Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features

2018 ◽  
Vol 45 (11) ◽  
pp. 5116-5128 ◽  
Author(s):  
Mazen Soufi ◽  
Hidetaka Arimura ◽  
Noriyuki Nagami
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Wavelet Decomposition ◽  
Survival Prediction ◽  
Lung Cancer Patients ◽  
Mother Wavelets

A nomogram to predict outcomes of lung cancer patients after pneumonectomy based on 47 indicators.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20014-e20014
Author(s):  
Bo Cheng ◽  
Cong Wang ◽  
Xue Meng
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Clinical Care ◽  
External Validation ◽  
Categorical Variables ◽  
Rank Test ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Lung Cancer Patients

e20014 Background: Nomograms are commonly used tools to estimate prognosis in oncology and medicine.We aimed to establish a nomogram with patients’ characteristics and all available hematological biomarkers for lung cancer patients. Methods: All indexes were cataloged according to clinical significance. Principle component analysis (PCA) was used to reduce the dimensions. Each component was transformed into categorical variables based on recognized cut-off values from receiver operating characteristic (ROC) curve. Kaplan-Meier analysis with log-rank test was used to evaluate the prognostic value of each component. Multivariate analysis was used to determine the promising prognostic biomarkers. Five components were entered into a predictive nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort from Shandong Cancer Hospital. The predictive accuracy and discriminative ability were measured by concordance index (C index) and risk group stratification. Results: Two hundred thirty-six patients were retrospectively analyzed in this study, with 134 in the Discovery Group and 102 in the Validation Group. Forty-seven indexes were sorted into 8 subgroups, and 20 principle components were extracted for further survival analysis. Via cox regression analysis, five components were significant and entered into predictive nomograms. The calibration curves for probability of 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The new scoring system according to nomogram allowed significant distinction between survival curves within respective tumor-node-metastasis (TNM) subgroups. Conclusions: A nomogram based on the clinical indexes was established for survival prediction of lung cancer patients, which can be used for treatment therapy selection and clinical care option. PCA makes big data analysis feasible.

scholarly journals Survival Prediction Model Using Clinico-Pathologic Characteristics for Nonsmall Cell Lung Cancer Patients After Curative Resection

Medicine ◽  
2015 ◽  
Vol 94 (45) ◽  
pp. e2013 ◽  
Author(s):  
Ching-Yang Wu ◽  
Jui-Ying Fu ◽  
Ching-Feng Wu ◽  
Ming-Ju Hsieh ◽  
Yun-Hen Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Curative Resection ◽  
Nonsmall Cell Lung Cancer ◽  
Survival Prediction ◽  
Lung Cancer Patients

scholarly journals Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images

2017 ◽  
Vol 123 (3) ◽  
pp. 363-369 ◽  
Author(s):  
Janna E. van Timmeren ◽  
Ralph T.H. Leijenaar ◽  
Wouter van Elmpt ◽  
Bart Reymen ◽  
Cary Oberije ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Cone Beam Ct ◽  
Small Cell ◽  
Cone Beam ◽  
Survival Prediction ◽  
Small Cell Lung ◽  
Lung Cancer Patients

scholarly journals Survival prediction for lung cancer patients by integrating clinical and molecular features using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Survival Prediction ◽  
Lung Cancer Patients ◽  
Molecular Features

The paper presents a model for survival prediction of lung cancer patients

scholarly journals Survival prediction for lung cancer patients by integrating clinical and molecular features using machine learning

2020 ◽  
Author(s):  
Rizwan Qureshi
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Survival Prediction ◽  
Lung Cancer Patients ◽  
Molecular Features

The paper presents a model for survival prediction of lung cancer patients

Development of an artificial intelligence model to predict survival at specific time intervals for lung cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6556-6556 ◽  
Author(s):  
Smita Agrawal ◽  
Vivek Vaidya ◽  
Prajwal Chandrashekaraiah ◽  
Hemant Kulkarni ◽  
Li Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Prediction Models ◽  
Parameter Tuning ◽  
Machine Learning Techniques ◽  
Survival Models ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Lung Cancer Patients ◽  
Time Points

6556 Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data for training, 25% for hyper-parameter tuning, 20% for testing and 15% for holdout validation. Death date available in the Electronic Health Record was cross checked by linkage to death registries. Results: The model was validated on the holdout set of 8,468 patients. The Area Under the Curve (AUC) for the model was 0.79. The precision and recall for predicting survival beyond the three time points were between 0.7-0.8 and 0.8-0.9 respectively (see table). This compares favourably to other lung cancer survival models created using different machine learning techniques (Jochems 2017, Dekker 2009). A Cox-PH model created using the top 20 variables also had a significantly lower performance (see table). Analysis of input variables yielded distinctive patterns for patient subgroups and time points. Tumor status, medications, lab values and functional status were found to be significant in patient sub cohorts. Conclusions: An AI model to predict survival of lung cancer patients built using a large real world dataset yielded high accuracy. This general model can further be used to predict survival of sub cohorts stratified by variables such as stage or various treatment effects. Such a model could be useful for assessing patient risk and treatment options, evaluating cost and quality of care or determining clinical trial eligibility. [Table: see text]

scholarly journals Image-Based Survival Prediction for Lung Cancer Patients Using CNNS

2019 ◽  
Author(s):  
Christoph Haarburger ◽  
Philippe Weitz ◽  
Oliver Rippel ◽  
Dorit Merhof
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Survival Prediction ◽  
Lung Cancer Patients
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