Birla Shweta, Tiwari Nimisha, Shyamal Pragati, Khatri Abhishek, Bandaru Divya, Sharma Arundhati, Gupta Dinesh, Agarwal Shipra
Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, 110067, India.
Department of Pathology, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029, India.
Endocr Pathol. 2025 Jun 18;36(1):22. doi: 10.1007/s12022-025-09865-0.
The recent introduction of the term non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) marked a pivotal shift in the classification of encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) lacking invasive features. While its reclassification from the "malignant" to "low-risk neoplasm" category significantly reduced overtreatment, its histopathological diagnosis remains challenging due to overlapping features with other thyroid lesions and inter-observer variability. Artificial intelligence (AI) overcomes such key limitations of histopathological evaluation, ensuring a robust and efficient diagnostic process. While preliminary studies are promising, AI models capable of efficiently distinguishing NIFTP from other benign and malignant thyroid entities are yet to be developed. We devised an innovative AI-based three-stage hierarchical pipeline that systematically evaluates architectural patterns and nuclear features. The prioritized models were trained using 154,498 patches, derived from 134 sections prepared from 125 thyroid nodules, representing follicular nodular disease (FND), follicular adenoma, dominant nodule in FND, invasive EFVPTC (IEFVPTC), and classic and infiltrative follicular subtypes of PTC. External validation revealed good accuracy at the overall, patient-wise, and class-wise levels. However, it showed limitations in the differential diagnosis of NIFTP from IEFVPTC-an expected challenge due to overlapping nuclear features and the absence of incorporating the assessment of the tumor capsule for invasive characteristics. While the novel approach and the algorithm show promise in transforming histopathological NIFTP diagnostics, further improvements and rigorous validations are necessary before considering its application in real-world clinical settings.
术语“具有乳头状核特征的非侵袭性滤泡性甲状腺肿瘤”（NIFTP）的近期引入标志着对缺乏侵袭特征的甲状腺乳头状癌滤泡变体（EFVPTC）进行分类的一个关键转变。虽然将其从“恶性”重新分类为“低风险肿瘤”类别显著减少了过度治疗，但由于其与其他甲状腺病变的特征重叠以及观察者间的差异，其组织病理学诊断仍然具有挑战性。人工智能（AI）克服了组织病理学评估的此类关键限制，确保了一个强大而高效的诊断过程。虽然初步研究很有前景，但能够有效区分NIFTP与其他良性和恶性甲状腺实体的AI模型尚未开发出来。我们设计了一种基于AI的创新型三阶段分层流程，系统地评估结构模式和核特征。使用从125个甲状腺结节制备的134个切片中提取的154,498个图像块对优先模型进行训练，这些结节代表滤泡性结节病（FND）、滤泡性腺瘤、FND中的优势结节、侵袭性EFVPTC（IEFVPTC）以及PTC的经典和浸润性滤泡亚型。外部验证在总体、患者层面和类别层面均显示出良好的准确性。然而，它在区分NIFTP与IEFVPTC的鉴别诊断中显示出局限性——由于核特征重叠以及未纳入对肿瘤包膜侵袭特征的评估，这是一个预期的挑战。虽然这种新方法和算法在改变组织病理学NIFTP诊断方面显示出前景，但在考虑将其应用于实际临床环境之前，还需要进一步改进和严格验证。
