Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning models

Han, Shuwen; Xi, Yang; Zhou, Qing; Jing, Zhuang; Wu, Wei

doi:10.1002/cam4.3289

Cited by 18 publications

(10 citation statements)

References 47 publications

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“…In the case of outcomes with imbalanced classes (OS24 and ORR), we computed the class weight that was further included in the model. The main reasons for selecting the CB model among other similar techniques are examples of CB’s successful application in oncological studies ( 20 – 22 ) where it outperformed the other gradient models. The efficacy of models was evaluated and compared using the following performance metrics: confusion matrix, accuracy (ACC), Precision, Recall, F1-score and Area Under the Curve (AUC).…”

Section: Methodsmentioning

confidence: 99%

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

et al. 2023

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IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

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Section: Methodsmentioning

confidence: 99%

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

et al. 2023

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“…CatBoost is an excellent choice for clinical data since categorical features are prevalent in these datasets [ 57 ]. Shuwen et al [ 58 ] used six algorithms, including Logistic Regression, Random Forest, SVM, GBDT, ANNs, and CatBoost, to detect liver metastasis in the early stages of colorectal cancer (CRC). Their analysis demonstrated the optimal performance of the CatBoost model for the early diagnosis of patients with liver metastasis.…”

Section: Methodsmentioning

confidence: 99%

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Safaei

Seyedekrami

et al. 2022

PLoS ONE

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Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients’ survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients’ discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models’ predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

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“…The methods of model construction were as described previously ( 36 ). First, before model construction, the recursive feature elimination (RFE) algorithm based on the sklearn.feature_selection method was applied to feature selection.…”

Section: Methodsmentioning

confidence: 99%

Different Characteristics in Gut Microbiome between Advanced Adenoma Patients and Colorectal Cancer Patients by Metagenomic Analysis

et al. 2022

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Predicting biomarkers from classifier for liver metastasis of colorectal adenocarcinomas using machine learning models

Cited by 18 publications

References 47 publications

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Different Characteristics in Gut Microbiome between Advanced Adenoma Patients and Colorectal Cancer Patients by Metagenomic Analysis

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