Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model

Lu, Tzu-Pin; Kuo, Kuan‐Ting; Chen, Ching-Hsuan; Chang, Ming; Lin, Hsiu-Ping; Hu, Yunxia; Chiang, Ying‐Cheng; Cheng, Wen‐Fang; Chen, Chi-An

doi:10.3390/cancers11020270

Cited by 24 publications

(18 citation statements)

References 32 publications

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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%

“…In recent years, the use of machine learning has gained much importance in the field of clinical study [14,15]. Recently, many researchers and clinicians have proposed the use of machine learning in the field of colorectal cancer [16][17][18] and other types of cancers [19][20][21][22][23][24]. For instance, in [25] authors used ML for prostate cancer screening and have determined the impact of few variables such as rate of change of prostate-specific antigen (PSA), age, BMI, and race on the model's accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Machine Learning Analysismentioning

confidence: 99%

Section: Introductionmentioning

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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“…Some machine learning algorithms are able to process large amounts of data with cloud computing. Moreover, many recent studies28,29 have indicated that machine learning can predict early biochemical recurrence after robot-assisted prostatectomy. Additionally, a prognostic genome for patients with epithelial ovarian cancer has been developed through a machine learning model.…”

Section: Discussionmentioning

confidence: 99%

<p>Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes</p>

Tong

et al. 2019

OTT

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ObjectiveTo use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.Methods1570 stage I-III breast cancer patients receiving treatment from Sun Yat-sen Memorial Hospital were analyzed. Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.ResultsThe results showed that platelets, LMR (lymphocyte-to-monocyte ratio), age, PLR (the platelet-to-lymphocyte ratio) and white blood cell counts accounted for a significant weight in the 5-year prognosis of triple-negative breast cancer patients. The results of model prediction indicated that rankings for accuracy among the training group (from high to low) were forest, gbm, and DecisionTree (0.770335, 0.760766, 0.751994, 0.737640 and 0.734450, respectively). For AUC value (high to low), they were forest, Logistic and DecisionTree (0.896673, 0.895408, 0.776836, 0.722799 and 0.702804, respectively). The highest MSE value for DecisionTree was 0.2656, and the lowest MSE value for forest was 0.2297. In the test group, accuracy rankings (from high to low) were DecisionTree, and GradientBoosting (0.748408, 0.738854, 0.738854, 0.732484 and gbm, respectively). For AUC value (high to low), the rankings were GradientBoosting, gbm, and DecisionTree (0.731595, 0.715438, 0.712767, 0.708348 and 0.691960, respectively). The maximum MSE value for gbm was 0.2707, and the minimum MSE value for DecisionTree was 0.2516.ConclusionThe machine learning algorithm can predict the death outcomes of patients with triple-negative breast cancer 5 years after discharge. This can be used to estimate individual outcomes for patients with triple-negative breast cancer.

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“…It is forecasted that big data and bioinformatic technology will stay prevalent in the coming year [5,6,7,8,9]. Artificial intelligence (AI) has been used to diagnose and classify cancer for more than two decades.…”

Section: Introductionmentioning

confidence: 99%

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

Huang¹,

Yang

Fong

et al. 2019

Cancers

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This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient’s prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%, and 1.83% higher than that of the traditional statistical method, sequential feature selection (SFS), mutual information with particle swarm optimization(MIPSO), and mutual information with sequential feature selection (MISFS), respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.

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Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model

Cited by 24 publications

References 32 publications

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

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

<p>Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes</p>

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

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