Skin cancer diagnosis using infrared microspectroscopy imaging as a molecular pathology tool

Abstract

Over the past decades, Fourier Transform Infrared (FTIR) microspectroscopy has emerged as a potential candidate to complement Histopathology in the study and diagnosis of tissue diseases. Contrary to the histological examination, which relies on the morphological tissue alterations assessed by visual inspection of stained samples, FTIR chemical imaging is a rapid and label-free tool that provide simultaneously information about histological structures as well as the localisation and magnitude of basic molecular units that compose tissue sections (proteins, nucleic acids, lipids, and carbohydrates). Despite the many proof-of-concept studies demonstrating the effectiveness of FTIR spectroscopy in detecting biological disorders with high levels of sensitivity and specificity, translation into clinical practice has been relatively slow due to the substantial cost of infrared transparent substrates required to collect the images. Thus, the main objective of this research is to evaluate the diagnostic potential of infrared chemical images collected from samples placed on conventional histology glass slides as alternative substrates for FTIR spectroscopy. Swiss mice were submitted to a well-established chemical carcinogenesis protocol, in which cancerous and non-cancerous cutaneous lesions were obtained by varying the exposure time of the animals to carcinogenenic factors. FTIR hyperspectral images were acquired in transmission mode over the mid-infrared region from tissue specimens placed on conventional infrared substrates (calcium fluoride - CaF2) and glass slides. In the first phase of our study, spectral datasets were segmented using k-means (KMCA) and Hierarchical Cluster Analysis (HCA) as clustering algorithms to reconstruct the hyperspectral images aiming to evaluate the ability of the false-color maps in reproducing the histological structures of tissue specimens. The images were segmented by each clustering technique using several different combinations varying parameters including the substrate used to place the samples (CaF2 or conventional glass) and the methods employed to preprocess the datasets. Fingerprint (1000-1800 cm-1) and high wavenumber (3100-4000 cm-1) regions from images collected on CaF2 were separately used as input for image reconstruction and only the high wavenumber range was employed in the case of samples placed on glass. All pseudocolor maps were compared to standard histopathology in order to evaluate the quality and consistency of images after segmentation. KMCA presented slightly superior ability in correctly assigning the pixels of morphochemical maps to the histological structures of the specimen, nevertheless, our findings indicate that the choice of the substrate, input data, preprocessing methods, and sample preparation have more influence in the final results than the clustering algorithm used to reconstruct the images. In the second phase of our study, Principal Component Analysis (PCA) was employed to compare datasets from healthy group to animals exposed to chemicals for 8, 16, and 48 weeks in order to evaluate the biochemical changes induced by chemical carcinogenesis. The performance of classification in each pairwise comparison was calculated using a binary classification test based on Linear Discriminant Analysis associated to PCA (PC-LDA). The method achieved satisfactory discrimination (over 80%) comparing healthy tissue to samples that were classified as papilloma (16 weeks) and invasive squamous cell carcinoma (48 weeks) regardless of the substrate used to place the samples. Statistical measurements obtained comparing healthy skin to animals exposed to carcinogenic factors for 8 weeks (free of malignancy based on the morphological and clinical evidence) ranged from 35-78%, indicating that the ability of PC-LDA in correctly classifying spectral data from cancerous and pre-cancerous lesions vary with the stage of the disease during the tumorigenesis process. Thus, as a proof-of-concept, we demonstrate the feasibility of FTIR spectroscopy in evaluating the biological events triggered by cancer using a label-free methodology that do not rely on expensive substrates and do not disrupt the pathologist workflow. This is a major step forward towards clinical application, since the method can be used to complement the diagnostic process of cancer as a non-subjective alternative that do not require laborious and time-consuming procedures nor expensive probes as biomarkers.