Nomogram model based on tumor-infiltrating lymphocytes and clinical characteristics to predict prognosis of patients with laryngeal squamous cell carcinoma

Head and neck carcinomas are the sixth most common cancers worldwide, with laryngeal squamous cell carcinoma (LSCC) being the second most prevalent subtype. Improving survival outcomes in LSCC patients remains a critical clinical challenge. This retrospective study aimed to develop a nomogram model integrating tumor-infiltrating lymphocytes (TILs) and clinicopathological characteristics to predict the prognosis of LSCC patients. The nomogram model was constructed using Cox and Lasso regression analyses and was subsequently evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized for model validation and to further elucidate the role of TILs and immune responses in LSCC. This study cohort included LSCC patients diagnosed by pathological examination between 2011 and 2014 at Xiangya Hospital and Harbin Medical University Cancer Hospital. A total of 412 patients were assigned to the training cohort and 140 patients to the test cohort for validation. The final nomogram model integrated TNM stage, TILs, PLR, BMI, age, differentiation and NLR. The area under the curve (AUC) was 0.745, indicating strong calibration and clinical utility. Kaplan-Meier survival curves demonstrated significant discrimination. TILs were positively correlated with immune cell abundance and the expression of immune-related genes. In conclusion, the nomogram model based on TILs and clinicopathological features effectively predicts the prognosis of LSCC patients.

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Publication Details

Journal
American Journal of Cancer Research
Published
2025-01-01
DOI
https://doi.org/10.62347/mkfi3976
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Nomogram model based on tumor-infiltrating lymphocytes and clinical characteristics to predict prognosis of patients with laryngeal squamous cell carcinoma

Xiaojuan Zhou
American Journal of Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Nomogram model based on tumor-infiltrating lymphocytes and clinical characteristics to predict prognosis of patients with laryngeal squamous cell carcinoma

Xiaojuan Zhou
article en

Abstract

Head and neck carcinomas are the sixth most common cancers worldwide, with laryngeal squamous cell carcinoma (LSCC) being the second most prevalent subtype. Improving survival outcomes in LSCC patients remains a critical clinical challenge. This retrospective study aimed to develop a nomogram model integrating tumor-infiltrating lymphocytes (TILs) and clinicopathological characteristics to predict the prognosis of LSCC patients. The nomogram model was constructed using Cox and Lasso regression analyses and was subsequently evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized for model validation and to further elucidate the role of TILs and immune responses in LSCC. This study cohort included LSCC patients diagnosed by pathological examination between 2011 and 2014 at Xiangya Hospital and Harbin Medical University Cancer Hospital. A total of 412 patients were assigned to the training cohort and 140 patients to the test cohort for validation. The final nomogram model integrated TNM stage, TILs, PLR, BMI, age, differentiation and NLR. The area under the curve (AUC) was 0.745, indicating strong calibration and clinical utility. Kaplan-Meier survival curves demonstrated significant discrimination. TILs were positively correlated with immune cell abundance and the expression of immune-related genes. In conclusion, the nomogram model based on TILs and clinicopathological features effectively predicts the prognosis of LSCC patients.

American Journal of Cancer ResearchVol. 15(3)
Central South University (CN), Third Xiangya Hospital (CN)
Openalex Percentile: Top 91%
Radiomics and Machine Learning in Medical Imaging
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