用于预测甲状腺乳头状癌颈部淋巴结转移的多模态MRI-FNAC放射组学-病理组学模型的开发与内部验证

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中文摘要

背景： 颈部淋巴结转移（CLNM）发生于40-60%的甲状腺乳头状癌（PTC）病例中，并严重影响手术规划和预后。目前的术前评估主要依赖超声和超声引导下细针穿刺细胞学（FNAC），其敏感性有限且假阴性率高。这些不足常导致漏诊转移或进行不必要的预防性颈部清扫，从而带来喉返神经损伤和甲状旁腺功能减退的风险。因此，迫切需要更准确的术前无创预测工具。本研究旨在开发并内部验证一种基于深度学习特征的多模态磁共振成像（MRI）-FNAC放射组学-病理组学模型，用于预测PTC患者的CLNM。
方法： 我们回顾性纳入了128例接受甲状腺切除术和颈部淋巴结清扫术的PTC患者（78例伴CLNM，50例不伴CLNM）。患者按7:3比例随机分为训练集（n=90）和测试集（n=38）。使用预训练的ResNet-50模型从术前MRI中提取深度特征，并从FNAC苏木精-伊红（H&E）图像中提取深度特征。应用六种机器学习算法，基于MRI放射组学、病理组学和 multimodal 融合特征开发模型。使用曲线下面积（AUC）、准确率、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、决策曲线分析（DCA）、DeLong检验以及校准指标（Brier评分和Hosmer-Lemeshow检验）评估性能。
结果： 队列平均年龄为48.82±10.02岁（71.1%为女性）。CLNM阳性组与阴性组在年龄、性别或肿瘤直径方面未发现显著差异，而多灶性和甲状腺外侵犯在CLNM阳性组中更为常见。在测试集中，多模态k近邻（KNN）模型取得了最高的AUC，为0.872（95%置信区间：0.778-0.955）。DeLong检验证实其优于最佳的仅病理组学模型（P=0.046）和最佳的仅MRI模型（P=0.003）。多模态模型还表现出最佳的校准（Brier评分0.1059；Hosmer-Lemeshow P=0.72），并在DCA上显示出更优的临床净获益。
结论： 多模态MRI-FNAC放射组学-病理组学模型在预测PTC中的CLNM方面显示出良好的性能。然而，在临床实施之前，需要在更大的多中心队列中进行外部验证。

英文摘要

BACKGROUND: Cervical lymph node metastasis (CLNM) occurs in 40-60% of papillary thyroid carcinoma (PTC) cases and critically affects surgical planning and prognosis. Current preoperative assessment relies mainly on ultrasound and ultrasound-guided fine-needle aspiration cytology (FNAC), which have limited sensitivity and a high false-negative rate. These shortcomings frequently lead to either missed metastases or unnecessary prophylactic neck dissection with risks of recurrent laryngeal nerve injury and hypoparathyroidism. Therefore, more accurate non-invasive preoperative prediction tools are urgently needed. This study aimed to develop and internally validate a multimodal magnetic resonance imaging (MRI)-FNAC radiomics-pathomics model based on deep learning features for predicting CLNM in patients with PTC.
METHODS: We retrospectively enrolled 128 PTC patients who underwent thyroidectomy and neck dissection (78 with CLNM, 50 without). Patients were randomly split into training (n=90) and test (n=38) sets at a 7:3 ratio. Deep features were extracted from preoperative MRI using a pretrained ResNet-50 model and from FNAC hematoxylin and eosin (H&E) images. Six machine learning algorithms were applied to develop models based on MRI radiomics, pathomics, and multimodal fusion features. Performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), DeLong test, and calibration metrics (Brier score and Hosmer-Lemeshow test).
RESULTS: The cohort had a mean age of 48.82±10.02 years (71.1% female). No significant differences were found between CLNM-positive and -negative groups in age, sex, or tumor diameter, whereas multifocality and extrathyroidal extension were more prevalent in the CLNM-positive group. In the test set, the multimodal k-nearest neighbors (KNN) model achieved the highest AUC of 0.872 (95% confidence interval: 0.778-0.955). DeLong tests confirmed its superiority over the best pathomics-only model (P=0.046) and the best MRI-only model (P=0.003). The multimodal model also demonstrated the best calibration (Brier score 0.1059; Hosmer-Lemeshow P=0.72) and superior clinical net benefit on DCA.
CONCLUSIONS: The multimodal MRI-FNAC radiomics-pathomics model showed promising performance for predicting CLNM in PTC. However, external validation in larger multicenter cohorts is required before clinical implementation.