利用基于病理组学的人工智能识别口腔白斑病和头颈部鳞状细胞癌的基因组改变和预后:一项多中心实验研究。

IF 12.5 2区 医学 Q1 SURGERY
Xin-Jia Cai, Chao-Ran Peng, Ying-Ying Cui, Long Li, Ming-Wei Huang, He-Yu Zhang, Jian-Yun Zhang, Tie-Jun Li
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引用次数: 0

摘要

背景:9p 染色体缺失是口腔白斑(OLK)恶变为头颈部鳞状细胞癌(HNSCC)的重要生物标志物,与 HNSCC 患者的预后有关。然而,各种挑战阻碍了 9p 缺失在临床实践中的评估。本研究旨在开发一种基于病理组学的人工智能(AI)模型,用于快速、经济地预测9p缺失(9PLP)。材料与方法:回顾性收集了333例OLK病例的苏木精和伊红(H&E)染色的全切片图像以及多中心队列的基因组改变数据,以开发基因组改变预测AI模型。这些数据被分为训练数据集(n=217)、验证数据集(n=93)和外部测试数据集(n=23)。结合最新的 Transformer 方法和 XGBoost 算法,建立了 9PLP 模型。人工智能模型在两个多中心 HNSCC 数据集(分别为 n=42、n=365)中得到了进一步应用和验证。此外,还将 9PLP 与临床病理参数相结合,建立了评估 HNSCC 患者预后的提名图模型:结果:9PLP能利用OLK和HNSCC图像快速有效地预测9p染色体缺失,曲线下面积分别达到0.890和0.825。此外,该预测模型在 HNSCC 患者预后评估中表现出较高的准确性(1 年预测的曲线下面积为 0.739,3 年预测的曲线下面积为 0.705,5 年预测的曲线下面积为 0.691):据我们所知,本研究开发了首个针对 OLK 和 HNSCC 的基因组改变预测深度学习模型。这种新型人工智能模型可以预测 9p 缺失,并通过识别 H&E 染色图像中的病理组学特征来评估患者的预后,效果良好。未来,9PLP模型可能有助于更好地临床管理OLK和HNSCC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of genomic alteration and prognosis using pathomics-based artificial intelligence in oral leukoplakia and head and neck squamous cell carcinoma: A multicenter experimental study.

Background: Loss of chromosome 9p is an important biomarker in the malignant transformation of oral leukoplakia (OLK) to head and neck squamous cell carcinoma (HNSCC), and is associated with the prognosis of HNSCC patients. However, various challenges have prevented 9p loss from being assessed in clinical practice. The objective of this study was to develop a pathomics-based artificial intelligence (AI) model for the rapid and cost-effective prediction of 9p loss (9PLP).

Materials and methods: 333 OLK cases were retrospectively collected with hematoxylin and eosin (H&E)-stained whole slide images and genomic alteration data from multicenter cohorts to develop the genomic alteration prediction AI model. They were divided into a training dataset (n=217), a validation dataset (n=93), and an external testing dataset (n=23). The latest Transformer method and XGBoost algorithm were combined to develop the 9PLP model. The AI model was further applied and validated in two multicenter HNSCC datasets (n=42, n=365, respectively). Moreover, the combination of 9PLP with clinicopathological parameters was used to develop a nomogram model for assessing HNSCC patient prognosis.

Results: 9PLP could predict chromosome 9p loss rapidly and effectively using both OLK and HNSCC images, with the area under the curve achieving 0.890 and 0.825, respectively. Furthermore, the predictive model showed high accuracy in HNSCC patient prognosis assessment (the area under the curve was 0.739 for 1-year prediction, 0.705 for 3-year prediction, and 0.691 for 5-year prediction).

Conclusion: To the best of our knowledge, this study developed the first genomic alteration prediction deep learning model in OLK and HNSCC. This novel AI model could predict 9p loss and assess patient prognosis by identifying pathomics features in H&E-stained images with good performance. In the future, the 9PLP model may potentially contribute to better clinical management of OLK and HNSCC.

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来源期刊
CiteScore
17.70
自引率
3.30%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The International Journal of Surgery (IJS) has a broad scope, encompassing all surgical specialties. Its primary objective is to facilitate the exchange of crucial ideas and lines of thought between and across these specialties.By doing so, the journal aims to counter the growing trend of increasing sub-specialization, which can result in "tunnel-vision" and the isolation of significant surgical advancements within specific specialties.
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