Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram
Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram
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DOI：
10.1007/s00330-023-09552-w
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发表时间：
2023-03
影响因子：
5.9
通讯作者：
Haiming Li;S. Cai;Lin Deng;Zebin Xiao;Q. Guo;J. Qiang;J. Gong;Yajia Gu;Zaiyi Liu
中科院分区：
文献类型：
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作者：
Haiming Li;S. Cai;Lin Deng;Zebin Xiao;Q. Guo;J. Qiang;J. Gong;Yajia Gu;Zaiyi Liu
目的探讨放射组学列线图在晚期高级别浆液性卵巢癌（HGSOC）铂类药物耐药及无进展生存期（PFS）预测中的应用价值。采用基于支持向量机的递归特征剔除方法对放射组学特征进行筛选，生成放射组学签名。此外，通过多变量逻辑回归，使用放射组学特征和临床特征开发放射组学列线图。使用受试者工作特征分析评估预测性能。净重新分类指数（NRI），综合歧视改善（IDI），决策曲线分析（DCA）被用来比较不同的models.ResultsFive功能显着相关的铂类药物耐药构建放射组学模型的临床效用和效益。放射组学列线图，结合放射组学特征与三个临床特征（FIGO分期，CA-125，和残留肿瘤），具有更高的曲线下面积（AUC）相比，单独的临床模型（AUC：0.799 vs 0.747），阳性NRI和IDI。放射组学列线图的净效益通常高于仅临床和仅放射组学模型。Kaplan-Meier生存分析显示，放射组学诺模图定义的高危组与低危组相比，晚期HGSOC.ConclusionsThe放射组学诺模图可以识别铂类耐药，预测PFS。关键点·基于放射组学的方法具有识别铂耐药性的潜力，可以帮助进行晚期HGSOC的个性化管理。·放射组学-临床列线图显示，与单独使用两者相比，预测铂类耐药HGSOC的性能有所改善。·在训练和测试队列中，拟议的诺模图在预测低风险和高风险HGSOC患者的PFS时间方面表现良好。
ObjectiveThis study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC).Materials and methodsIn this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine–based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models.ResultsFive features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan–Meier survival analysis showed that the radiomics nomogram–defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC.ConclusionsThe radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC.Key Points•The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC.•The radiomics–clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC.•The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.