Feature selection and predicting chemotherapy-induced ulcerative mucositis using machine learning methods

Satheeshkumar, P.S.; El-Dallal, Mohammed; Mohan, Minu P.

doi:10.1016/j.ijmedinf.2021.104563

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…Although Logistic Regression offers greater interpretability, machine learning models, particularly XGBoost, excel in predictive accuracy, aligning with the growing emphasis on machine learning for complication prediction in medical research. Studies like Poolakkad et al [ 15 ], who achieved a higher AUC using gradient boosting for predicting mucositis post-chemotherapy, and Smith et al [ 16 ], who demonstrated superior performance using neural networks for post-radiation xerostomia, support this trend. Dean et al’s use of penalized logistic regression, SVM, and random forest to predict dysphagia with strong external validation further validates the efficacy of advanced models [16].…”

Section: Discussionmentioning

confidence: 89%

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning

Lee,

Liu,

Chang

et al. 2024

Radiat Oncol

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Purpose This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models. Materials and methods Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson’s correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning. Results Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD. Conclusion The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

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Section: Discussionmentioning

confidence: 89%

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning

Lee,

Liu,

Chang

et al. 2024

Radiat Oncol

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“…After screening the title and abstracts 5,489 records were excluded. From the remaining 71 records, 39 articles were excluded in the full-text reading stage: 15 articles worked on non-clinicopathological data [ 19 – 30 ], five articles were about preventive outcomes including early diagnosis and malignancy transformation [ 31 – 35 ], five articles were on pre-treatment lymph node metastasis [ 36 – 40 ], four articles were about the use of ML in treatment planning and delivery [ 41 – 44 ], three articles used traditional stochastic modeling [ 45 – 47 ], one article was not about HNC [ 48 ], one article validated an already available tool and did not develop a model [ 49 ], one article was about toxicity [ 50 ], and three articles were on non-SCC cancer [ 51 – 54 ]. After manually screening the reference lists, two other studies were added, and the final 34 included studies were processed for data extraction [ 55 – 88 ].…”

Section: Resultsmentioning

confidence: 99%

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami,

Azimian Zavareh,

Watson

et al. 2024

PLoS ONE

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Background This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. Methods A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70–0.79 vs. 0.66–0.76, and for all sub-sites including oral cavity (0.73–0.89 vs. 0.69–0.77) and larynx (0.71–0.85 vs. 0.57–0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75–0.97, with an F1-score of 0.65–0.89 for HNC; AUROC of 0.61–0.91 and F1-score of 0.58–0.86 for the oral cavity; and AUROC of 0.76–0.97 and F1-score of 0.63–0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. Conclusions ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

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“…Poolakkad and his colleagues established a machine learning (ML) model of 253 H&N patients’ clinical data with the best AUC of 0.79 for AOM prediction [ 42 ]. Most clinical data selected in their study were late after the CRT scheme, for example, the anti-neoplastic chemotherapy-induced pancytopenia, co-morbidity score, and agranulocytosis.…”

Section: Discussionmentioning

confidence: 99%

Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy

Dong

Zhang

Lam

et al. 2023

Cancers

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(1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction.

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Feature selection and predicting chemotherapy-induced ulcerative mucositis using machine learning methods

Cited by 14 publications

References 39 publications

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy

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