After skin cancer, prostate cancer (PCa) is the most frequently diagnosed malignancy in American men (1). The risk of disease progression and adverse outcomes varies broadly based on clinicopathologic characteristics (2). Hence, accurate disease risk stratification is paramount to align the aggressiveness of management to the severity of disease. PCa is commonly stratified as low-, intermediate-, or high-risk, as first proposed by D’Amico et al. (3). Other risk stratification models have also been described (4–6). Prostate biopsy is a component of all such models. However, it is invasive and frequently underestimates pathological data due to grading and sampling errors, and borderline grades (7). An accurate and non-invasive approach would be a valuable enhancement to the currently available armamentarium. It has been demonstrated that highly trained dogs detect PCa-specific Volatile Organic Compounds (VOCs) in urines (8–10). To harness the potential of PCa-specific VOCs, an electronic nose (eNose), which mimics dogs’ capability to detect and recognize odors, was designed for widespread utilization in clinical settings (10–13), achieving a sensitivity of 85.2% and a specificity of 79.1% in diagnosing PCa (10). Recent studies indicate that analyzing urinary VOCs is a promising approach for discover new biomarkers for early cancer detection. Alterations in urinary VOCs have been linked to pathological conditions like tumors and infections. Various research has demonstrated the eNose ability to detect these changes in urine, successfully identifying various human neoplasia, such as prostate (10, 14), bladder (15), kidney (16), breast (17), lung (18) and colorectal (19) cancers.

In a recent study involving 252 participants, including 110 renal patients and 142 healthy individuals, urine samples were analyzed using a commercially available eNose (16). The device achieved a specificity of 89.4%, sensitivity of 71.8%, positive predictive value of 84.04%, and negative predictive value of 80.37% (16). Similarly, Filianoti et al. (14) applied the same technology to differentiate PCa patients from healthy controls. The study demonstrated a sensitivity of 82.7%, specificity of 88.5%, positive predictive value of 87.3%, and negative predictive value of 84.2%. These findings reinforce the potential of urine volatilome profiling with an eNose as a non-invasive diagnostic method. Matsumoto and colleagues (15) analyzed urine samples from 36 bladder cancer patients, 29 with urolithiasis, 10 with urinary tract infections, and 27 healthy volunteers. Using ROC analysis, they established optimal cut-off values for bladder cancer detection at θ=49 for healthy volunteers, θ=48 for urolithiasis, and θ=55 for urinary tract infections. Significant differences were noted between bladder cancer and other conditions at these thresholds (15). The diagnostic sensitivity was 61.4%, 45.6%, and 60.8%, while specificity was 52.8%, 68.4%, and 90.2%, respectively (15). These results support the use of the eNose as a cost-effective, non-invasive tool for distinguishing bladder cancer from benign conditions. Overall, the current findings confirm the eNose efficacy as a non-invasive, easy-to-use, and rapid response method for detecting disease-specific VOC patterns in both neoplastic and non-neoplastic diseases. It is important to emphasize that the eNose doesn’t furnish details about the chemical composition of samples; instead, it characterizes their overall VOC patterns. The aim of the present study was to evaluate the capability of risk stratification of PCa subjects using an eNose detecting PCa-specific VOCs in urine samples.

Comparing the eNose with the PG after RARP, the eNose achieved an accuracy of 79% (95% confidence interval [CI]: 71 – 86%). When comparing the eNose with D’Amico risk-classification, the eNose exhibited an accuracy of 74% (95% CI: 65 – 82%). We also examined whether intermediate-risk PCa could be categorized as either low- or high-risk, assessing two scenarios: a) low- versus intermediate/high-risk, and b) low/intermediate- versus high-risk. When categorizing low-risk versus intermediate/high-risk PCa, the eNose demonstrated an accuracy of 88% (95% CI: 80 - 93%) when compared to the PG. Conversely, based on the D’Amico risk-classification, the eNose achieved an accuracy of 91% (95% CI: 84 - 95%).

