Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath

Highlights What are the main findings? Exhaled breath was analyzed using a commercial electronic nose for early breast cancer detection. Samples were collected with no prior requirements from the patients. After feature extraction, the processed data was used for model optimization and training. What is the implication of the main finding? Accuracy, precision, and specificity of 91.0% were achieved by the model. The method could provide a path for a rapid, simple, screening technique for low-income countries.Highlights What are the main findings? Exhaled breath was analyzed using a commercial electronic nose for early breast cancer detection. Samples were collected with no prior requirements from the patients. After feature extraction, the processed data was used for model optimization and training. What is the implication of the main finding? Accuracy, precision, and specificity of 91.0% were achieved by the model. The method could provide a path for a rapid, simple, screening technique for low-income countries.Abstract Breast cancer (BC) is the most commonly occurring cancer in women and one of the leading causes of cancer death in women worldwide. BC mortality is related to early tumor detection, highlighting the importance of early detection methods. This work aims to develop a robust, accurate and highly reliable, non-invasive, low-cost screening method for early detection of BC in routine screening using exhaled breath (EB) analysis. For this, exhaled breath samples were collected from 267 women: 131 breast cancer patients and 136 healthy women. After collection, the samples were measured using a commercially available electronic nose. The signals obtained for each sample were first processed and then went through a feature extraction step. An SVM model was then optimized with respect to the accuracy matrix using a validation set by applying a Monte Carlo cross-validation with 100 iterations, with each iteration containing 20% of the data. The validation set results were 80, 94, 88, and 95% for recall, precision, accuracy, and specificity, correspondingly. Once model optimization had concluded, 22 unknown samples were analyzed by the model, and an accuracy, precision, and specificity of 91% was achieved.