Binary classification of gynecological cancers based on ATR-FTIR spectroscopy and machine learning using urine samples.

Abstract

Making an early diagnosis of cancer still in the early stages, when completely asymptomatic, is the challenge modern medicine has been setting for several decades. In gynecology, no effective screening has yet been found and approved for endometrial and ovarian cancer. Mammography is an effective screening method for Breast Cancer, as well as Pap Test for Cervical Cancer, but they are underused in third world countries because of their expensive and specific instrumentation. Previous studies showed how "machine learning analysis methods" of the spectral information obtained from dried urine samples could provide good accuracy in differentiation between healthy and ovarian or endometrial cancer. In this study, we also apply ATR-FTIR spectrometry's practical, fast, and relatively inexpensive principles to liquid urine analysis from 309 patients undergoing surgical treatment for benign or malignant diseases (endometrium, breast, cervix, vulvar and ovarian cancer). The data obtained from those liquid samples were then analyzed to train a machine learning model to classify healthy VS cancer patients. We obtained an accuracy of > 91%, and we also identified discriminant wavelengths (2093, 1774 cm^-1). These frequencies are close to already reported ones in other studies, indicating a possible association with tumor presence and/or progression.