A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models

Hindocha, Sumeet; Charlton, Thomas; Linton-Reid, Kristofer; Hunter, Benjamin; Chan, Charleen; Ahmed, Merina; Robinson, Emily; Orton, Matthew; Ahmad, Shahreen; McDonald, Fiona; Locke, Imogen; Power, Danielle; Blackledge, Matthew; Lee, Richard W.; Aboagye, Eric O.

doi:10.1016/j.ebiom.2022.103911

Cited by 31 publications

(19 citation statements)

References 27 publications

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“…The AUC values of these models have been shown to depend primarily on how much information is incorporated into the model, what type of information is used, and what specific outcomes are investigated. Our AUC values are consistent with the recent studies that investigated the recurrence of prostate, non-small cell lung, colorectal, and biliary cancers, with reported AUC ranging from 0.581 to 0.894 [26][27][28][29][30][31] . The incorporation of more potentially predictive features tends to improve the performance of models.…”

Section: Discussionsupporting

confidence: 90%

Prediction of early-stage melanoma recurrence using clinical and histopathologic features

Wan

Nguyen²,

Liu³

et al. 2022

npj Precis. Onc.

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Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.

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Section: Discussionsupporting

confidence: 90%

Prediction of early-stage melanoma recurrence using clinical and histopathologic features

Wan

Nguyen²,

Liu³

et al. 2022

npj Precis. Onc.

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“…Through human assumptions, this approach may limit the choice of input features and result in biasing. Selecting a large set of input features and using ML models to select those that perform the best may mitigate the issue to some extent ( 30 ). Some of these may not correlate with those deemed to be most important by clinical professionals and may highlight features that were previously not considered.…”

Section: Discussionmentioning

confidence: 99%

“…The ML algorithms we evaluated included support vector machine (SVM), generalized boosted regression modeling (GBRM), k-nearest neighbors (KNN), naive Bayes (NB), RF and extreme gradient boosting machine (XGB). These feature selection methods and ML algorithms were common methods, which were introduced in previous studies ( 27 , 30 ). A total of 36 ML models were developed using the six modeling algorithms and six feature selection techniques for predicting LNM.…”

Section: Methodsmentioning

confidence: 99%

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

Zhang²,

Yan³

et al. 2022

Front. Oncol.

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BackgroundFor patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.MethodsPatients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model’s generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied.ResultsOf the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701).ConclusionsML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

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“…The ML algorithm may also help clinicians select appropriate candidates for immune checkpoint inhibitors 7 . In terms of risk stratification, an ML model that derived 34 features predicted recurrence and overall survival (OS) more accurately compared to the TNM staging system 8 …”

Section: Introductionmentioning

confidence: 99%

“…7 In terms of risk stratification, an ML model that derived 34 features predicted recurrence and overall survival (OS) more accurately compared to the TNM staging system. 8 This study aimed to evaluate the performance of several ML algorithms in predicting the outcome of afatinib treatment in patients with advanced EGFR-mutated NSCLC in South Korea, using data collected from multiple medical centers.…”

mentioning

confidence: 99%

Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

2022

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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. K E Y W O R D S machine learning, NSCLC, outcome, survival BACKGROUND Lung cancer is one of the leading causes of death, accounting for approximately 23.3% of all cancer-related deaths in

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A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models

Cited by 31 publications

References 27 publications

Prediction of early-stage melanoma recurrence using clinical and histopathologic features

Prediction of early-stage melanoma recurrence using clinical and histopathologic features

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma

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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