scholarly journals Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

Liver Cancer ◽  
2021 ◽  
pp. 1-11
Author(s):  
I-Cheng Lee ◽  
Jo-Yu Huang ◽  
Ting-Chun Chen ◽  
Chia-Heng Yen ◽  
Nai-Chi Chiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Surgical Resection ◽  
Clinical Features ◽  
Prediction Models ◽  
Early Recurrence ◽  
Evolutionary Learning ◽  
Training Set ◽  
Test Set ◽  
Better Than

<b><i>Background and Aims:</i></b> Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection. <b><i>Methods:</i></b> Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (<i>n</i> = 362) and a test set (<i>n</i> = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery. <b><i>Results:</i></b> A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, <i>p</i> &#x3c; 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, <i>p</i> &#x3c; 0.001 vs. GARSL postoperative) models using clinical features only. <b><i>Conclusion:</i></b> The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

scholarly journals Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 332
Author(s):  
Ernest Kwame Ampomah ◽  
Zhiguang Qin ◽  
Gabriel Nyame
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Superior Performance ◽  
Operating Characteristics ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Ensemble Machine Learning ◽  
Better Than

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

2021 ◽  
pp. 1-12
Author(s):  
Zongqiong Sun ◽  
Linfang Jin ◽  
Shuai Zhang ◽  
Shaofeng Duan ◽  
Wei Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Clinical Features ◽  
Calibration Curve ◽  
Prediction Models ◽  
Ct Images ◽  
Clinical Findings ◽  
Training Set ◽  
Prediction Probability ◽  
Radiomics Signature ◽  
Testing Set

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p <  0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p >  0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

scholarly journals Ezrin expression is associated with hepatocellular carcinoma possibly derived from progenitor cells and early recurrence after surgical resection

2008 ◽  
Vol 21 (7) ◽  
pp. 847-855 ◽  
Author(s):  
Daiki Okamura ◽  
Masayuki Ohtsuka ◽  
Fumio Kimura ◽  
Hiroaki Shimizu ◽  
Hiroyuki Yoshidome ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Progenitor Cells ◽  
Surgical Resection ◽  
Early Recurrence ◽  
Ezrin Expression

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Early Recurrence ◽  
Prediction Performance ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Combined Model ◽  
Test Set ◽  
Pathological Model ◽  
Radiomics Signature

AbstractTo investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

scholarly journals Exploratory analysis on prediction of loan privilege for customers using random forest

2018 ◽  
Vol 7 (2.21) ◽  
pp. 339 ◽  
Author(s):  
K Ulaga Priya ◽  
S Pushpa ◽  
K Kalaivani ◽  
A Sartiha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Data Model ◽  
Model Evaluation ◽  
Banking Industry ◽  
Performance Parameters ◽  
Training Set ◽  
Test Set ◽  
Learning Technique ◽  
Analytical Processing

In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.  

scholarly journals Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

2020 ◽  
Vol 9 (12) ◽  
pp. 732
Author(s):  
Hongjie Yu ◽  
Lin Liu ◽  
Bo Yang ◽  
Minxuan Lan
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Prediction Models ◽  
Crime Data ◽  
Crime Prediction ◽  
Movement Data ◽  
Environment Variables ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Better Than

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

The comparisons of the outcomes between surgical resection and proton beam therapy for single primary hepatocellular carcinoma.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 482-482
Author(s):  
Shunsuke Tamura ◽  
Yukiyasu Okamura ◽  
Teiichi Sugiura ◽  
Takaaki Ito ◽  
Yusuke Yamamoto ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Proton Beam ◽  
Surgical Resection ◽  
Performance Status ◽  
Proton Beam Therapy ◽  
Primary Hepatocellular Carcinoma ◽  
Free Survival ◽  
Significant Difference ◽  
Clinical Trial Information ◽  
Better Than

482 Background: There are many treatment choices for hepatocellular carcinoma (HCC). Proton beam therapy (PBT) is considered a treatment option for HCC. The purpose of this study was to compare surgical resection (SR) and PBT in order to clarify the prognostic factors for operable HCC based on a single institution’s database. Methods: Patients with single primary nodular HCC ≤ 100 mm without vessel invasion on pretreatment imaging were divided into the SR group and PBT group. In the PBT group, the patients with unresectable HCC due to their liver function and/or performance status (PS) were excluded. Results: There were 314 and 31 patients who underwent SR and PBT, respectively. The median survival time in the SR group was significantly better than in the PBT group (104.1 vs. 64.6 months, p = 0.008). Regarding the relapse-free survival (RFS), there was no significant difference between the SR and PBT groups (33.8 vs. 14.0 months, p = 0.099). Conclusions: In RFS, the PBT group and the SR group were comparable. However, the PBT group was significantly worse than SR group in overall survival. SR may therefore be favorable as an initial treatment for HCC compared to PBT. Clinical trial information: 1856.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

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