When grouping low/intermediate- versus high-risk PCa, the eNose showed an accuracy of 92% (95% CI: 85 - 96%) when compared with the PG, while an accuracy of 81% (95% CI: 73 - 87%) was found when compared with D’Amico risk-classification. Table 2 summarizes all these findings, including the recall ratio. Cohen’s κ-coefficient revealed a substantial level of agreement between eNose and the PG of 0.7 (95% CI: 0.49 - 0.84, p < 0.0001), and between eNose and D’Amico risk-classification of 0.6 (95% CI: 0.42 - 0.78, p < 0.0001). Furthermore, there was an agreement between eNose outcomes and the other risk-stratification models ( Figure 2 ). The agreement between eNose and ISUP was 0.3 (95% CI: 0.15 - 0.51, p < 0.0003), while the agreement between eNose and CAPRA was 0.4 (95% CI: 0.19 - 0.55, p < 0.0001), and the comparison with NCCN yielded an agreement of 0.4 (95% CI: 0.23 - 0.59, p < 0.0001).

At present, it remains unclear which tool is the most effective for stratifying patients with PCa, but the risk of overdiagnosis and overtreatment persists in all approaches (7, 23, 24). The current scoring systems (3–6) rely on subjective features except for age, and all required a biopsy sampling. The Gleason score is a useful indicator of tumor aggressiveness, and a close correlation between biopsy and pathological specimens would be beneficial. However, biopsies frequently underestimate pathological data with discrepancies ranging from 27% to 46% (25). Prostate-specific antigen (PSA) has been found to have poor accuracy in predicting the severity of PCa. Despite improvements, multiple studies have also demonstrated significant variability in the quality of magnetic resonance imaging (MRI) scans and that patient-related factors can introduce errors (26, 27). Considering these challenges, a growing need exists for objective, accurate, non-invasive and cost-effective methods in selecting patients with PCa before an eventual diagnostic biopsy. In this study, we evaluated the eNose’s capability to stratify the risk of PCa. Our findings demonstrated that the eNose exhibits a high level of accuracy and agreement when compared to the PG, which is considered to closely represent the real state of the disease. Remarkably, the eNose also demonstrates an agreement with D’Amico and other risk-stratification models, possibly eliminating the need for invasive biopsy sampling. Additionally, in this current study, as no exclusion criteria were established regarding medical history, alcohol consumption, drugs, diet, tobacco, or other habits, it can be inferred that our results are not influenced by potential confounding variables. The accuracy of the technology is primarily dependent on the PCa-specific VOCs in urine samples, a finding that is corroborated by other studies (11, 28). Collectively, these initial findings indicate that eNose may have a significant role in classifying patients before resorting to costly and invasive procedures. This potential application could alleviate the financial strain on healthcare systems and reduce the risks for individuals under suspicion of having PCa. The tool could be further refined to serve as the primary screening method, potentially leading to a series of costly and invasive tests, contingent on the patient’s age and clinical condition. In fact, if the eNose indicates a potential presence of low-risk tumors, it could prompt a reevaluation of the entire subsequent diagnostic process. Additionally, the eNose may be effectively employed for longitudinal monitoring of patients over time, with minimal costs and in a non-invasive manner. After validation in a wider population and the refinement of pattern recognition models through additional targeted training, this device could be readily employed for detecting various neoplastic or non-neoplastic diseases. It is known that the methods used for feature extraction and training pattern recognition models play a critical role in enhancing the performance of the eNose system (29, 30). It is worth noting that a limitation of our study lies in the relatively small size of the investigated population. To enhance and confirm the efficacy of the eNose, a forthcoming multicenter study is needed, encompassing a larger and more diverse patient cohort. Although the eNose shows promising results, it is also crucial to acknowledge the existence of technical challenges. Furthermore, comparing the results of the eNose with MRI interpretations using the Prostate Imaging-Reporting and Data System (PI-RADS) presents an additional challenge. This comparison aims to determine whether there is a concordance between eNose outcomes and MRI, which remains the primary diagnostic test for men suspected of having PCa.

This preliminary study has shown that eNose represents a valuable tool not only to diagnose PCa but also to stratify its aggressiveness. The future direction will enhance the eNose risk-stratification model to provide tailored responses based on different risk-stratification models, enabling urologists to use it according to their personal practices.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by IRCCS Humanitas Research Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

GTa: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. FG: Data curation, Writing – original draft, Writing – review & editing. CB: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. LT: Writing – review & editing. MZ: Writing – review & editing. PV: Writing – review & editing. CM: Writing – review & editing. MC: Writing – review & editing. GTo: Writing – review & editing. MH: Data curation, Formal analysis, Methodology, Writing – review & editing. BL: Data curation, Formal analysis, Methodology, Writing – review & editing. RH: Writing – review & editing. LC: Conceptualization, Supervision, Writing – original draft, Writing – review & editing.