Abstract
Simple SummaryThe pituitary gland governs the function of nearly all endocrine glands and pituitary oncogenesis often distorts the hormonal balance. For optimal surgical cure it is crucial to discriminate pathological tissue from intact pituitary gland. Our multimodal imaging approach allows for morpho-molecular metabolic analysis and discrimination of pituitary gland and adenomas combining different complementary techniques such as optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM). Radiomics as well as analysis of spectroscopic features allows enhanced discrimination of pituitary gland and adenomas and, furthermore, classification of pituitary adenoma subtypes.Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.
Highlights
Pituitary adenomas account for 15% of all intracranial tumors [1,2,3,4] and are associated with significant morbidity and increased mortality by virtue of hormone hyperand hyposecretion, even though they show a benign behavior in the majority of cases [5]
First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on optical coherence tomography (OCT) and multiphoton microscopy (MPM) images and achieved an accuracy of 88%
All multimodal images are represented in the same way and show the correlation of the morphological, metabolic and molecular information arising from co-registered OCT and MPM images as well as the corresponding line scan Raman microspectroscopy (LSRM) images and mean spectra acquired with the multimodal platform
Summary
Pituitary adenomas account for 15% of all intracranial tumors [1,2,3,4] and are associated with significant morbidity and increased mortality by virtue of hormone hyperand hyposecretion, even though they show a benign behavior in the majority of cases [5]. Due to various feedback mechanisms, the pituitary gland regulates several other endocrine glands within the body, thereby playing a crucial role in growth, metabolism and reproductive function. This role mandates an accurate diagnostic for safe and effective disease treatment. The goal of modern pituitary surgery is to remove the adenoma entirely while preserving normal gland function. In this context, an overly cautious resection carries the risk of tumor remnants and associated recurrence [9]. Accurate three-dimensional (3D) image reconstruction and real-time intra-surgical tissue investigation is often not possible
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