Cui Haoyu, Guo Qinhao, Xu Jun, Wu Xiaohua, Cai Chengfei, Jiao Yiping, Ming Wenlong, Wen Hao, Wang Xiangxue
Jiangsu Key Laboratory of Intelligent Medical Image Computing, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Bioinformatics. 2025 Feb 4;41(2). doi: 10.1093/bioinformatics/btaf059.
Endometrial cancer is a prevalent gynecological malignancy that requires accurate identification of its molecular subtypes for effective diagnosis and treatment. Four molecular subtypes with different clinical outcomes have been identified: POLE mutation, mismatch repair deficient, p53 abnormal, and no specific molecular profile. However, determining these subtypes typically relies on expensive gene sequencing. To overcome this limitation, we propose a novel method that utilizes hematoxylin and eosin-stained whole slide images to predict endometrial cancer molecular subtypes.
Our approach leverages a hierarchical foundation model as a backbone, fine-tuned from the UNI computational pathology foundation model, to extract tissue embedding from different scales. We have achieved promising results through extensive experimentation on the Fudan University Shanghai Cancer Center cohort (N = 364). Our model demonstrates a macro-average AUROC of 0.879 (95% CI, 0.853-0.904) in a five-fold cross-validation. Compared to the current state-of-the-art molecular subtypes prediction for endometrial cancer, our method outperforms in terms of predictive accuracy and computational efficiency. Moreover, our method is highly reproducible, allowing for ease of implementation and widespread adoption. This study aims to address the cost and time constraints associated with traditional gene sequencing techniques. By providing a reliable and accessible alternative to gene sequencing, our method has the potential to revolutionize the field of endometrial cancer diagnosis and improve patient outcomes.
The codes and data used for generating results in this study are available at https://github.com/HaoyuCui/hi-UNI for GitHub and https://doi.org/10.5281/zenodo.14627478 for Zenodo.
子宫内膜癌是一种常见的妇科恶性肿瘤，需要准确识别其分子亚型以进行有效的诊断和治疗。已确定了四种具有不同临床结果的分子亚型：POLE突变型、错配修复缺陷型、p53异常型和无特定分子特征型。然而，确定这些亚型通常依赖于昂贵的基因测序。为克服这一局限性，我们提出了一种利用苏木精和伊红染色的全切片图像来预测子宫内膜癌分子亚型的新方法。
我们的方法利用一个分层基础模型作为主干，该模型从UNI计算病理学基础模型微调而来，以从不同尺度提取组织嵌入特征。我们通过对复旦大学附属肿瘤医院队列（N = 364）进行广泛实验取得了有前景的结果。在五折交叉验证中，我们的模型展示了0.879的宏平均受试者工作特征曲线下面积（95%置信区间，0.853 - 0.904）。与当前子宫内膜癌分子亚型预测的最先进方法相比，我们的方法在预测准确性和计算效率方面表现更优。此外，我们的方法具有高度可重复性，易于实施和广泛应用。本研究旨在解决与传统基因测序技术相关的成本和时间限制问题。通过提供一种可靠且易于获取的基因测序替代方法，我们的方法有潜力革新子宫内膜癌诊断领域并改善患者预后。
本研究中用于生成结果的代码和数据可在GitHub的https://github.com/HaoyuCui/hi-UNI以及Zenodo的https://doi.org/10.5281/zenodo.14627478获取。
