P–337 Mid-Infrared spectroscopy as a real time diagnostic tool for chronic endometritis

Abstract

\n \n \n Can we develop a real-time diagnostic tool for chronic endometritis (CE) by using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to evaluate biopsies obtained during hysteroscopy?\n \n \n \n A discrimination model based on the absorbance data was developed by machine learning techniques, differentiating between positive and negative CE histopathology with 97% accuracy.\n \n \n \n CE is diagnosed in approximately 15% of infertile women who undergo in vitro fertilization (IVF), in 42% of women with recurrent implantation failure (RIF), and in 57.8% of women with RPL. Diagnosis is done by endometrial biopsy, and the presence of plasma cells in the endometrial stroma is the generally accepted histological diagnostic criterion. However, the histological detection of CE is time-consuming and difficult. ATR-FTIR spectroscopy is a non-destructive method that can provide valuable information on biochemical changes that occur during pathological processes, such as inflammation and cancer.\n \n \n \n We performed a prospective study in which fresh biopsies of endometrium were obtained during standard hysteroscopies. Each biopsy was examined by the spectrophotometer and afterward by histopathological analysis in which multiple myeloma oncogene 1 (MUM–1) staining for plasma cells, a marker of CE, was performed. We planned to investigate 80 samples to develop a discrimination model, and another 40 samples for validation of the model. The study was planned to last two years.\n \n \n \n Women that underwent hysteroscopy as a part of infertility evaluation were recruited. The hysteroscopies and the biopsy evaluation were performed at the same center. A cut-off of 8 MUM–1 positive cells per 10 high power fields (HPF) was set. We compared the spectroscopy analysis of the positive CE group (≥8) and the negative CE group (<8). Machine learning technique was utilized to build discrimination models. Data analysis was performed using Matlab and Unscrambler software packages.\n \n \n \n We present preliminary results for our study. Forty-two women were recruited from January 2020 until November 2020. Of the 42 measured spectra, three were discarded due to high measurement noise. Of the 39 biopsies, 33 had MUM–1<8 (CE negative group) and 6 had MUM–1≥8 (CE positive group). Measured spectra of tissue smears from CE negative and positive groups differed from each other in the spectral range of 850–990 [cm–1] (p\u2009<\u20090.05). This wavenumber can be associated with the C-H in-plane bend in the alkene group (CnH2n). A discriminant model was developed between the groups using the Principal Component Analysis and Linear Discriminant Analysis techniques. The accuracy obtained by the model was 97%.\n We divided the 39 hysteroscopies based on the CE signs into 2 groups: “Negative hysteroscopic-CE” and “Positive hysteroscopic-CE”. Positive hysteroscopic signs were micropolyps, strawberry pattern, hyperemia, punctuation, or pale endometrium. Twenty-three samples were taken in the Negative group and 16 samples were taken in the Positive group. However, measured spectra of tissue smears from negative and positive hysteroscopy groups were not significantly different. The correlation coefficient between hysteroscopy groups and MUM–1 score was r\u2009=\u20090.29, meaning that the characteristic signs of CE in hysteroscopy were not correlated to the histopathology.\n \n \n \n First, these are preliminary results and we need to investigate more samples to validate our model. Second, diagnostic criteria for CE are diverse in the literature and we chose 8 MUM–1 positive cells in 10 HPF, a criterion which may not be accepted by all experts in the field.\n Wider implications of the findings: ATR-FTIR spectroscopy is highly sensitive to molecular changes and has been utilized as a diagnostic tool in a variety of clinical studies. While histopathological results take about two weeks, ATR-FTIR spectroscopy might give us the possibility to diagnose CE in real-time, allowing an immediate initiation of the appropriate treatment.\n \n \n \n ClinicalTrials.gov Identifier: NCT04197167\n