Predicting first-line VEGFR-TKI resistance and survival in metastatic clear cell renal cell carcinoma using a clinical-radiomic nomogram.

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-11-11 DOI:10.1186/s40644-024-00792-7
Yichen Wang, Xinxin Zhang, Sicong Wang, Hongzhe Shi, Xinming Zhao, Yan Chen
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引用次数: 0

Abstract

Background: This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients.

Materials and methods: In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test.

Results: The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p < 0.001) and OS (HR 0.38 (0.20, 0.71), p = 0.002), in training set. PFS in low-risk group were significantly longer than high-risk group in training (p < 0.001) and test (p < 0.001) set, respectively. OS in low-risk group were significantly longer than high-risk group in training (p = 0.003) and test (p = 0.009) set, respectively.

Conclusion: A nomogram combining baseline radiomic signature and clinical factors helped detecting first-line VEGFR-TKI early resistance and predicting short and long-term prognosis in mccRCC patients.

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利用临床放射线组学提名图预测转移性透明细胞肾细胞癌的一线 VEGFR-TKI 耐药性和生存期。
研究背景本研究旨在利用放射学和临床特征构建预测模型,以预测转移性透明细胞肾细胞癌(mccRCC)患者的一线血管内皮生长因子受体-酪氨酸激酶抑制剂(VEGFR-TKI)早期耐药情况。我们还旨在探索预测模型与 mccRCC 患者短期和长期生存的相关性:在这项回顾性研究中,我们纳入了2009年至2019年的110名mccRCC患者,并将其分为训练集和测试集。从基线增强 CT 图像的肿瘤 3D-ROI 中提取放射学特征。通过 Lasso 方法选择放射学特征,构建放射学评分。利用放射学评分和临床因素的组合建立了综合提名图。利用 ROC 曲线量化了放射学、临床和组合提名图的判别能力。Cox回归分析用于检验提名图评分与无进展生存期(PFS)和总生存期(OS)的相关性。通过对数秩检验比较不同风险组的无进展生存期和总生存期:结果:放射学、临床和组合提名图在训练集中的AUC分别为0.81、0.75和0.83;在测试集中的AUC分别为0.79、0.77和0.88。提名图得分≥1.18是PFS的一个独立预后因素(HR 0.22 (0.10, 0.47), p 结论:结合基线放射特征的提名图是PFS的一个独立预后因素:结合基线放射学特征和临床因素的提名图有助于检测一线VEGFR-TKI早期耐药并预测mccRCC患者的短期和长期预后。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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