Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer
Background The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata. Methods Data that wer...
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| Published in | Thoracic cancer Vol. 13; no. 23; pp. 3353 - 3361 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Melbourne
John Wiley & Sons Australia, Ltd
01.12.2022
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1759-7706 1759-7714 1759-7714 |
| DOI | 10.1111/1759-7714.14694 |
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| Abstract | Background
The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata.
Methods
Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan–Meier (KM) curve was used to visualize the identified strata obtained from the ML models.
Results
No significant differences in the input variables were observed between the training and test datasets. The best‐performing models were support vector machine in predicting 1‐year afatinib continuation (AUC 0.626) and decision tree in 2‐year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log‐rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time‐on‐treatment (TOT) and overall survival (OS).
Conclusion
The performances of ML models in our study found no discernible roles in predicting afatinib‐related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record‐based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance.
Machine learning methods used in this study did not offer any advantage in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation. Despite poor performance, ML algorithms successfully classified the strata that showed significant differences in survival outcomes, which were assessed using time‐on‐treatment and overall survival. The application of machine learning using routine electronic medical record‐based variables may be cost inefficient. Further studies using machine learning techniques to predict the outcome of non‐small‐cell lung cancer would benefit from including various clinical, histopathological, and genetic predictors. |
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| AbstractList | BackgroundThe present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata.MethodsData that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan–Meier (KM) curve was used to visualize the identified strata obtained from the ML models.ResultsNo significant differences in the input variables were observed between the training and test datasets. The best‐performing models were support vector machine in predicting 1‐year afatinib continuation (AUC 0.626) and decision tree in 2‐year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log‐rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time‐on‐treatment (TOT) and overall survival (OS).ConclusionThe performances of ML models in our study found no discernible roles in predicting afatinib‐related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record‐based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1-year afatinib continuation and 2-year survival after afatinib initiation and to identify the differences in survival outcomes between ML-classified strata.BACKGROUNDThe present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1-year afatinib continuation and 2-year survival after afatinib initiation and to identify the differences in survival outcomes between ML-classified strata.Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan-Meier (KM) curve was used to visualize the identified strata obtained from the ML models.METHODSData that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan-Meier (KM) curve was used to visualize the identified strata obtained from the ML models.No significant differences in the input variables were observed between the training and test datasets. The best-performing models were support vector machine in predicting 1-year afatinib continuation (AUC 0.626) and decision tree in 2-year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log-rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time-on-treatment (TOT) and overall survival (OS).RESULTSNo significant differences in the input variables were observed between the training and test datasets. The best-performing models were support vector machine in predicting 1-year afatinib continuation (AUC 0.626) and decision tree in 2-year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log-rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time-on-treatment (TOT) and overall survival (OS).The performances of ML models in our study found no discernible roles in predicting afatinib-related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record-based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance.CONCLUSIONThe performances of ML models in our study found no discernible roles in predicting afatinib-related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record-based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. Abstract Background The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata. Methods Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan–Meier (KM) curve was used to visualize the identified strata obtained from the ML models. Results No significant differences in the input variables were observed between the training and test datasets. The best‐performing models were support vector machine in predicting 1‐year afatinib continuation (AUC 0.626) and decision tree in 2‐year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log‐rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time‐on‐treatment (TOT) and overall survival (OS). Conclusion The performances of ML models in our study found no discernible roles in predicting afatinib‐related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record‐based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. Background The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata. Methods Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan–Meier (KM) curve was used to visualize the identified strata obtained from the ML models. Results No significant differences in the