Nutritional status and its correlation to prognosis of nasopharyngeal carcinoma patients in different ages in China: a multicenter cohort study

Jia, Pingping; Wu, Xiaoxiao; Shen, Fangqi; Xu, Guangzhong; Xu, Hongxia; Cong, Minghua; Song, Chunhua; Shi, Hanping

doi:10.1007/s00520-023-08104-8

Support Care Cancer

2023

DOI: 10.1007/s00520-023-08104-8

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Nutritional status and its correlation to prognosis of nasopharyngeal carcinoma patients in different ages in China: a multicenter cohort study

Pingping Jia,

Xiaoxiao Wu,

Fangqi Shen

et al.

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2024

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Cited by 3 publications

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Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

Zuo,

Ma,

Yan

et al. 2024

Head & Neck

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BackgroundEarly detection of high‐risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning‐derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression‐free survival (PFS) in patients with non‐metastatic NPC.MethodsA cohort of 653 patients with non‐metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models—random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)—to predict progression‐free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS.ResultsThe RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low‐risk individuals showing better PFS than high‐risk individuals (p < 0.001).ConclusionMLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non‐metastatic NPC.

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Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

Zuo,

Ma,

Yan

et al. 2024

Head & Neck

View full text Add to dashboard Cite

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Selection of induction chemotherapy cycles for stage N3 nasopharyngeal carcinoma based on pre-treatment plasma EBV DNA

Weng,

Cai,

et al. 2024

Sci Rep

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NRS2002 score as a prognostic factor in solid tumors treated with immune checkpoint inhibitor therapy: a real-world evidence analysis

Tang,

Li,

Huang

et al. 2024

Cancer Biology & Therapy

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Nutritional status and its correlation to prognosis of nasopharyngeal carcinoma patients in different ages in China: a multicenter cohort study

Cited by 3 publications

References 29 publications

Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

Machine learning‐derived prognostic signature for progression‐free survival in non‐metastatic nasopharyngeal carcinoma

Selection of induction chemotherapy cycles for stage N3 nasopharyngeal carcinoma based on pre-treatment plasma EBV DNA

NRS2002 score as a prognostic factor in solid tumors treated with immune checkpoint inhibitor therapy: a real-world evidence analysis

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