# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 4 Differential Diagnosis and Disease Phenotyping and Staging in Breath Analysis
### Cancers  
Discrimination between different cancer types and cancer stages/histologies as well as between malignant and benign tumors (additionally to LC which has been mentioned earlier) using breath analysis of VOCs is also a hot research topic. Analytical techniques have been used for such applications. [PERSON] at al., for example, detected 5 VOCs and were able to differentiate BC patients from patients with abnormal mammograms and negative biopsies, with 93.8% sensitivity and 84.6% specificity [249]. [PERSON]'s group identified 21 exhaled VOCs that were significantly different between HC and patients suffering from breast benign tumors (BBT), ductal carcinoma in situ (DCIS, early-stage BC) and BC; a potentially cancer-related set of 14 VOCs that were significantly different between malignant and non-malignant patients was also identified in this study thus permitting group differentiation with 78% sensitivity and 72% accuracy [238]. In another study by the same group, the detection of GCa and the presence/absence and risk level of precancerous lesions was attempted using GC-MS so as to identify 8 VOCs statistically different between GCa and operative link on gastric intestinal metaplasia (OLGIM) groups (e.g., GCa vs. OLGIM 0-IV, GCa vs. OLGIM 0-II, GCa vs. OLGIM 0), as well as between GCa and peptic ulcer disease (PUD) and OLGIM 0-IV and PUD (\(p\)-values < 0.017) [239]. Those 8 VOCs, in different combinations, are considered to correspond to the breathprints of OLGIM groups [239]. Remarkably, respective applications of sensing devices have been extensively investigated for various cancer types. [PERSON]'s group has used NA-NOSE in order to discriminate between subjects with BC, benign breast conditions or normal mammographs, achieving increased sensitivity and specificity values for the BBT patients in comparison to the other 2 groups [116]. In another study by the same group a chemiresistor based on organically-coated Au NPs and SWCNTs was successfully used for the differentiation of BC from BBT and HC, BBT only or DCIS only, as well as for the differentiation of different molecular BC sub-groups as presented on Table 4. Larger studies are necessary, though for significant statistical results and more information to be obtained [238]. Very recently, differentiation between BC and LC has been achieved as well by [PERSON] et al. with a correct classification of 93.05% [201]. Concerning GCa, the differentiation of GCa and OLGIM groups as well as between different OLGIM stages (e.g., OLGIM 0 vs. I-II, 0 vs. I-IV, I-II vs. III-IV) has been attempted by [PERSON]'s group using the same type of chemiresistor-array and leading to high validated accuracy values in some cases (Table 4) [239]. In another promising study, [PERSON] et al. used a nanomaterial-based chemiresistor (Table 4) that permitted the successful discrimination of Gca from benign conditions along with Gca staging (early vs. late stages) [240]. Gca differentiation from gastric ulcer patients has been achieved by [PERSON] et al. with a great classification ability, using an array of commercial MOS gas sensors and various ANN types [241]. Remarkably, a molecularly modified Si NW FET, developed by [PERSON]'s group, has permitted Gca staging with an accuracy of 87% as well as Gca differentiation from LC with an accuracy of 92% [43]. More recently, discrimination of ovarian cancer (OC) from women with benign tumors and HC was achieved by [PERSON] et al. with great classification performance, both in the case of strict and most probable prediction, using again MOS sensors [242]. Notably in class prediction application, 4/23 early-stage OC patients were misclassified as benign/HC along with 2/14 OC patients with tumor size < 3 cm in cross validation phase, while in prediction phase only 1/9 early-stage patients were misdiagnosed [242]. Another common cancer type, i.e., head and neck cancer (HNC), has been studied for differential diagnosis with sensing devices. As an example, [PERSON] et al.