基于深度学习的超声、细胞学与临床特征多模态融合鉴别滤泡性甲状腺癌与腺瘤：多中心研究
该回顾性多中心研究纳入714例病理确诊的滤泡性甲状腺肿瘤患者，构建融合超声图像、FNAC图像和临床特征的多模态深度学习模型，用于术前鉴别FTC与FTA。模型在训练集、内部验证集和两个外部验证集的AUC分别为0.947、0.933、0.936和0.928，显著优于单模态模型，且瘤周区域超声权重在FTC中更高。
要点
- 多模态融合模型在细胞学不确定的滤泡性肿瘤中AUC达0.928-0.947，可辅助术前鉴别FTC与FTA。
- 瘤周区域超声特征对FTC鉴别有增量价值，FTC中瘤周超声权重显著高于FTA。
- 模型在0.1-0.8阈值概率范围内净获益优于单模态，具有临床决策辅助潜力。
核心结论
- 整合超声、细胞学和临床特征的多模态深度学习模型对FTC与FTA的术前鉴别表现出良好诊断性能。
- 纳入瘤周区域可提供显著增量诊断价值，模型可作为细胞学不确定滤泡性肿瘤患者个体化诊疗的辅助工具。
研究设计与样本
- 回顾性多中心研究，纳入三个医学中心共714例病理确诊的滤泡性甲状腺肿瘤患者。
- 患者分为训练集304例、内部验证集130例、外部验证集1共201例和外部验证集2共79例。
- 超声特征提取采用Swin Transformer（含瘤内和瘤周区域），细胞学图像采用基于注意力的多实例学习，临床特征采用自注意力多层感知机，跨模态特征通过Transformer模块融合。
- 模型性能评估采用AUC、校准曲线和决策曲线分析（DCA）。
诊断标准与关键数值
- 多模态融合模型AUC在训练集为0.947、内部验证集0.933、外部验证集1为0.936、外部验证集2为0.928。
- 多模态模型较超声模型AUC提高0.080-0.098，较细胞学模型提高0.165-0.183，较临床模型提高0.236-0.254，均P<0.001。
- 添加瘤周区域使超声模型AUC提高0.042-0.043（P<0.05）。
- FTC病例中瘤周超声权重为29.3-30.5%，显著高于FTA的23.9-24.6%（P<0.001）。
- DCA显示多模态模型在0.1-0.8阈值概率范围内具有更优净获益。
临床应用与阅片要点
- 对于细胞学不确定的滤泡性甲状腺肿瘤，可考虑结合超声（尤其瘤周区域）、细胞学图像和临床特征进行多模态评估以辅助鉴别FTC与FTA。
- 阅片时关注瘤周区域超声特征，模型分析提示其在FTC鉴别中权重更高。
局限性
- 本研究为回顾性设计，可能存在选择偏倚。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
RATIONALE AND OBJECTIVES: Preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challenging due to overlapping cytological and ultrasonographic features. This study aimed to develop and validate a multimodal deep learning model integrating ultrasound images, fine-needle aspiration cytology (FNAC) images, and clinical features for preoperative differentiation of FTC from FTA in patients with cytologically indeterminate follicular thyroid neoplasms.
MATERIALS AND METHODS: This retrospective multicenter study included 714 patients with pathologically confirmed follicular thyroid neoplasms from three medical centers. Patients were divided into training set (n = 304), internal validation set (n = 130), and two external validation sets (n = 201 and n = 79). The multimodal model employed Swin Transformer for ultrasound feature extraction (intratumoral and peritumoral regions), attention-based multiple instance learning for cytological image processing, and self-attention multilayer perceptron for clinical feature encoding. Cross-modal feature fusion was achieved through a Transformer module. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS: The multimodal fusion model achieved AUCs of 0.947, 0.933, 0.936, and 0.928 in the training set, internal validation set, external validation set 1, and external validation set 2, respectively. Compared with unimodal models, the multimodal model demonstrated significant improvements (all P < 0.001): AUC increased by 0.080-0.098 versus the ultrasound model, 0.165-0.183 versus the cytological model, and 0.236-0.254 versus the clinical model. The addition of the peritumoral region improved ultrasound model AUC by 0.042-0.043 (P < 0.05). Modality contribution analysis revealed that the peritumoral ultrasound weight was significantly higher in FTC than FTA cases (29.3-30.5% vs. 23.9-24.6%, P < 0.001). DCA demonstrated superior net benefit of the multimodal model across threshold probabilities of 0.1-0.8.
CONCLUSION: The multimodal deep learning model integrating ultrasound, cytological, and clinical features demonstrated favorable diagnostic performance for preoperative differentiation between FTC and FTA. The inclusion of peritumoral region provided significant incremental diagnostic value. This model may serve as an effective auxiliary tool for individualized diagnosis and treatment decision-making in patients with cytologically indeterminate follicular thyroid neoplasms.
引用
- Guo XF, Zhou L, Bao XY, et al. Deep Learning-Based Multimodal Fusion of Ultrasound, Cytology, and Clinical Features to Distinguish Follicular Thyroid Carcinoma from Adenoma: A Multicenter Study. Acad Radiol. 2026. PMID: 41982038. doi:10.1016/j.acra.2026.03.044
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。