The Role of Breath Analysis in the Non-Invasive Early Diagnosis of Malignant Pleural Mesothelioma (MPM) and the Management of At-Risk Individuals

Our plain-English summary of The Role of Breath Analysis in the Non-Invasive Early Diagnosis of Malignant Pleural Mesothelioma (MPM) and the Management of At-Risk Individuals, by M. Nisi, A. Di Gilio, J. Palmisani, et al (Molecules, 2025).

The study

• Authors: M. Nisi, A. Di Gilio, J. Palmisani, N. Varesano, D. Galetta, A. Catino, G. de Gennaro
• Journal: Molecules
• Published: 2025;30(19)
• Read it: PubMed · Publisher · Free full text

Abstract

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy associated with occupational or environmental exposure to asbestos. Effective management of MPM remains challenging due to its prolonged latency period and the typically late onset of clinical symptoms. Accordingly, there is an increasing demand for the implementation of reliable, non-invasive, and data-driven diagnostic strategies within large-scale screening programs. In this context, the chemical profiling of volatile organic compounds (VOCs) in exhaled breath has recently gained recognition as a promising and non-invasive approach for the early detection of cancer, including MPM. Therefore, in this cross-sectional observational study, an overall number of 125 individuals, including 64 MPM patients and 61 healthy controls (HC), were enrolled. End-tidal breath fraction (EXP) was collected directly onto two-bed adsorbent cartridges by an automated sampling system and analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC/MS). A machine learning approach based on a random forest (RF) algorithm and trained using a 10-fold cross-validation framework was applied to experimental data, yielding remarkable results (AUC = 86%). Fifteen VOCs reflecting key metabolic alterations characteristic of MPM pathophysiology were found to be able to discriminate between MPM and HC. Moreover, twenty breath samples from asymptomatic former asbestos-exposed (AEx) and eight MPM patients during follow-up (FUMPM) were exploratively analyzed, processed, and tested as blinded samples by the validated statistical method. Good agreement was found between model output and clinical information obtained by CT. These findings underscore the potential of breath VOC analysis as a non-invasive diagnostic approach for MPM and support its feasibility for longitudinal patient and at-risk subjects monitoring.

In plain English

This cross-sectional observational study, done in Bari, Italy, from 2021 to 2024, tested whether chemicals in exhaled breath could separate people with malignant pleural mesothelioma (MPM) from healthy people. The authors analyzed breath from 64 MPM patients and 61 healthy controls, aged 39 to 84, for volatile organic compounds. A machine-learning model using 15 top-ranked compounds reached an average classification accuracy of 80% and an area under the ROC curve of 0.86. The model was then applied blind to breath from 20 healthy people previously exposed to asbestos. The one person scored as likely MPM, with a probability of 0.8, was later diagnosed with MPM on CT. Five samples scoring below 0.5 came from people whose CT showed pleural plaques, which the authors called likely pre-cancerous. Eight MPM patients were retested after chemotherapy. The two who scored above 0.5 had disease progression on CT, and the others had stable disease. The authors concluded that breath analysis shows potential for early MPM diagnosis. They said the small at-risk and follow-up groups need validation in larger cohorts.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: multicenter cross-sectional observational study of exhaled-breath chemicals (volatile organic compounds), with a machine-learning (random forest) classifier, plus an exploratory blinded test in asbestos-exposed subjects and MPM patients in follow-up
• Who was studied: 125 volunteers aged 39 to 84 years: 61 healthy controls and 64 patients with malignant pleural mesothelioma, recruited with the Thoracic Oncology Unit of the Cancer Institute of Bari, Italy, 2021 to 2024; additionally 20 healthy asbestos-exposed subjects and 8 MPM patients resampled in follow-up
• Exposure: The study measured breath compounds, not asbestos itself. An extra group of 20 healthy people with past asbestos exposure was tested. Fiber types, jobs and doses were not reported.
• Main results:
  • Using the top 15 ranked breath compounds was enough to reach the model's best predictive accuracy in separating MPM patients from healthy controls
  • The final classifier had an average classification accuracy of 80% for MPM patients versus healthy controls
  • Area under the ROC curve for distinguishing MPM patients from healthy controls was 0.86
  • Among the 20 asbestos-exposed subjects tested blind, sample no. 17 was classified as pathological with a probability of 0.8 and was later diagnosed with MPM on CT
  • Among the asbestos-exposed subjects, five breath samples with probability scores below 0.5 came from people whose CT showed pleural plaques, which the authors described as likely a pre-cancerous condition
  • Almost all asbestos-exposed samples the model classified as controls were confirmed healthy by further tests such as CT
  • Among 8 MPM patients resampled after chemotherapy, patients no. 1 and no. 8 scored above 0.5 for MPM and were in disease progression on CT, while all others had stable disease
• The authors' conclusion: The authors concluded that breath analysis combined with machine-learning classification shows potential as a tool for early diagnosis of MPM, and that the exploratory results support the feasibility of breath-based longitudinal monitoring
• Limitations the authors state:
  • The study population was small, particularly the at-risk asbestos-exposed group and the MPM follow-up group
  • The follow-up findings were exploratory and need validation in larger and independent cohorts

Topics

The National Library of Medicine indexes this study under subjects our site covers: Mesothelioma.

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Environmental Exposure, Latency.

Related studies

• Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure — G. de Gennaro, S. Dragonieri, et al. (2010)
• Exhaled Breath Analysis in Diagnosis of Malignant Pleural Mesothelioma: Systematic Review — Z.N. Töreyin, M. Ghosh, et al. (2020)
• Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways — A. Di Gilio, A. Catino, et al. (2020)
• Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma — A. Catino, G. de Gennaro, et al. (2019)
• Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types — F. Pennisi, S. Borlini, et al. (2025)
• Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts — L.D. Little, S.E. Barnett, et al. (2024)

Browse more research in Molecules, or return to medical research.