Development and Validation of a Pathomics Model Using Machine Learning to Predict  Expression and Prognosis in Head and Neck Cancer

Weihua Wang; Suyu Ruan; Yuhang Xie; Shengjian Fang; Junxian Yang; Xueyan Li; Yu Zhang

doi:10.21053/ceo.2023.00026

Clinical and Experimental Otorhinolaryngology (Feb 2024)

Development and Validation of a Pathomics Model Using Machine Learning to Predict Expression and Prognosis in Head and Neck Cancer

Weihua Wang,
Suyu Ruan,
Yuhang Xie,
Shengjian Fang,
Junxian Yang,
Xueyan Li,
Yu Zhang

Affiliations

Weihua Wang: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Suyu Ruan: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Yuhang Xie: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Shengjian Fang: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Junxian Yang: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Xueyan Li: Department of Nursing, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
Yu Zhang: Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

DOI: https://doi.org/10.21053/ceo.2023.00026
Journal volume & issue: Vol. 17, no. 1
pp. 85 – 97

Abstract

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Objectives. The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. Methods. Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin–stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm. Results. Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model’s high clinical applicability. Conclusion. The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.

Published in Clinical and Experimental Otorhinolaryngology

ISSN: 1976-8710 (Print); 2005-0720 (Online)
Publisher: Korean Society of Otorhinolaryngology-Head and Neck Surgery
Country of publisher: Korea, Republic of
LCC subjects: Medicine: Otorhinolaryngology
Website: https://www.e-ceo.org/

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