FAIMS analysis of urine gaseous headspace is capable of differentiating ovarian cancer

Abstract

Aim: We hypothesized that field asymmetric waveform ion mobility spectrometry (FAIMS) as a novel artificial olfactory technology could differentiate urine of women with malignant ovarian tumors from controls and women with benign tumors, based on previous findings on the ability of canine olfactory system to "smell" cancer. Patients and methods: Preoperative urine samples from 51 women with ovarian tumors, both benign and malignant, and from 18 women with genital prolapse, as controls, were collected. The samples were analyzed by FAIMS device. Data analysis was processed by quadratic data analysis (QDA) and linear discriminant analysis (LDA), and cross-validated using 10-fold cross-validation. Results: Thirty-three women had malignant ovarian tumors, of which 18 were high-grade cancers. FAIMS distinguished controls from malignancies with the accuracy of 81.3 % (sensitivity 91.2 % and specificity 63.1 %), and benign tumors from malignancies with the accuracy of 77.3 % (sensitivity 91.5 % and specificity 51.4 %). Moreover, low grade tumors were also separated from high grade cancers and benign ovarian tumors with accuracies of 88.7 % (sensitivity 87.8 % and specificity 89.6 %) and 83.9 % (sensitivity 73.1 % and specificity 92.9 %), respectively. Conclusions: This proof of concept-study indicates that the FAIMS from urine has potential to discriminate malignant ovarian tumors from no tumor-bearing controls and benign tumors.

Urine Headspace Analysis with Field Asymmetric Ion Mobility Spectrometry for Detection of Chronic Kidney Disease

“…The settings of the FAIMS scanning were provided by the manufacturer. The more detailed scan procedure is described in another paper 25 , briefly: The gas (air) flow rate over the sample was 500 ml/min and this was mixed with clean air flow of 2000 ml/min, which makes a total flow of 2500 ml/min for the sensor. Dispersion field from 0 to 90% was scanned in 51 steps and compensation voltage from −6 to +6 V was scanned in 512 steps.…”

“…They used an eNose based on quartz microbalance sensors to analyze urine samples from children with kidney disease and hematuria matched with healthy controls same age. Recently, in a proof-of-concept study 25 , we applied an eNose with field asymmetric ion mobility spectrometry (FAIMS) technology to diagnose ovarian cancer from urine samples. Kidney function has not been taken into consideration in most eNose studies, yet we hypothesize that it is a considerable confounding factor.…”

Differentiation of aspirated nasal air from room air using analysis with a differential mobility spectrometry-based electronic nose: a proof-of-concept study

“…Further, an ion mobility spectrometry-based eNose was able to diagnose prostate cancer from urine with a sensitivity of 78% and a specificity of 67% [8]. Using DMS analysis of urine samples, malignant ovarian tumors were differentiated from healthy controls with a sensitivity of 91% and a specificity of 63% [9]. As reported in a review by Farraia et al [10], many published studies use exhaled breath as a sample material.…”

Fecal Volatile Metabolomics Predict Gram-Negative Late-Onset Sepsis in Preterm Infants: A Nationwide Case-Control Study

“…Different VOC measuring techniques on various sample types have been employed to detect and predict diseases over the last decade, including variants of mass spectrometry (MS), such as liquid chromatography-MS (LC-MS) and the portable electronic nose (e-nose) [ 21 ]. The generated data is often analyzed by advanced machine learning techniques, resulting in accurate detection of inflammatory bowel disease, Alzheimer’s disease, preterm birth, and several types of cancer [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Recently, VOC-evoked neuronal fingerprints generated by insect brains were used as a biological brain-based pattern-sensing technique for detection of cancer in vitro [ 31 ].…”