input variables were observed between the training and test datasets. The best‐performing models were support vector machine in predicting 1‐year afatinib continuation (AUC 0.626) and decision tree in 2‐year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log‐rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time‐on‐treatment (TOT) and overall survival (OS). Conclusion The performances of ML models in our study found no discernible roles in predicting afatinib‐related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record‐based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. Machine learning methods used in this study did not offer any advantage in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation. Despite poor performance, ML algorithms successfully classified the strata that showed significant differences in survival outcomes, which were assessed using time‐on‐treatment and overall survival. The application of machine learning using routine electronic medical record‐based variables may be cost inefficient. Further studies using machine learning techniques to predict the outcome of non‐small‐cell lung cancer would benefit from including various clinical, histopathological, and genetic predictors. Machine learning methods used in this study did not offer any advantage in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation. Despite poor performance, ML algorithms successfully classified the strata that showed significant differences in survival outcomes, which were assessed using time‐on‐treatment and overall survival. The application of machine learning using routine electronic medical record‐based variables may be cost inefficient. Further studies using machine learning techniques to predict the outcome of non‐small‐cell lung cancer would benefit from including various clinical, histopathological, and genetic predictors. The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1-year afatinib continuation and 2-year survival after afatinib initiation and to identify the differences in survival outcomes between ML-classified strata. Data that were also used in the RESET study were retrospectively collected from 16 hospitals in South Korea. A stratified random sampling method was applied to split the data into training and test sets (70:30 split ratio). Clinical information, such as age, sex, tumor stage, smoking, performance status, metastasis, type of metastasis, dose adjustment, and pathologic information on EGFR mutations were inputted. Training was performed using eight ML algorithms: logistic regression, decision tree, deep neural network, random forest, support vector machine, boosting, bagging, and the naïve Bayes classifier. The model performance was assessed based on sensitivity, specificity, and accuracy. Area under the receiver operator characteristic curve (AUC) was calculated and compared between the ML models using DeLong's test. A Kaplan-Meier (KM) curve was used to visualize the identified strata obtained from the ML models. No significant differences in the input variables were observed between the training and test datasets. The best-performing models were support vector machine in predicting 1-year afatinib continuation (AUC 0.626) and decision tree in 2-year survival after afatinib start (AUC 0.644), although the performances of the ML models were comparable and did not display any predictive roles. KM analysis and log-rank test revealed significant differences between the strata identified from the ML model (p < 0.001) in terms of both time-on-treatment (TOT) and overall survival (OS). The performances of ML models in our study found no discernible roles in predicting afatinib-related outcomes, although the identified strata revealed different TOT and OS in the KM analysis. This implies the strength of ML in predicting the survival outcome, as well as the limitation of electronic medical record-based variables in ML algorithms. Careful consideration of variable inclusion is likely to improve the general model performance. |
| Author | Lee, Sang Jin Jang, Tae‐Won Kim, Taeyun |
| AuthorAffiliation | 2 Department of Statistics Pusan National University Busan Republic of Korea 1 Division of Pulmonology, Department of Internal Medicine The Armed Forces Goyang Hospital Goyang Republic of Korea 3 Division of Pulmonology, Department of Internal Medicine Kosin University College of Medicine, Kosin University Gospel Hospital Busan Republic of Korea |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36278315$$D View this record in MEDLINE/PubMed |
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| Copyright | 2022 The Authors. published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. 2022 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. 2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and... The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1-year afatinib continuation and 2-year survival... BackgroundThe present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year... Machine learning methods used in this study did not offer any advantage in predicting 1‐year afatinib continuation and 2‐year survival after afatinib... Abstract Background The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation... |
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| SubjectTerms | Afatinib - pharmacology Afatinib - therapeutic use Algorithms Bayes Theorem Carcinoma, Non-Small-Cell Lung - drug therapy Carcinoma, Non-Small-Cell Lung - genetics Carcinoma, Non-Small-Cell Lung - pathology Classification Datasets Drug dosages ErbB Receptors - genetics ErbB Receptors - therapeutic use Generalized linear models Humans Lung cancer Lung Neoplasms - drug therapy Lung Neoplasms - genetics Lung Neoplasms - pathology Machine Learning Medical prognosis Metastasis Neural networks NSCLC Original outcome Performance evaluation Retrospective Studies Support vector machines survival Treatment Outcome |
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| Title | Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer |
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