Fiche publication


Date publication

avril 2025

Journal

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

Auteurs

Membres identifiés du Cancéropôle Est :
Pr PIOT Olivier


Tous les auteurs :
Sarkees E, Taha F, Oudahmane I, Vuiblet V, Larré S, Piot O

Résumé

The increasing global incidence of bladder cancer necessitates better diagnostic methods. This study investigates the potential of mid-infrared spectroscopy on fresh urine samples as a non-invasive approach for diagnosing bladder urothelial carcinoma. In order to position our approach as close as possible to real clinical practice, urine samples were collected from patients undergoing cystoscopy in a hospital urology department. The spectral data were analysed using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Unsupervised methods did not reveal clear differences between cancerous and non-cancerous samples, supervised models, including support vector machines (SVM) and random forest (RF), were applied to classify patients into cancer and control groups. These models achieved a diagnostic accuracy of approximately 65 %, with a sensitivity of 87 % for high-grade tumours and 100 % for pT2 tumours. Despite these promising results, the overall accuracy remains insufficient for routine clinical use. The inherent variability in urine composition, influenced by factors such as diet and medications, poses challenges in identifying reliable spectroscopic markers. Nonetheless, mid-infrared spectroscopy shows a promising, non-invasive diagnostic approach for bladder cancer. Further research is essential to enhance prediction models and meet the criteria for potential clinical deployment.

Mots clés

Bladder cancer, FTIR, Machine learning, Spectral analysis, Unsupervised analysis, Urine analysis

Référence

Spectrochim Acta A Mol Biomol Spectrosc. 2025 04 19;339:126274