联合放射组学和分子生物标志物：提高甲状腺乳头状癌诊断能力的新型经济工具。
Combining radiomics and molecular biomarkers: a novel economic tool to improve diagnostic ability in papillary thyroid cancer.
机构信息
Department of Thyroid Surgery, National Key Clinical Specialty (General Surgery), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
出版信息
Front Endocrinol (Lausanne). 2024 Aug 14;15:1378360. doi: 10.3389/fendo.2024.1378360. eCollection 2024.
BACKGROUND
A preoperative diagnosis to distinguish malignant from benign thyroid nodules accurately and sensitively is urgently important. However, existing clinical methods cannot solve this problem satisfactorily. The aim of this study is to establish a simple, economic approach for preoperative diagnosis in eastern population.
METHODS
Our retrospective study included 86 patients with papillary thyroid cancer and 29 benign cases. The ITK-SNAP software was used to draw the outline of the area of interest (ROI), and Ultrosomics was used to extract radiomic features. Whole-transcriptome sequencing and bioinformatic analysis were used to identify candidate genes for thyroid nodule diagnosis. RT-qPCR was used to evaluate the expression levels of candidate genes. SVM diagnostic model was established based on the METLAB 2022 platform and LibSVM 3.2 language package.
RESULTS
The radiomic model was first established. The accuracy is 73.0%, the sensitivity is 86.1%, the specificity is 17.6%, the PPV is 81.6%, and the NPV is 23.1%. Then, CLDN10, HMGA2, and LAMB3 were finally screened for model building. All three genes showed significant differential expressions between papillary thyroid cancer and normal tissue both in our cohort and TCGA cohort. The molecular model was established based on these genetic data and partial clinical information. The accuracy is 85.9%, the sensitivity is 86.1%, the specificity is 84.6%, the PPV is 96.9%, and the NPV is 52.4%. Considering that the above two models are not very effective, We integrated and optimized the two models to construct the final diagnostic model (C-thyroid model). In the training set, the accuracy is 96.7%, the sensitivity is 100%, the specificity is 93.8%, the PPV is 93.3%, and the NPV is 100%. In the validation set, the accuracy is 97.6%, the sensitivity remains 100%, the specificity is 84.6%, the PPV is 97.3%, and the NPV is 100%.
DISCUSSION
A diagnostic panel is successfully established for eastern population through a simple, economic approach using only four genes and clinical data.
背景
准确而敏感地鉴别甲状腺良恶性结节的术前诊断至关重要。然而，现有的临床方法并不能很好地解决这个问题。本研究旨在建立一种简单、经济的东方人群术前诊断方法。
方法
我们的回顾性研究纳入了 86 例甲状腺乳头状癌患者和 29 例良性病例。使用 ITK-SNAP 软件勾画感兴趣区（ROI），并使用 Ultrosomics 提取放射组学特征。全转录组测序和生物信息学分析用于鉴定甲状腺结节诊断的候选基因。使用 RT-qPCR 评估候选基因的表达水平。基于 METLAB 2022 平台和 LibSVM 3.2 语言包建立 SVM 诊断模型。
结果
首先建立了放射组学模型，其准确率为 73.0%，灵敏度为 86.1%，特异性为 17.6%，阳性预测值为 81.6%，阴性预测值为 23.1%。然后，筛选出 CLDN10、HMGA2 和 LAMB3 用于构建模型。在本队列和 TCGA 队列中，这三个基因在甲状腺乳头状癌与正常组织之间均表现出显著的差异表达。基于这些遗传数据和部分临床信息构建了分子模型，其准确率为 85.9%，灵敏度为 86.1%，特异性为 84.6%，阳性预测值为 96.9%，阴性预测值为 52.4%。考虑到以上两个模型效果不是很理想，我们整合和优化了两个模型，构建了最终的诊断模型（C-thyroid 模型）。在训练集中，准确率为 96.7%，灵敏度为 100%，特异性为 93.8%，阳性预测值为 93.3%，阴性预测值为 100%。在验证集中，准确率为 97.6%，灵敏度仍为 100%，特异性为 84.6%，阳性预测值为 97.3%，阴性预测值为 100%。
讨论
通过仅使用 4 个基因和临床数据，我们成功建立了一种简单、经济的东方人群诊断试剂盒。
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