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

Autor: Moharrami M; Faculty of Dentistry, University of Toronto, Toronto, Canada.; Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada.; Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland., Azimian Zavareh P; Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland., Watson E; Faculty of Dentistry, University of Toronto, Toronto, Canada.; Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada., Singhal S; Faculty of Dentistry, University of Toronto, Toronto, Canada.; Chronic Disease and Injury Prevention Department, Health Promotion, Public Health Ontario, Toronto, Canada., Johnson AEW; Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada., Hosni A; Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Canada., Quinonez C; Faculty of Dentistry, University of Toronto, Toronto, Canada.; Schulich School of Medicine & Dentistry, Western University, London, Canada., Glogauer M; Faculty of Dentistry, University of Toronto, Toronto, Canada.; Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada.; Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Canada.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jul 24; Vol. 19 (7), pp. e0307531. Date of Electronic Publication: 2024 Jul 24 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0307531
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Moharrami et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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