Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Abstract

Objective This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). Data Sources Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. Review Methods A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. Conclusions AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. Implications for Practice AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

“…For example, as a predictive modeling and early detection, AI could be used to analyze data from a variety of sources, such as electronic health records, genetic information, and environmental data, to predict an individual’s risk of developing cancer and to tailor prevention strategies accordingly [ 13 , 14 , 15 , 16 ]. AI-related applications may reduce screening costs [ 17 ], provide more reliable diagnostics [ 13 , 18 , 19 , 20 ], improve prognostics [ 13 , 19 , 21 , 22 , 23 , 24 , 25 ], and aid in the discovery of new drugs [ 14 , 15 ]. Several areas of cancer care are expected to benefit from AI-related applications, including cancer radiology and clinical oncology [ 10 ].…”

Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features

“…[12][13][14][15] The application of ML in medical practice can potentially revolutionize patient care and therapeutic decisions by individualizing the treatment according to specific riskfactors. 16,17 Recently the applications of AI in LC have encompassed a variety of fields, including radiomics, genomics, acoustics, and videomics to support screening, diagnosis, decision making, and oncological outcome.…”

“…13,14 As regards management and the outcomes (risk of recurrence, possibility of distant metastases, therapeutic choice) the use of artificial intelligence is still in its initial stages. 17 This pilot study aims to evaluate the performance of a ML algorithm in predicting 1-and 3-year OS in a cohort of patient surgical treated for LC. Moreover, the role and the impact of different adverse features will be investigated with a decisional tree.…”

“…The collaboration between biomedicals and bioinformatics has led to develop the Artificial Intelligence (AI) algorithms, which have consistently improved the cancer's analysis compared with conventional statistical tools 9–11 . Machine learning (ML) is a branch of AI that deals with predictions in unknown situations from previous observations that have been “learnt.” 12–15 The application of ML in medical practice can potentially revolutionize patient care and therapeutic decisions by individualizing the treatment according to specific risk‐factors 16,17 …”

“…As regards management and the outcomes (risk of recurrence, possibility of distant metastases, therapeutic choice) the use of artificial intelligence is still in its initial stages 17 …”