < 30 cm3 (p < 0.001). In the final model, ΔVT2T1 proved to be the strongest independent predictor of seizure outcome in insular LGG patients (p < 0.0001). Conclusions No or little postoperative seizure improvement occurs mainly in cases with a prevalent infiltrative tumor growth pattern, expressed by high ΔVT2T1 values, which consequently reflects a smaller EOR.

P–673 A visualized clinical model predicting cumulative pregnancy rate after IVF-ET: a real-world study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
M Zhang
Keyword(s):  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Single Center ◽  
Threshold Probability ◽  
Internal Validation ◽  
Female Age ◽  
Clinical Model ◽  
Temporal Validation ◽  
Ivf Et

Abstract Study question How is the cumulative pregnancy probability of individual patients after IVF-ET,could we develop a visualized clinical model to predict it based on patient’s characteristics? Summary answer The visualized clinical mode incorporates five items of female age, number of oocytes, antral follicle count, endometrium thickness and basal FSH level. What is known already Many factors can result in infertility, prognosis prediction is clinically relevant for making the right therapeutic strategy while avoiding overtreatment. It is also helpful in counselling, making the patient aware of possible treatment duration and estimated expense and managing patient’s expectation. Visualized clinical mode and accurate prediction would also be helpful in designing clinical trials to evaluate new treatments. Study design, size, duration We conducted a retrospective analysis of a single-center database using prospectively collected data from women who underwent IVF/ICSI treatment from January 2013 to December 2015, All the participants were followed up for at least 2 years, 3538 IVF-ET cycles were included in the study.A total of 3538 IVF/ICSI cycles were included in the study. Participants/materials, setting, methods Data from a total of 2312 IVF/ICSI cycles from January 2013 to December 2014 were randomly split into training dataset (1550, 67%) and internal validation dataset (762, 33%). A total of 1226 IVF/ICSI cycles in 2015 was applied to external validation dataset (temporal validation) Main results and the role of chance Multivariable logistic regression model combined with restricted cubic splines function was used to test independent prognostic factors and estimate their effects on treatment outcome for patients treated with IVF/ICSI. Female age, number of oocytes retrieved, AFC, endometrium thickness and basal FSH were included the final model. The above model was used to calculate prediction scores for all women in the training and validation datasets. The C-index was 0.693 (95% CI: 0.692∼0.695) in training sets, 0.689 in internal validation sets and 0.710 in external validation sets, which denotes a good performance. Calibration curves suggest excellent model calibration, with an ideal agreement between the prediction and actual observation . The DCA showed that if the threshold probability is between 0 and 0.7, using the nomogram derived in the present study to predict cumulative pregnancy provided a greater benefit than either thetreat-all or the treat-none strategy. Limitations, reasons for caution it was a retrospective, single-center study.In the future, prospective, randomized controlled, multicenter clinical studies will be designed. Wider implications of the findings: The visualized nomogram model provides great predictive value for infertility patients in their first IVF/ICSI cycle, and predicts the pregnancy probability of individuals ,and could help clinicians improving clinical counselling. Trial registration number Not applicable

scholarly journals Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiangyu Tan ◽  
Kexin Li ◽  
Jiucheng Zhang ◽  
Wenzhe Wang ◽  
Bian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Cancer Screening ◽  
Convolutional Neural Network ◽  
Cervical Cancer Screening ◽  
Incidence Rates ◽  
Training Dataset ◽  
Validation Dataset ◽  
Low Grade ◽  
Medical Resources

Abstract Background The incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer. Methods ThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system. Results The sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases. Conclusions In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.

scholarly journals Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaoyun Cheng ◽  
Jinzhang Li ◽  
Tianming Xu ◽  
Kemin Li ◽  
Jingnan Li
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Medical Center ◽  
External Validation ◽  
Staging System ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Seer Database ◽  
Internal Validation ◽  
Survival Status

Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning.Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset.Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83).Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.

scholarly journals An online diabetic retinopathy screening tool for patients with type 2 diabetes

2021 ◽  
Vol 14 (11) ◽  
pp. 1748-1755
Author(s):  
Wan-Yue Li ◽  
◽  
Ya-Nan Song ◽  
Ling Luo ◽  
Chuang Nie ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Screening Tool ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Internal Validation ◽  
Diabetic Retinopathy Screening

AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

scholarly journals Simple-to-use nomogram for predicting the risk of syphilis among MSM in Guangdong Province: results from a serial cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Peizhen Zhao ◽  
Ziying Yang ◽  
Baohui Li ◽  
Mingzhou Xiong ◽  
Ye Zhang ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Multivariate Logistic Regression Analysis ◽  
Guangdong Province ◽  
Assessment Model ◽  
Training Dataset ◽  
Validation Dataset ◽  
Syphilis Infection ◽  
Discriminative Ability ◽  
Cross Sectional ◽  
The Past

Abstract Background The purpose of this study was to develop and validate a simple-to-use nomogram for the prediction of syphilis infection among men who have sex with men (MSM) in Guangdong Province. Methods A serial cross-sectional data of 2184 MSM from 2017 to 2019 was used to develop and validate the nomogram risk assessment model. The eligible MSM were randomly assigned to the training and validation dataset. Factors included in the nomogram were determined by multivariate logistic regression analysis based on the training dataset. The receiver operating characteristic (ROC) curves was used to assess its predictive accuracy and discriminative ability. Results A total of 2184 MSM were recruited in this study. The prevalence of syphilis was 18.1% (396/2184). Multivariate logistic analysis found that age, the main venue used to find sexual partners, condom use in the past 6 months, commercial sex in the past 6 months, infection with sexually transmitted diseases (STD) in the past year were associated with syphilis infection using the training dataset. All these factors were included in the nomogram model that was well calibrated. The C-index was 0.80 (95% CI 0.76–0.84) in the training dataset, and 0.79 (95% CI 0.75–0.84) in the validation dataset. Conclusions A simple-to-use nomogram for predicting the risk of syphilis has been developed and validated among MSM in Guangdong Province. The proposed nomogram shows good assessment performance.

Dynamic Scintigraphy of the Oesophagus in the Evaluation of Reflux Oesophagitis

1990 ◽  
Vol 29 (05) ◽  
pp. 204-209
Author(s):  
B. Ugarković ◽  
D. Ivančević ◽  
D. Babić ◽  
Ž. Babić
Keyword(s):  
Reflux Oesophagitis ◽  
Oesophageal Reflux ◽  
Low Grade ◽  
Time Activity ◽  
The Difference ◽  
Asymptomatic Subjects ◽  
Oesophageal Transit ◽  
Dynamic Scintigraphy ◽  
Positive Results ◽  
Reflux Œsophagitis

A method is presented which combines gastro-oesophageal reflux quantification and oesophageal transit measurement so as to differentiate true reflux from residual oesophageal activity. A group of 33 subjects with gastro-oesophageal reflux symptoms and endoscopically confirmed reflux oesophagitis and a group of 21 asymptomatic subjects with normal oesophageal, gastric and duodenal endoscopic findings were examined. The subjects were given 37 MBq 99mTc-Sn-colloid in saline orally and then scintiscanned dynamically. The gastro-oesophageal quantification was done after transit measurement and after the oesophageal time activity (to detect residual oesophageal activity) reached its minimum. The difference in the reflux indices between the two groups was highly significant. In low-grade oesophagitis measured reflux was lower than in higher grades of disease. Only 4.7% false-positive results were observed with a specificity of 95%, indicating that this method may be superior to methods published earlier.

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

Sign in / Sign up

Export Citation Format

Share Document