| 周琼辉,陈路桥,倪千喜,等.基于影像组学和剂量组学预测局部晚期宫颈癌患者的血液学不良反应[J].中华放射医学与防护杂志,2025,45(3):188-193.Zhou Qionghui,Chen Luqiao,Ni Qianxi,et al.Prediction of hematologic toxicity in patients with locally advanced cervical cancer based on radiomics and dosiomics[J].Chin J Radiol Med Prot,2025,45(3):188-193 |
| 基于影像组学和剂量组学预测局部晚期宫颈癌患者的血液学不良反应 |
| Prediction of hematologic toxicity in patients with locally advanced cervical cancer based on radiomics and dosiomics |
| 投稿时间：2024-05-16 |
| DOI：10.3760/cma.j.cn112271-20240516-00179 |
| 中文关键词: 机器学习 影像组学 剂量组学 宫颈癌 血液学不良反应 |
| 英文关键词:Machine learning Radiomics Dosiomics Cervical cancer Hematologic toxicity |
| 基金项目:湖南省自然科学基金面上项目(2023JJ30373);国家重点研发计划项目(2022YFC2404604);湖南省科技创新计划资助项目(2021SK51116,2023SK4034);湖南省肿瘤医院攀登科研计划重点研发项目(YF2021006) |
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| 中文摘要: |
| 目的 探索基于影像组学和剂量组学的机器学习模型以评估晚期宫颈癌患者的血液学不良反应(HT),并对多组学特征的综合应用进行初步探讨。方法 回顾性收集2022年1月至2023年6月在中南大学湘雅医学院附属肿瘤医院接受同步放化疗的205例晚期宫颈癌患者的临床资料、计划CT图像及剂量文件,并根据患者的HT严重程度进行分类。在同一感兴趣区分别提取影像组学特征和剂量组学特征,并使用随机森林算法进行特征选择,分别建立基于极端梯度提升树的影像组学模型、剂量组学模型和混合模型,计算灵敏度、特异度和受试者工作特征曲线下面积(AUC)值,以评估模型的分类性能。结果 影像组学模型和剂量组学模型的灵敏度、特异度、AUC值分别为0.42、0.86、0.78和0.50、0.90、0.74;而混合模型的灵敏度、特异度、AUC值分别为0.50、0.83、0.83,与单独的影像组学和剂量组学模型相比,混合模型表现出更好地分类能力。结论 针对接受同步放化疗的晚期宫颈癌患者,使用基于影像组学或剂量组学的机器学习方法在HT的分类预测中具有可行性,并且综合影像组学特征和剂量组学特征在一定程度上可以提高预测模型的分类性能,具有改善患者治疗策略的应用潜力。 |
| 英文摘要: |
| Objective To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features. Methods A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models. Conclusions It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer. |
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