Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Abstract

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900–1425 cm−1, 1475–1710 cm−1, 2800–3000 cm−1, and 3090–3700 cm−1, to be modeled and detected respectively. The final results showed that the ranges 900–1425 cm−1 and 1475–1710 cm−1 had superior classification accuracies. The region 900–1425 cm−1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475–1710 cm−1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.