Nomogram based on immune-inflammatory indicators and age-adjusted charlson comorbidity index score to predict prognosis of postoperative parotid gland carcinoma patients
- Hao Cheng 1, Jin-Hong Xu 2, Jia-Qi He 3, Chen-Chen Wu 1, Jia-Fan Li 1, Xue-Lian Xu 4
- Hao Cheng 1, Jin-Hong Xu 2, Jia-Qi He 3
- 1Department of Radiotherapy Oncology, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Xinxiang, Henan, 453100, Henan, China.
- 2Department of Otolaryngology, AnYang District Hospital, Anyang, Henan, 455000, China.
- 3Department of Radiotherapy Oncology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, 450000, China.
- 4Department of Radiotherapy Oncology, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Xinxiang, Henan, 453100, Henan, China. xxl17651951833@163.com.
- 0Department of Radiotherapy Oncology, The First Affiliated Hospital of Xinxiang Medical University, 88 Jiankang Road, Xinxiang, Henan, 453100, Henan, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed nomogram models to predict prognosis for parotid gland carcinoma (PGC) patients, incorporating immune-inflammatory-nutrition indicators and age-adjusted Charlson comorbidity index score (ACCI). These models offer individualized prognostic references for better patient follow-up strategies.
Area Of Science
- Oncology
- Surgical Oncology
- Cancer Prognostics
Background
- Parotid gland carcinoma (PGC) is a rare malignancy.
- Identifying reliable prognostic factors is crucial for PGC patient management.
Purpose Of The Study
- To investigate the prognostic role of immune-inflammatory-nutrition indicators and ACCI in PGC.
- To develop and validate nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in PGC patients.
Main Methods
- Retrospective analysis of 344 PGC patients treated with surgical resection.
- Univariate and multivariate Cox regression to identify independent prognostic factors.
- Development and validation of nomogram models for DFS and OS prediction.
Main Results
- AJCC stage, pathology, tumor location, ENE, SII, PNI, ACCI, and GPS were independent prognostic factors for DFS and OS.
- Nomogram models demonstrated good discriminative capability (AUCs > 0.8) and clinical utility.
- The new risk stratification system showed superior discrimination compared to the AJCC stage system.
Conclusions
- Immune-inflammatory-nutrition indicators and ACCI are significant prognostic factors for PGC.
- The developed nomograms provide individualized prognostic references for PGC patients.
- Adjuvant radiotherapy showed no benefit in the low-risk subgroup post-surgery.
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