L'Imperio Vincenzo, Coelho Vasco, Cazzaniga Giorgio, Papetti Daniele M, Del Carro Fabio, Capitoli Giulia, Marino Mario, Ceku Joranda, Fusco Nicola, Ivanova Mariia, Gianatti Andrea, Nobile Marco S, Galimberti Stefania, Besozzi Daniela, Pagni Fabio
School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy; Department of Pathology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Mod Pathol. 2024 Dec;37(12):100608. doi: 10.1016/j.modpat.2024.100608. Epub 2024 Sep 5.
The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases and further complicated by the inclusion of noninvasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTP. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye and (2) develop a deep learning model for multiclass segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable internuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available next-generation sequencing data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole-slide images of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all whole-slide image tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.
甲状腺结节的诊断评估因临界病例中不确定性的持续存在而受到阻碍，并且由于将具有乳头样核特征的非侵袭性滤泡性肿瘤（NIFTP）纳入其中作为甲状腺乳头状癌（PTC）侵袭性较低的替代方案而变得更加复杂。在这种情况下，计算方法可能通过揭示NIFTP的关键核特征来促进诊断过程。这项工作的主要目的是：（1）识别NIFTP和PTC的形态特征，这些特征对人眼来说是可解释的；（2）开发一种用于多类分割的深度学习模型，作为减少诊断变异性的支持工具。我们的研究结果证实，NIFTP和PTC中的细胞核具有多个共同特征，这使它们与增生性结节（HP）区分开来。形态分析确定了15个特征，这些特征可以转化为病理学家易于理解的核改变，例如HP的核内均匀性显著，而NIFTP的染色质纹理主要是复杂性，PTC的核纹理变化具有独特模式。还分析了一些具有可用下一代测序数据的NIFTP病例，以初步探索RAS相关突变对核形态测量的影响。最后，在NIFTP、PTC和HP病例的全切片图像上训练和测试了基于像素的深度学习模型。该模型名为NUTSHELL（甲状腺肿瘤细胞核分割以突出包封的低恶性病变），成功地检测并分类了所有全切片图像块中的大多数细胞核，显示出与已经成熟的病理核评分相当的结果。NUTSHELL提供了NIFTP区域的即时概述，可用于在广泛的腺体样本中检测PTC的微灶或识别淋巴结转移。NUTSHELL可以在WSInfer中运行，并在QuPath中轻松渲染，从而促进数字病理学的普及。
