Identification of Prostate Cancer Using Laser-Induced Breakdown Spectroscopy Combined with Machine Learning

Prostate cancer is a primary cause of cancer-related deaths in men which requires its rapid and accurate identification to improve treatment outcomes. This research aimed to develop an innovative, accurate and non-invasive diagnostic framework for the early detection of prostate cancer by combining laser-induced breakdown spectroscopy (LIBS) with machine learning. Prostate cancer and healthy samples were examined using LIBS to analyze their spectra. Elements like calcium, nitrogen and sodium along with the CN-band showed a higher concentration in cancer samples as compared to healthy ones. Various machine learning models were trained to accurately discriminate between cancer and healthy samples. Among several machine learning models, a trilayered neural network achieved the highest training accuracy of 85.0% and the linear SVM model achieved the highest prediction accuracy of 80.0%. Compared to other traditional methods, LIBS emerged as a robust and reliable technique. This study demonstrated the strong potential of combining LIBS with machine learning for the detection of prostate cancer. The technique not only enhanced the diagnostic accuracy but also reduced the need for invasive procedures showing promise for broader medical use.