A nomogram for predicting severe myelosuppression in small cell lung cancer patients following the first-line chemotherapy.
A nomogram for predicting severe myelosuppression in small cell lung cancer patients following the first-line chemotherapy.
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DOI：
10.1038/s41598-023-42725-7
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发表时间：
2023-10-14
影响因子：
4.6
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中科院分区：
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本研究旨在建立和验证一个诺模图来预测小细胞肺癌（SCLC）患者一线化疗后发生严重骨髓抑制的概率。共筛选出179例SCLC患者作为训练组，另外124例患者用于验证组。在多变量逻辑回归分析中，采用最小赤池信息准则（AIC）确定预测因子，得到新的诺模图。在训练组和验证组中验证诺模图，并通过受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估预测值。提取年龄和肿瘤分期作为预测因素，建立诺模图，训练组和验证组的AUC值分别为0.725和0.727。如果训练组和验证组DCA中的阈值概率范围在15 - 60%之间，则该列线图在两组中显示出可接受的校准曲线，并且其预测比所有治疗方案和无治疗方案增加更多的净获益。因此，诺模图客观准确地预测了一线化疗后SCLC患者严重骨髓抑制的发生。
This study aimed at establishing and validating a nomogram to predict the probability of severe myelosuppression in small cell lung cancer (SCLC) patients following the first-line chemotherapy. A total of 179 SCLC cases were screened as the training group and another 124 patients were used for the validation group. Predictors were determined by the smallest Akaike’s information criterion (AIC) in multivariate logistic regression analysis, leading to a new nomogram. The nomogram was validated in both training and validation groups and the predicting value was evaluated by area under the receiver operating characteristics (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Age and tumor staging were extracted as predictors to establish a nomogram, which displayed the AUC values as 0.725 and 0.727 in the training and validation groups, respectively. This nomogram exhibited acceptable calibration curves in the two groups and its prediction added more net benefits than the treat-all scheme and treat-none scheme if the range of threshold probability in the DCA was between 15 and 60% in the training and validation groups. Therefore, the nomogram objectively and accurately predict the occurrence of severe myelosuppression in SCLC patients following the first-line chemotherapy.
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