Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Background:
Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study.
Methods:
A prospective, single-center, controlled, non-randomized, phase 2 study was carried out...
Citations
... Various sensor-based electronic noses have been used in recent years to complement GC-MS for the detection and differentiation of various types of cancers, including gastric cancer [13][14][15][16][17]. Different self-powered respiration sensors and wearable biosensors are also used for non-invasive chemical breath analysis and physical respiratory motion detection [18][19][20][21][22]. ...
Background:
Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters).
Methods:
We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests.
Results:
The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity.
Conclusions:
The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.
... A good urinary bladder cancer marker should have the following characteristics: technically simple, high specificity and high sensitivity, good reliability and reproducibility, and low cost. The paper by Bassi et al. is a pilot study about the possible ability of a simple device, such as the electronic nose, to detect bladder cancer by the evaluation of specific volatile organic compounds in the gas emitted from urine samples [2]. Preliminary data are interesting, but obviously this is the beginning and further studies are required. ...
Bladder cancer (BC) is a complex disease with the following presentations, which are completely different from one another: non-muscle-infiltrating bladder cancer (NMIBC) and muscle-infiltrating bladder cancer (MIBC) [...]
Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC at an early stage and maintain postoperative surveillance through ″smelling″ clinical urine samples at room temperature. In clinical trials, 18 healthy controls and 76 BLC patients (60 and 16 at early and advanced stages, respectively) are assessed by the artificial olfactory system. With the assistance of a support vector machine (SVM), very high sensitivity and accuracy from healthy controls are achieved, exceeding those obtained by the current techniques in practice. In addition, the recurrences of both early and advanced stages are diagnosed well, with the effect of confounding factors on the performance of the artificial olfactory system found to have a negligible influence on the diagnostic performance. Overall, this study contributes a novel, noninvasive, easy-to-use, inexpensive, real-time, accurate method for urine disease diagnosis, which can be useful for personalized care/diagnosis and postoperative surveillance, resulting in saving more lives.
