A 80-gene set potentially predicts the relapse in laryngeal carcinoma optimized by support vector machine

A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years

OBJECTIVE

Currently, there are few practical tools for predicting the prognosis of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model and a convenient online prediction platform to predict whether LSCC patients will survive 5 years after diagnosis, providing a reference for further evaluation of patient prognosis.

METHODS

This is a retrospective study based on data collected from two centers. Center 1 included 117 LSCC patients with survival prognosis data, and center 2 included 33 patients, totaling 150 patients. All data were divided into independent training sets (60 %) and testing sets (40 %). Eight machine learning (ML) algorithms were used to establish models with 11 clinical parameters as input features. The accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) of the testing set were used to evaluate the models, and the best model was selected. The model was then developed into a website-based 5-year survival status prediction platform for LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to conduct interpretability analysis on the parameters of the model.

RESULTS

The LSCC 5-year survival status prediction model using the support vector machine (SVM) algorithm achieved the best results, with accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 81.2 % respectively. The online platform for predicting the 5-year survival status of LSCC based on this model was successfully established. The SHAP analysis shows that the clinical stage is the most important feature of the model.

CONCLUSION

This study successfully established a ML model and a practical online prediction platform to predict the survival status of laryngeal cancer patients after 5 years, which may help clinicians to better evaluate the prognosis of LSCC.

LEVEL OF EVIDENCE: 3

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

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.

A roadmap of six different pathways to improve survival in laryngeal cancer patients

PURPOSE OF REVIEW

Laryngeal cancer continues to require improvement in earlier stage diagnosis and better imaging delineation of disease, and hence 'more evidence-based' selection of treatment, as recent evidence suggests that related mortality, in the last decades, has not significantly decreased worldwide. Even though the reasons are not fully understood, there persists an urgency for a review and development of future strategies to embrace such clinical and diagnostic challenges from a political, societal, as well as scientific and clinical points of view.

RECENT FINDINGS

This review of the published literature suggests that survival improvement in laryngeal cancer may be achieved by fuelling and combining at least some or all of six targeted agendas: documentation of disease global incidence and national burden monitoring; development and implementation of high-quality cancer registries; education on risk factors and hazardous habits associated with laryngeal cancer for the general population; active modification of proven at-risk population lifestyles; centralization of treatment; and use of machine learning of gathered 'big data' and their integration into approaches for the optimization of prevention and treatments strategies.

SUMMARY

Laryngeal cancer should be tackled on several fronts, commencing with disease monitoring and prevention, up to treatment optimisation. Available modern resources offer the possibility to generate significant advances in laryngeal cancer management. However, each nation needs to develop a comprehensive approach, which is an essential prerequisite to obtain meaningful improvement on results.

Machine learning and its potential applications to the genomic study of head and neck cancer-A systematic review

BACKGROUND

Machine learning (ML) is powerful tool that can identify and classify patterns from large quantities of cancer genomic data that may lead to the discovery of new biomarkers, new drug targets, and a better understanding of important cancer genes. The aim of this systematic review was to evaluate the existing literature and assess the application of machine learning of genomic data in head and neck cancer (HNC).

MATERIALS AND METHODS

The addressed focused question was "Does machine learning of genomic data play a role in prognostic prediction of HNC?" PubMed, EMBASE, Scopus, Web of Science, and gray literature from January 1990 up to and including May 2018 were searched. Two independent reviewers performed the study selection according to eligibility criteria.

RESULTS

A total of seven studies that met the eligibility criteria were included. The majority of studies were cohort studies, one a case-control study and one a randomized controlled trial. Two studies each evaluated oral cancer and laryngeal cancer, while other one study each evaluated nasopharyngeal cancer and oropharyngeal cancer. The majority of studies employed support vector machine (SVM) as a ML technique. Among the included studies, the accuracy rates for ML techniques ranged from 56.7% to 99.4%.

CONCLUSION

Our findings showed that ML techniques for the analysis of genomic data can play a role in the prognostic prediction of HNC.