转移的过程是复杂的，通常在常规临床诊断中无法识别。膀胱癌（BC）的淋巴结转移（LNM）通常与浸润肌层的肿瘤有关。为了预防和识别LNM，标准治疗包括根治性膀胱切除术并进行淋巴结清扫和组织学检查。在这里，我们提出红外（IR）显微镜作为LNM预测的工具。为此，首先收集了BC患者膀胱活检的红外成像，包括已诊断的非转移性早期（E BC）和晚期（A BC）以及转移性晚期（M BC）膀胱癌的图像。此外，该数据集还包括来自M BC患者淋巴结（M LN）的二次肿瘤的图像。我们使用无监督聚类从红外图像中提取组织结构，创建了一个包含382个非转移性膀胱肿瘤和241个转移性肿瘤的数据集。基于此，我们接下来使用PLS-DA建立了判别模型，并使用重复随机抽样双交叉验证和置换测试进行分类。对于早期和晚期BC，基于红外膀胱活检的BC转移预测的准确率分别为83%和78%，从而证明了BC转移红外检测的概念证明。谱线分析还显示转移性膀胱和淋巴结肿瘤之间的分子成分相似性。我们还确定了与糖代谢、细胞外基质重塑和肿瘤细胞形态特征相关的LNM光谱生物标志物。我们的方法可以改善泌尿肿瘤学中的临床决策。版权所有© 2023 Elsevier B.V.发表。

The process of metastasis is complex and often impossible to be recognized in conventional clinical diagnosis. Lymph node metastasis (LNM) of bladder carcinoma (BC) is often associated with muscle-invasive tumors. To prevent and recognize LNM, the standard treatment includes radical cystectomy with lymph node dissection and histological examination. Here, we propose infrared (IR) microscopy as a tool for the prediction of LNM. For this purpose, IR images of bladder biopsies from patients with diagnosed non-metastatic early (E BC) and advanced (A BC), as well as metastatic advanced (M BC) bladder cancer were first collected. Furthermore, this dataset was complemented with images of the secondary tumors from the lymph nodes (M LN) of the M BC patients. Unsupervised clustering was used to extract tissue structures from IR images to create a data set comprising 382 IR spectra of non-metastatic bladder tumors and 241 metastatic ones. Based on that, we next established discrimination models using PLS-DA with repeated random sampling double cross-validation, and permutation test to perform the classification. The accuracy of BC metastasis prediction from IR bladder biopsies was 83 % and 78 % for early and advanced BC, respectively, herein demonstrating a proof-of-concept IR detection of BC metastasis. The analysis of spectral profiles additionally showed molecular composition similarity between metastatic bladder and lymph node tumors. We also determined spectral biomarkers of LNM that are associated with sugar metabolism, remodeling of extracellular matrix, and morphological features of cancer cells. Our approach can improve clinical decision-making in urological oncology.Copyright © 2023. Published by Elsevier B.V.