Machine Learning–Guided Adjuvant Treatment of Head and Neck Cancer

Howard, Frederick M.; Kochanny, Sara; Koshy, Matthew; Spiotto, Michael T.; Pearson, Alexander T.

doi:10.1001/jamanetworkopen.2020.25881

Cited by 105 publications

(81 citation statements)

References 36 publications

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“…This is not surprising since deep neural network models are often advantageous when sufficient amount of training data available. DeepSurv model is typical successfully applied when there are over thousands of records available for model training [ 16 , 23 , 24 , 25 ]. The C-index we obtained using DeepSurv is also close to the result in [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

“…DeepSurv model is typical successfully applied when there are over thousands of records available for model training [ 16 , 23 , 24 , 25 ]. The C-index we obtained using DeepSurv is also close to the result in [ 23 ]. Another common strategy for predicting clinical outcome is to treat the prediction of survival at given times points as classification problems.…”

Section: Resultsmentioning

confidence: 99%

“…In another study, Howard et al investigated the usage of machine learning in the guidance of the adjuvant treatment of head and neck cancers. While other models had better performance on personalized treatment recommendations, RSF showed best performance in predicting the OS with a C-index of 0.695 [ 23 ]. This observation is consistent with our results using clinical and demographic features alone (0.703).…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Jun

Farrugia

et al. 2021

Cancers

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Prognostication for cancer patients is integral for patient counseling and treatment planning, yet providing accurate prediction can be challenging using existing patient-specific clinical indicators and host factors. In this work, we evaluated common machine learning models in predicting head and neck squamous cell carcinoma (HNSCC) patients’ overall survival based on demographic, clinical features and host factors. We found random survival forest had best performance among the models evaluated, which achieved a C-index of 0.729 and AUROC of 0.792 in predicting two-year overall survival. In addition, we verified that host factors are independently predictive of HNSCC overall survival, which improved the C-index by a margin of 0.026 and the AUROC by 0.034. Due to the strong correlation among host factors, we showed that proper dimension reduction is an important step before their incorporation into the machine learning models, which provides a host factor score reflecting the patients’ nutrition and inflammation status. The score by itself showed excellent discriminating capacity with the high-risk group having a hazard ratio of 3.76 (1.93–7.32, p < 0.0001) over the low-risk group. The hazard ratios were further improved to 7.41 (3.66–14.98, p < 0.0001) by the random survival forest model after including demographic and clinical features.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Jun

Farrugia

et al. 2021

Cancers

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“…Overall, 12 of the included studies considered oncological outcomes following curative-intent treatment as their target of prediction. In details, six studies ( 40 , 42 , 78 – 81 ) aimed at predicting OS, while five ( 40 , 82 – 85 ) considered loco-regional control (LRC) and one ( 86 ) distant metastasis-free survival (DMFS). Only two works focused on more than one oncological outcomes ( 40 , 87 ).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist

et al. 2021

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Background and PurposeMachine learning (ML) is emerging as a feasible approach to optimize patients’ care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).Materials and MethodsElectronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.ResultsForty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).Discussion and ConclusionThe range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.

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“…Overall, 12 of the included studies considered oncological outcomes following curative-intent treatment as their target of prediction. In details, six studies (40,42,(78)(79)(80)(81) aimed at predicting OS, while five (40, 82-85) considered loco-regional control (LRC) and one (86) distant metastasis-free survival (DMFS). Only two works focused on more than one oncological outcomes (40,87).…”

Section: Oncological Outcome Predictionmentioning

confidence: 99%

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Machine Learning–Guided Adjuvant Treatment of Head and Neck Cancer

Cited by 105 publications

References 36 publications

Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist

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