Preoperative Prediction of Occult Level V Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and Validation of a Radiomics-Driven Nomogram Model.

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-10-22 DOI:10.1016/j.acra.2024.10.001
Jia-Wei Feng, Feng Zheng, Shui-Qing Liu, Gao-Feng Qi, Xin Ye, Jing Ye, Yong Jiang
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Abstract

Rationale and objectives: The study aimed to analyze the patterns and frequency of Level V lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC), identify its risk factors, and construct predictive models for assessment.

Methods: We conducted a retrospective analysis of 325 PTC patients who underwent thyroidectomy and therapeutic unilateral bilateral modified radical neck dissection from October 2020 to January 2023. Patients were randomly allocated into a training cohort (70%) and a validation cohort (30%). The radiomics signature model was developed using ultrasound images, applying the minimum Redundancy-Maximum Relevance and Least Absolute Shrinkage and Selection Operator regression to extract high-throughput quantitative features. Concurrently, the clinic signature model was formulated based on significant clinical factors associated with Level V LNM. Both models were independently translated into nomograms for ease of clinical use.

Results: The radiomics signature model, without the inclusion of clinical factors, showed high discriminative power with an area under the curve (AUC) of 0.933 in the training cohort and 0.912 in the validation cohort. Conversely, the clinic signature model, composed of tumor margin, simultaneous metastasis, and high-volume lateral LNM, achieved an AUC of 0.749 in the training cohort. The radiomics signature model exhibited superior performance in sensitivity, specificity, positive predictive value, negative predictive value across both cohorts. Decision curve analysis demonstrated the clinical utility of the radiomics signature model, indicating its potential to guide more precise treatment decisions.

Conclusion: The radiomics signature model outperformed the clinic signature model in predicting Level V LNM in PTC patients. The radiomics signature model, available as a nomogram, offers a promising tool for preoperative assessment, with the potential to refine clinical decision-making and individualize treatment strategies for PTC patients with potential Level V LNM.

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甲状腺乳头状癌隐匿性五级淋巴结转移的术前预测:放射组学驱动的提名图模型的开发与验证
依据和目的:该研究旨在分析甲状腺乳头状癌(PTC)V级淋巴结转移(LNM)的模式和频率,确定其风险因素,并构建评估预测模型:我们对2020年10月至2023年1月期间接受甲状腺切除术和治疗性单侧双侧改良根治性颈部清扫术的325例PTC患者进行了回顾性分析。患者被随机分配到训练队列(70%)和验证队列(30%)。放射组学特征模型是利用超声图像开发的,应用最小冗余-最大相关性和最小绝对收缩和选择操作器回归提取高通量定量特征。同时,根据与五级 LNM 相关的重要临床因素制定了临床特征模型。为了便于临床使用,两个模型都被独立转化为提名图:结果:未纳入临床因素的放射组学特征模型显示出很高的鉴别力,训练队列的曲线下面积(AUC)为 0.933,验证队列的曲线下面积(AUC)为 0.912。相反,由肿瘤边缘、同时转移和高体积侧LNM组成的临床特征模型在训练队列中的AUC为0.749。在两个队列中,放射组学特征模型在灵敏度、特异性、阳性预测值和阴性预测值方面均表现优异。决策曲线分析表明了放射组学特征模型的临床实用性,表明它具有指导更精确治疗决策的潜力:结论:在预测PTC患者的V级LNM方面,放射组学特征模型优于临床特征模型。放射组学特征模型以提名图的形式出现,为术前评估提供了一个很有前景的工具,有可能完善临床决策,并为有潜在V级LNM的PTC患者提供个体化治疗策略。
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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