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The early diagnosis of esophageal cancer (EC) is extremely challenging due to a lack of effective diagnostic methods. The study presented herein aims to assess whether serum volatile organic compounds (VOCs) could be utilised as emerging diagnostic biomarkers for EC. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect VOCs in the serum samples of 55 patients with EC, with samples from 84 healthy controls (HCs) patients analysed as a comparison. All machine learning analyses were based on data from serum VOCs obtained by GC-IMS. A total of 33 substance peak heights were detected in all patient serum samples. The ROC analysis revealed that four machine learning models were effective in facilitating the diagnosis of EC. In addition, the random forests model for 5 VOCs had an AUC of 0.951, with sensitivities and specificities of 94.1 and 96.0%, respectively. [ABSTRACT FROM AUTHOR]

Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC. [ABSTRACT FROM AUTHOR]