Prostate cancer (PCa) is one of the most common cancers in men worldwide. Early diagnosis of PCa is extremely challenging due to the lack of effective diagnostic methods. The study presented here aims to evaluate whether urine volatile organic compounds (VOCs) can be used as an emerging diagnostic biomarker for PCa. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect VOCs in urine samples from 66 patients with PCa and to comparatively analyze samples from 87 patients with non-cancerous controls (NCs). A total of 86 substance peak heights were detected in urine samples from all patients. Analysis using four machine learning algorithms suggested that the diagnosis of PCa could be effectively facilitated. Ultimately, diagnostic models were constructed based on the four VOCs selected. The AUC for the RF and SVM model were 0.955 and 0.981, respectively. Both the NN and DT diagnostic models also achieved an AUC of 0.8 or more, but their sensitivity or specificity was poor compared to the RF and SVM models.
Keywords: Diagnosis; Prostate cancer; Urine; Volatile organic compounds.
© 2023 Published by Elsevier Ltd.