Background: Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. Methods: This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov–Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. Results: Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. Conclusion: The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.
背景：癌症生物标志物研究的最新进展催生了多种不同的数据模态，如医学影像和组织病理学图像。尽管这些模态具有正交性，但在开发预测性免疫治疗生物标志物时，它们通常被独立使用。本研究旨在探索接受免疫治疗的非小细胞肺癌（NSCLC）患者的放射学扫描与数字化病理图像之间的跨尺度关联。 方法：本研究纳入36例接受免疫治疗且同时具备放射学与病理学影像资料的NSCLC患者。从CT扫描图像和不同分辨率下的组织学细胞密度图中，分别提取了851个和260个特征。我们研究了放射组学与病理组学特征之间的关联及其与临床、生物学终点的关系。采用Kolmogorov-Smirnov（KS）检验分析两种影像模态特征相关系数分布的差异性，并通过无监督聚类识别不同影像模态对预后差异患者的区分能力。 结果：研究结果显示细胞密度病理组学特征与放射组学特征存在显著相关性。此外，影像衍生特征与临床终点之间的相关性分布呈现异质性。KS检验表明，两种影像特征分布在无进展生存期（PFS）和CD8细胞计数方面存在显著差异，而在总生存期（OS）方面分布相似。聚类分析进一步显示，基于放射组学和病理组学特征生成的聚类在患者生存期和CD8细胞计数方面均存在显著差异。 结论：本研究结果表明，在免疫检查点抑制剂治疗的NSCLC患者中，CT扫描与H&E病理切片之间存在跨尺度关联。这些关联可进一步用于开发多模态免疫治疗生物标志物，以推动肺癌个性化诊疗的发展。
肿瘤(癌症)患者之家