Fourier transform IR imaging of primary tumors predicts lymph node metastasis of bladder carcinoma

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.

Automated summary

The process of metastasis in bladder carcinoma is difficult to recognize using conventional clinical diagnosis. In this study, infrared microscopy was proposed as a tool for predicting lymph node metastasis (LNM). Using IR images, a dataset comprising 382 IR spectra of non-metastatic bladder tumors and 241 metastatic ones was created, and discrimination models were established to classify the tumors. The accuracy of BC metastasis prediction from IR bladder biopsies was 83% and 78% for early and advanced BC, respectively. Spectral analysis revealed molecular composition similarities between metastatic bladder and lymph node tumors, and spectral biomarkers associated with LNM were identified. This approach can improve clinical decision-making in urological oncology.