深度学习发现新的形态特征,同时预测甲状腺乳头状癌组织病理学的遗传改变。

IF 6.7 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Thyroid Pub Date : 2025-07-01 Epub Date: 2025-07-03 DOI:10.1089/thy.2024.0691
Ingrid Marion, Stefan Schulz, Christina Glasner, Jakob Nikolas Kather, Daniel Truhn, Markus Eckstein, Celine Mueller, Aurélie Fernandez, Simone Marquard, Marie Oliver Metzig, Wilfried Roth, Matthias Martin Gaida, Stephanie Strobl, Daniel-Christoph Wagner, Arno Schad, Moritz Jesinghaus, Nils Hartmann, Thomas Johannes Musholt, Julia I Staubitz-Vernazza, Sebastian Foersch
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引用次数: 0

摘要

背景:甲状腺乳头状癌(PTC)是内分泌系统最常见的恶性肿瘤。BRAF突变发生率为40-60%,panRAS突变发生率为10-15%,不同的基因融合事件如RET融合发生率为7-35%。人工智能(AI)方法可用于预测传统组织病理切片的遗传变化。方法:在这项回顾性研究中,我们使用了两个独立的PTC患者队列,共计662例来建立我们的AI管道。Cancer Genome Atlas队列(496例)作为发育队列,Mainz队列(166例)作为独立的外部测试队列。所有这些患者的BRAF、panRAS和融合状态被确定为目标变量。Vision Transformer在数字化注释的苏木精和伊红染色玻片上进行训练,以确定这些改变的存在。最高概率图像瓦片用于识别与遗传变化相关的新形态标准。结果:BRAF、panRAS、基因融合的受试者工作特征曲线下面积分别为0.882(置信区间0.829-0.931)、0.876(置信区间0.822-0.927)和0.858(置信区间0.801-0.912)。BRAF的准确率为79.3% (72.7-85.8%),panRAS的准确率为89.3%(84.2-94.0%),基因融合的准确率为84.7%(78.8-90.2%)。验证集上的性能与测试集上的性能几乎相同。分析最高预测瓦片,可以发现融合相关PTC的新形态学标准。结论:我们的研究表明,在PTC患者中,使用AI预测数字化组织病理切片的遗传改变是可行的。我们的模型在预测这些变化方面显示出很高的准确性,这使得它可能适用于预筛选。可解释性方法揭示了先前未描述的与某些基因型相关的形态模式。为病理学家提供这些基于人工智能的特征可以提高他们的准确性。假设进一步积极的前瞻性验证,这一发现将有助于更深入地了解PTC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.

Background: Papillary thyroid carcinoma (PTC) is the most common malignant tumor of the endocrine system. BRAF mutations occur in 40-60%, panRAS mutations in 10-15%, and different gene fusion events such as RET fusions in 7-35% of these neoplasms. Artificial intelligence (AI) methods could be used to predict genetic changes from conventional histopathological slides. Methods: In this retrospective study, we used two independent cohorts of patients with PTC, totaling 662 cases for the establishment of our AI pipeline. The Cancer Genome Atlas cohort (496 cases) served as the developmental cohort, while the Mainz cohort (166 cases) served as an independent external test cohort. BRAF, panRAS, and fusion status was determined for all of these patients as target variables. Vision Transformer was trained on digitized annotated hematoxylin and eosin-stained slides for the presence of these alterations. Highest probability image tiles were used to identify new morphological criteria associated with the genetic changes. Results: The trained model resulted in an area under the receiver operating characteristic curve of 0.882 (confidence interval 0.829-0.931) for BRAF, 0.876 (0.822-0.927) for panRAS, and 0.858 (0.801-0.912) for gene fusions. Accuracy was 79.3% (72.7-85.8%) for BRAF, 89.3% (84.2-94.0%) for panRAS, and 84.7% (78.8-90.2%) for gene fusions. The performance on the validation set was almost identical to that on the test set. Analyzing the highest predictive tiles, novel morphological criteria for fusion-associated PTC could be discovered. Conclusions: Our study demonstrates that predicting genetic alterations in digitized histopathological slides using AI is feasible in patients with PTC. Our model showed high accuracy in predicting these changes, making it potentially suitable for pre-screening. Explainability approaches uncovered previously undescribed morphological patterns associated with certain genotypes. Providing pathologists with these AI-based features could improve their accuracy. Assuming further positive prospective validation, this discovery could contribute to a deeper understanding of PTC.

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来源期刊
Thyroid
Thyroid 医学-内分泌学与代谢
CiteScore
12.30
自引率
6.10%
发文量
195
审稿时长
6 months
期刊介绍: This authoritative journal program, including the monthly flagship journal Thyroid, Clinical Thyroidology® (monthly), and VideoEndocrinology™ (quarterly), delivers in-depth coverage on topics from clinical application and primary care, to the latest advances in diagnostic imaging and surgical techniques and technologies, designed to optimize patient care and outcomes. Thyroid is the leading, peer-reviewed resource for original articles, patient-focused reports, and translational research on thyroid cancer and all thyroid related diseases. The Journal delivers the latest findings on topics from primary care to clinical application, and is the exclusive source for the authoritative and updated American Thyroid Association (ATA) Guidelines for Managing Thyroid Disease.
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