Abstract
Accurate detection of cancer tissue regions (CTR) enables deeper analysis of the tumor microenvironment and offers crucial insights into treatment response. Traditional CTR detection methods, which typically rely on the rich cellular morphology in histology images, are susceptible to a high rate of false positives due to morphological similarities across different tissue regions. The groundbreaking advances in spatial transcriptomics (ST) provide detailed cellular phenotypes and spatial localization information, offering new opportunities for more accurate cancer region detection. However, current methods are unable to effectively integrate histology images with ST data, especially in the context of cross-sample and cross-platform/batch settings for accomplishing the CTR detection. To address this challenge, we propose SpaCRD, a transfer learning-based method that deeply integrates histology images and ST data to enable reliable CTR detection across diverse samples, platforms, and batches. Once trained on source data, SpaCRD can be readily generalized to accurately detect cancerous regions across samples from different platforms and batches. The core of SpaCRD is a category-regularized variational reconstruction-guided bidirectional cross-attention fusion network, which enables the model to adaptively capture latent co-expression patterns between histological features and gene expression from multiple perspectives. Extensive benchmark analysis on 23 matched histology-ST datasets spanning various disease types, platforms, and batches demonstrates that SpaCRD consistently outperforms existing eight state-of-the-art methods in CTR detection.
Abstract (translated)
精确检测癌症组织区域（CTR）有助于深入分析肿瘤微环境，并为治疗反应提供关键见解。传统的CTR检测方法通常依赖于组织学图像中丰富的细胞形态，但由于不同组织区域之间存在形态相似性，这种方法容易产生高比率的假阳性结果。空间转录组学（ST）领域的突破性进展提供了详细的细胞表型和空间定位信息，为更准确地检测癌症区域带来了新的机遇。然而，目前的方法无法有效地将组织学图像与ST数据相结合，尤其是在跨样本、跨平台/批次设置下完成CTR检测时更是如此。 为了应对这一挑战，我们提出了一种基于迁移学习的方法——SpaCRD，该方法深度整合了组织学图像和ST数据，能够在不同的样本、平台和批次中实现可靠的CTR检测。经过源数据训练后，SpaCRD可以轻松推广到不同平台和批次的样本中以准确地检测癌症区域。 SpaCRD的核心是一个类别正则化的变分重构引导双向交叉注意力融合网络，使模型能够从多个角度自适应捕捉组织学特征与基因表达之间的潜在共表达模式。在23组匹配的组织学-ST数据集上进行广泛的基准测试分析（涵盖不同的疾病类型、平台和批次），结果显示SpaCRD在CTR检测方面始终优于现有的八种最先进的方法。 这种方法不仅提升了癌症区域检测的准确性，还为跨样本、跨平台/批次的数据融合提供了新的思路和技术支持。
URL
https://arxiv.org/abs/2603.06186
