| 葛旭东,崔书君,田 龙.基于剂量表面积直方图的前列腺癌放疗致放射性直肠炎Nomogram预测模型研究[J].肿瘤学杂志,2025,31(3):238-243. |
| 基于剂量表面积直方图的前列腺癌放疗致放射性直肠炎Nomogram预测模型研究 |
| A Nomogram Prediction Model for Radiation Proctitis in Prostate Cancer Patient Undergoing Radiotherapy Based on Dose and Surface Histogram |
| 投稿时间：2024-11-08 |
| DOI：10.11735/j.issn.1671-170X.2025.03.B009 |
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| 中文关键词: 前列腺肿瘤 剂量表面积直方图 放射性直肠炎 Nomogram模型 预测 |
| 英文关键词:prostate neoplasms dose and surface histogram radiation proctitis Nomogram model prediction |
| 基金项目:张家口市重点研发计划项目（2421018D） |
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| 摘要点击次数: 71 |
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| 中文摘要: |
| 摘 要：［目的］ 基于新型放疗计划工具评价结果等临床资料创建前列腺癌放疗致急性放射性直肠炎（radiation proctitis，RP）Nomogram预测模型，评价模型的临床应用价值。［方法］ 筛选河北北方学院附属第一医院2020年7月至2024年7月前列腺癌患者200例，采用中央随机系统分配法分为建模组（n=140）和验证组（n=60）。基于患者CT影像资料创建剂量表面积直方图（dose and surface histogram，DSH）。基于建模组DSH评价结果等临床资料，采用Logistic回归分析急性RP影响因素，并创建Nomogram预测模型。通过建模组内部验证和验证组外部验证评价模型的临床应用价值。［结果］ Nomogram预测模型显示肿瘤体积（OR=1.420， 95%CI：1.105～2.394）、计划肿瘤体积最大剂量（OR=2.171，95%CI：1.374～3.763）、S70（OR=2.956，95%CI：1.579～5.093）、S78（OR=4.735，95%CI：1.983～8.227）均是影响急性RP发生的独立危险因素（P均＜0.05）。肿瘤体积、计划肿瘤体积最大剂量、S70、S78预测急性RP的曲线下面积（area under curve，AUC）分别为0.610（95%CI：0.572～0.733）、0.658（95%CI：0.584～0.765）、0.692（95%CI：0.603～0.793）和0.703（95%CI：0.623～0.835）。建模组内部验证：模型一致性指数为0.832（95%CI：0.781～0.949），校准曲线上观测值与实际值一致性良好，模型能够提供高于各独立危险因素的临床净收益。建模组和验证组外部验证：模型预测两组急性RP的AUC均高于各独立危险因素，两组ROC曲线拟合良好（χ2=1.893，P＞0.05）。［结论］ 基于DSH评价结果等临床资料的急性RP Nomogram预测模型具有一定的临床应用价值，能够为急性RP防治提供有价值的参考依据。 |
| 英文摘要: |
| Abstract： ［Objective］ To develop a Nomogram prediction model for acute radiation proctitis (RP) in prostate cancer patient undergoing radiotherapy based on the new radiotherapy planning tool， and to evaluate the clinical application value of the model. ［Methods］ A total of 200 prostate cancer patients undergoing radiotherapy in the First Affiliated Hospital of Hebei North University from July 2020 to July 2024 were enrolled. Patients were randomly divided into a modeling group (n=140) and a validation group (n=60). A new radiotherapy plan evaluation tool-dose and surface histogram(DSH) was created using CT imaging data of the patients. Based on DSH results and clinical data of the modeling group， influencing factors of acute RP were identified with Logistic regression analysis and a Nomogram prediction model was created. The predictive value of the model was evaluated through the internal and external validation. ［Results］ Nomogram prediction model showed that tumor volume (OR=1.420， 95%CI： 1.105~2.394)， maximum dose for planning tumor volume (OR=2.171， 95%CI： 1.374~3.763)， S70 (OR=2.956， 95%CI： 1.579~5.093)， and S78 (OR=4.735， 95%CI： 1.983~8.227) were independent risk factors for acute RP (all P<0.05). Area under curve (AUC) of tumor volume， maximum dose of planning tumor volume， S70 and S78 for predicting acute RP were 0.610(95%CI： 0.572~0.733)， 0.658 (95%CI： 0.584~0.765)， 0.692 (95%CI： 0.603~0.793)， and 0.703 (95%CI： 0.623~0.835)， respectively. The consistency index of internal validation in the modeling group was 0.832 (95%CI： 0.781~0.949)， and the observation values on the calibration curve were in good agreement with the actual values. The clinical net benefits of the model were higher than those of each independent risk factor. For external validation of both the modeling group and validation group， the AUC of the model in predicting acute RP were higher than those of each independent risk factor. The ROC curves of the two group were well fitted(χ2=1.893， P>0.05). ［Conclusion］ A Nomogram prediction model based on the new radiotherapy planning tool has been developed in the study， which has certain application value for predicting acute RP in prostate patients undergoing radiotherapy. |
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