Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer

Malafaia, Mafalda; Pereira, Tânia; Silva, Francisco; Morgado, Joana; Cunha, António; Oliveira, Hélder

doi:10.1109/embc46164.2021.9629755

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…With that information, zero importance and low-importance features, that do not contribute to cumulative importance of 0.95, are removed from the training set. Applying Gradient Boosting Models for feature selection has proven to be of value in previous literature (28)(29)(30).…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records

et al. 2022

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BackgroundTraumatic Brain Injury (TBI) is one of the leading causes of injury related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences, complicating medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Machine learning (ML) methods can take advantage of the data and guide physicians toward a better prognosis and, consequently, better healthcare. The objective of this study was to develop and test a wide range of machine learning models and evaluate their capability of predicting mortality of TBI, at hospital discharge, while assessing the similarity between the predictive value of the data and clinical significance.MethodsThe used dataset is the Hackathon Pediatric Traumatic Brain Injury (HPTBI) dataset, composed of electronic health records containing clinical annotations and demographic data of 300 patients. Four different classification models were tested, either with or without feature selection. For each combination of the classification model and feature selection method, the area under the receiver operator curve (ROC-AUC), balanced accuracy, precision, and recall were calculated.ResultsMethods based on decision trees perform better when using all features (Random Forest, AUC = 0.86 and XGBoost, AUC = 0.91) but other models require prior feature selection to obtain the best results (k-Nearest Neighbors, AUC = 0.90 and Artificial Neural Networks, AUC = 0.84). Additionally, Random Forest and XGBoost allow assessing the feature's importance, which could give insights for future strategies on the clinical routine.ConclusionPredictive capability depends greatly on the combination of model and feature selection methods used but, overall, ML models showed a very good performance in mortality prediction for TBI. The feature importance results indicate that predictive value is not directly related to clinical significance.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records

et al. 2022

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Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

Kawazoe

Shiinoki

Fujimoto

et al. 2023

J Applied Clin Med Phys

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Purpose We investigated optimal peritumoral size and constructed predictive models for epidermal growth factor receptor (EGFR) mutation. Methods A total of 164 patients with lung adenocarcinoma were retrospectively analyzed. Radiomic signatures for the intratumoral region and combinations of intratumoral and peritumoral regions (3, 5, and 7 mm) from computed tomography images were extracted using analysis of variance and least absolute shrinkage. The optimal peritumoral region was determined by radiomics score (rad‐score). Intratumoral radiomic signatures with clinical features (IRS) were used to construct predictive models for EGFR mutation. Combinations of intratumoral and 3, 5, or 7 mm‐peritumoral signatures with clinical features (IPRS3, IPRS5, and IPRS7, respectively) were also used to construct predictive models. Support vector machine (SVM), logistic regression (LR), and LightGBM models with five‐fold cross‐validation were constructed, and the receiver operating characteristics were evaluated. Area under the curve (AUC) of the training and test cohorts values were calculated. Brier scores (BS) and decision curve analysis (DCA) were used to evaluate the predictive models. Results The AUC values of the SVM, LR, and LightGBM models derived from IRS were 0.783 (95% confidence interval: 0.602–0.956), 0.789 (0.654–0.927), and 0.735 (0.613–0.958) for training, and 0.791 (0.641–0.920), 0.781 (0.538–0.930), and 0.734 (0.538–0.930) for test cohort, respectively. Rad‐score confirmed that the 3 mm‐peritumoral size was optimal (IPRS3), and AUCs values of SVM, LR, and lightGBM models derived from IPRS3 were 0.831 (0.666–0.984), 0.804 (0.622–0.908), and 0.769 (0.628–0.921) for training and 0.765 (0.644–0.921), 0.783 (0.583–0.921), and 0.796 (0.583–0.949) for test cohort, respectively. The BS and DCA of the LR and LightGBM models derived from IPRS3 were better than those from IRS. Conclusion Accordingly, the combination of intratumoral and 3 mm‐peritumoral radiomic signatures may be helpful for predicting EGFR mutations.

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Deep Learning-Based Lung Cancer Detection and Classification

Venkatesh

Kiran

et al. 2023

Information and Communication Technology for Competitive Strategies (ICTCS 2022)

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Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer

Cited by 3 publications

References 8 publications

Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records

Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records

Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma

Deep Learning-Based Lung Cancer Detection and Classification

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