A method for the comparison of multi-platform spectral histopathology (SHP) data sets

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

Is Infrared Spectroscopy Ready for the Clinic?

Fourier transform-infrared spectroscopy (FT-IR) represents an attractive molecular diagnostic modality for translation to the clinic, where comprehensive chemical profiling of biological samples may revolutionize a myriad of pathways in clinical settings. Principally, FT-IR provides a rapid, cost-effective platform to obtain a molecular fingerprint of clinical samples based on vibrational transitions of chemical bonds upon interaction with infrared light. To date, considerable research activities have demonstrated competitive to superior performance of FT-IR strategies in comparison to conventional techniques, with particular promise for earlier, accessible disease diagnostics, thereby improving patient outcomes. However, amidst the changing healthcare landscape in times of aging populations and increased prevalence of cancer and chronic disease, routine adoption of FT-IR within clinical laboratories has remained elusive. Hence, this perspective shall outline the significant clinical potential of FT-IR diagnostics and subsequently address current barriers to translation from the perspective of all stakeholders, in the context of biofluid, histopathology, cytology, microbiology, and biomarker discovery frameworks. Thereafter, future perspectives of FT-IR for healthcare will be discussed, with consideration of recent technological advances that may facilitate future clinical translation.

Spectral histopathology of the lung: A review of two large studies

This paper summarizes results from two large lung cancer studies comprising over 700 samples that demonstrate the ability of spectral histopathology (SHP) to distinguish cancerous tissue regions from normal tissue, to differentiate benign lesions from normal tissue and cancerous lesions, and to classify lung cancer types. Furthermore, malignancy-associated changes can be identified in cancer-adjacent normal tissue. The ability to differentiate a multitude of normal cells and tissue types allow SHP to identify tumor margins and immune cell infiltration. Finally, SHP easily distinguishes small cell lung cancer (SCLC) from non-SCLC (NSCLC) and provides a further differentiation of NSCLC into adenocarcinomas and squamous cell carcinomas with an accuracy comparable of classical histopathology combined with immunohistochemistry. Case studies are presented that demonstrates that SHP can resolve interobserver discrepancies in standard histopathology.

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology

This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.

Optimizing decision tree structures for spectral histopathology (SHP)

This paper reviews methods to arrive at optimum decision tree or label tree structures to analyze large SHP datasets. Supervised methods of analysis can utilize either sequential or (flat) multi-classifiers depending on the variance in the data, and on the number of spectral classes to be distinguished. For small number of spectral classes, multi-classifiers have been used in the past, but for the analysis of datasets containing large numbers (∼20) of disease or tissue types, mixed decision tree structures were found to be advantageous. In these mixed structures, discrimination into classes and subclasses is achieved via hierarchical decision/label tree structures.

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Infrared micro-spectroscopy of human tissue: principles and future promises

This article summarizes the methods employed, and the progress achieved over the past two decades in applying vibrational (Raman and IR) micro-spectroscopy to problems of medical diagnostics and cellular biology. During this time, several research groups have verified the enormous information contained in vibrational spectra; in fact, information on protein, lipid and metabolic composition of cells and tissues can be deduced by decoding the observed vibrational spectra. This decoding process is aided by the availability of computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared micro-spectral data has enabled the collection of images of cells and tissues based solely on vibrational spectroscopic data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational spectroscopy in the biological and biomedical arenas.

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Infrared imaging of high density protein arrays

We propose in this paper that protein microarrays could be analysed by infrared imaging in place of enzymatic or fluorescence labelling.

Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging

Breast cancer screening provides sensitive tumor identification, but low specificity implies that a vast majority of biopsies are not ultimately diagnosed as cancer. Automated techniques to evaluate biopsies can prevent errors, reduce pathologist workload and provide objective analysis. Fourier transform infrared (FT-IR) spectroscopic imaging provides both molecular signatures and spatial information that may be applicable for pathology. Here, we utilize both the spectral and spatial information to develop a combined classifier that provides rapid tissue assessment. First, we evaluated the potential of IR imaging to provide a diagnosis using spectral data alone. While highly accurate histologic [epithelium, stroma] recognition could be achieved, the same was not possible for disease [cancer, no-cancer] due to the diversity of spectral signals. Hence, we employed spatial data, developing and evaluating increasingly complex models, to detect cancers. Sub-mm tumors could be very confidently predicted as indicated by the quantitative measurement of accuracy via receiver operating characteristic (ROC) curve analyses. The developed protocol was validated with a small set and statistical performance used to develop a model that predicts study design for a large scale, definitive validation. The results of evaluation on different instruments, at higher noise levels, under a coarser spectral resolution and two sampling modes [transmission and transflection], indicate that the protocol is highly accurate under a variety of conditions. The study paves the way to validating IR imaging for rapid breast tumor detection, its statistical validation and potential directions for optimization of the speed and sampling for clinical deployment.

Fundamental developments in infrared spectroscopic imaging for biomedical applications

Infrared chemical imaging is a rapidly emerging field with new advances in instrumentation, data acquisition and data analysis. These developments have had significant impact in biomedical applications and numerous studies have now shown that this technology offers great promise for the improved diagnosis of the diseased state. Relying on purely biochemical signatures rather than contrast from exogenous dyes and stains, infrared chemical imaging has the potential to revolutionise histopathology for improved disease diagnosis. In this review we discuss the recent advances in infrared spectroscopic imaging specifically related to spectral histopathology (SHP) and consider the current state of the field. Finally we consider the practical application of SHP for disease diagnosis and consider potential barriers to clinical translation highlighting current directions and the future outlook.