Journal of cancer research and clinical oncology · 2023
A nomogram for distinguishing benign and malignant parotid gland tumors using clinical data and preoperative blood markers: development and validation.
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Zhao Feng, Huang Xiaoying, He Junkun, Li Jiangmiao, Li Qiyun, Wei Fangyu, Chen Huiying, Su Jiping
Аннотация
PURPOSE: This study aimed to construct and validate a nomogram that incorporated clinical data and preoperative blood markers to differentiate BPGTs from MPGTs more efficiently and at low cost.
METHODS: We retrospectively analyzed patients who underwent parotidectomy and histopathological diagnosis at the First Affiliated Hospital of Guangxi Medical University from January 2013 to June 2022. Subjects were randomly divided into training and validation sets with a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select the most relevant features from 19 variables and built a nomogram using logistic regression. We evaluated the model's performance using receiver-operating characteristic (ROC) curves, calibration curves, clinical decision curve analysis (DCA), and clinical impact curve analysis (CICA).
RESULTS: The final sample consisted of 644 patients, of whom 108 (16.77%) had MPGTs. The nomogram included four features: current smoking status, pain/tenderness, peripheral facial paralysis, and lymphocyte-to-monocyte ratio (LMR). The optimal cut-off value for the nomogram was 0.17. The areas under the ROC curves (AUCs) of the nomogram were 0.748 (95% confidence interval [CI] = 0.689-0.807) and 0.754 (95% CI = 0.636-0.872) in the training and validation sets, respectively. The nomogram also showed good calibration, high accuracy, moderate sensitivity, and acceptable specificity in both sets. The DCA and CICA demonstrated that the nomogram had significant net benefits for a wide range of threshold probabilities (0.06-0.88 for the training set; 0.06-0.57 and 0.73-0.95 for the validation set).
CONCLUSION: The nomogram based on clinical characteristics and preoperative blood markers was a reliable tool for discriminating BPGTs from MPGTs preoperatively.
METHODS: We retrospectively analyzed patients who underwent parotidectomy and histopathological diagnosis at the First Affiliated Hospital of Guangxi Medical University from January 2013 to June 2022. Subjects were randomly divided into training and validation sets with a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select the most relevant features from 19 variables and built a nomogram using logistic regression. We evaluated the model's performance using receiver-operating characteristic (ROC) curves, calibration curves, clinical decision curve analysis (DCA), and clinical impact curve analysis (CICA).
RESULTS: The final sample consisted of 644 patients, of whom 108 (16.77%) had MPGTs. The nomogram included four features: current smoking status, pain/tenderness, peripheral facial paralysis, and lymphocyte-to-monocyte ratio (LMR). The optimal cut-off value for the nomogram was 0.17. The areas under the ROC curves (AUCs) of the nomogram were 0.748 (95% confidence interval [CI] = 0.689-0.807) and 0.754 (95% CI = 0.636-0.872) in the training and validation sets, respectively. The nomogram also showed good calibration, high accuracy, moderate sensitivity, and acceptable specificity in both sets. The DCA and CICA demonstrated that the nomogram had significant net benefits for a wide range of threshold probabilities (0.06-0.88 for the training set; 0.06-0.57 and 0.73-0.95 for the validation set).
CONCLUSION: The nomogram based on clinical characteristics and preoperative blood markers was a reliable tool for discriminating BPGTs from MPGTs preoperatively.