Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

Huang, Shigao; Yang, Jie; Fong, Simon; Zhao, Qi

doi:10.3390/cancers11081140

Cited by 29 publications

(17 citation statements)

References 33 publications

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“…[ 5 ] Furthermore, there was a significant interaction between sex and PS within ES-SCLC, suggesting that PS was highly prognostic in males, with no significant impact on females. Our previous study [ 6 – 8 ] tried to find several strong prognostic factors to establish a prognostic scoring system to predict survival. Sculier et al [ 9 ] established a Recursive Partitioning Analysis (RPA) grading system by screening out 4 prognostic factors including TNM staging, PS, age, and gender through the analysis of cases from the international staging database of the International Association for the Study of Lung Cancer.…”

Section: Discussionmentioning

confidence: 99%

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

Gao

Dang

et al. 2020

Medicine

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This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program. A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort. In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort. The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

Gao

Dang

et al. 2020

Medicine

Self Cite

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“…It is difficult to correlate the features manually and predict the outcome of a patient in terms of tumor staging or DFS period. Therefore, we used different ML classifiers such as Random Forest, Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptrons (MLP), K-Nearest Neighbors (KNN), and Adaptive Boosting (AdaBoost) [22][23][24]28], which are popularly used in medical data analysis. Implementing different classifiers in Scikit-learn for the multi-class classification, the scheme of one-against-one was used for the SVM and the scheme OvR was used for Logistic Regression (LR) and other models, which gave the average of all metrics used in our analysis.…”

Section: Machine Learning Analysismentioning

confidence: 99%

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

Gupta

Chiang

Sahoo

et al. 2019

Cancers

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The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.

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“…One of the novel techniques that have been proposed has been that which seeks to store big data via data warehouse environment-based image implementation [11]. Here, data warehouse systems in the healthcare environment have been used.…”

Section: Resultsmentioning

confidence: 99%

Review on Predicting Disease Severity – Learning Algorithms as Classifiers for Data Warehouse Environments

SakshiHooda¹

2021

ITII

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When research fields such as biological and medical arenas are considered, computer science-based applications have been utilized on an increasing basis. In the process, data has accumulated in significant amounts. In particular, most of the big data that has resulted has accrued from experimental and medical results that are reported. The eventuality is that in the healthcare platforms, data mining techniques have been employed towards timely predictions of conditions with which patients present, a process achieved through data analysis. Also, the data analyses have strived to discern correlations among disease-related variables or parameters in otherwise voluminous information. It is also notable that data mining has received increasing adoption in the healthcare field because of the perceived ability to provide room for informed decision-making, reliable treatment decisions, and the detection of potential fraudulence when it comes to health care service payments. While these interplays point to a promising trend regarding the utilization of data mining and disease severity prediction, the eventual bid data has proved to be so complex that the use of traditional methods of data processing and analyzing is deemed less effective. In addition, given data warehouse, the use of traditional methods towards data extraction makes it difficult to discern some of the hidden intersections, pointing to the importance of a new approach for data classification and disease severity prediction. The aim of this review paper was to demonstrate how the use of classifiers in the form of machine learning algorithms could be utilized in the prediction of the severity of diseases. Indeed, the context of the research entailed warehouse environments. To have an ideal kernel established towards data classification (that would then enable additional diagnostic tests), some of the machine learning algorithms that were investigated included SVM (support vector machine) and ANN (artificial neural network). From the review outcomes, it was established that when machine learning algorithms are used to obtain trend similarities in disease severity through predictive analytics, similar severity magnitudes of the given diseases could be projected in terms of their probabilities of and trends in occurrence. Also, the review outcomes demonstrated that disease severity prediction through machine learning algorithms is an insightful practice because it allows for the prediction of patients’ conditions, as well as the prevision of room for optimal decision-making regarding treatment modalities to be adopted by healthcare firms.

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Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

Cited by 29 publications

References 33 publications

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

Review on Predicting Disease Severity – Learning Algorithms as Classifiers for Data Warehouse Environments

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