基于放射组学的头颈部鳞状细胞癌 TGF-β1 表达预测模型

IF 2 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
American journal of nuclear medicine and molecular imaging Pub Date : 2024-08-25 eCollection Date: 2024-01-01 DOI:10.62347/JMKV7596
Kai Qin, Chen Gong, Yi Cheng, Li Li, Chengxia Liu, Feng Yang, Jie Rao, Qianxia Li
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引用次数: 0

摘要

目的探讨TGF-β1表达与头颈部鳞状细胞癌(HNSCC)患者生存期的关系,以及基于CT的无创放射组学能否预测HNSCC患者的TGF-β1表达:方法:从 TCGA 数据库中获取转录谱分析数据和临床信息,然后根据 TGF-β1 表达截断值进行分类。根据增强动脉期CT扫描的完整性,选择了139例HNSCC患者。使用 PyRadiomics 软件包提取放射学特征,并使用 3D Slicer 软件进行图像分割。利用 mRMR_RFE 和 Repeat LASSO 算法,确定了建立相应梯度增强预测模型的最佳特征:对 483 例患者进行了生存分析,根据 TGF-β1 表达截断值将患者分为两组。Kaplan-Meier曲线显示,TGF-β1是降低患者生存率的重要独立风险因素。为了构建梯度增强预测模型,我们使用了 mRMR_RFE 算法和 Repeat_LASSO 算法,获得了两个特征(glrlm 和 ngtdm)和三个辐射特征(glrlm、first order_10percentile 和 gldm)。在训练组和验证组中,这两个已建立的模型都表现出很强的预测潜力。此外,mRMR_RFE_GBM 模型和 LASSO_GBM 模型的校准曲线、DCA 图或 AUC 值在统计学上没有显著差异,这表明这两个模型拟合良好:基于这些发现,TGF-β1 被证明与不良预后显著相关,是 HNSCC 的潜在风险因素。此外,通过使用 mRMR_RFE_GBM 和 Repeat_LASSO_GBM 模型,我们能够通过基于 CT 的无创放射组学有效预测 HNSCC 中 TGF-β1 的表达水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma.

Objective: To explore the connection between TGF-β1 expression and the survival of patients with head and neck squamous cell carcinoma (HNSCC), as well as whether non-invasive CT-based Radiomics can predict TGF-β1 expression in HNSCC patients.

Methods: Data on transcriptional profiling and clinical information were acquired from the TCGA database and subsequently categorized based on the TGF-β1 expression cutoff value. Based on the completeness of enhanced arterial phase CT scans, 139 HNSCC patients were selected. The PyRadiomics package was used to extract radiomic features, and the 3D Slicer software was used for image segmentation. Using the mRMR_RFE and Repeat LASSO algorithms, the optimal features for establishing the corresponding gradient enhancement prediction models were identified.

Results: A survival analysis was performed on 483 patients, who were divided into two groups based on the TGF-β1 expression cut-off. The Kaplan-Meier curve indicated that TGF-β1 was a significant independent risk factor that reduced patient survival. To construct gradient enhancement prediction models, we used the mRMR_RFE algorithm and the Repeat_LASSO algorithm to obtain two features (glrlm and ngtdm) and three radiation features (glrlm, first order_10percentile, and gldm). In both the training and validation cohorts, the two established models demonstrated strong predictive potential. Furthermore, there was no statistically significant difference in the calibration curve, DCA diagram, or AUC values between the mRMR_RFE_GBM model and the LASSO_GBM model, suggesting that both models fit well.

Conclusion: Based on these findings, TGF-β1 was shown to be significantly associated with a poor prognosis and to be a potential risk factor for HNSCC. Furthermore, by employing the mRMR_RFE_GBM and Repeat_LASSO_GBM models, we were able to effectively predict TGF-β1 expression levels in HNSCC through non-invasive CT-based Radiomics.

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来源期刊
American journal of nuclear medicine and molecular imaging
American journal of nuclear medicine and molecular imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
自引率
4.00%
发文量
4
期刊介绍: The scope of AJNMMI encompasses all areas of molecular imaging, including but not limited to: positron emission tomography (PET), single-photon emission computed tomography (SPECT), molecular magnetic resonance imaging, magnetic resonance spectroscopy, optical bioluminescence, optical fluorescence, targeted ultrasound, photoacoustic imaging, etc. AJNMMI welcomes original and review articles on both clinical investigation and preclinical research. Occasionally, special topic issues, short communications, editorials, and invited perspectives will also be published. Manuscripts, including figures and tables, must be original and not under consideration by another journal.
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