Development of stainless cardiac histology of clinical biopsy samples with infrared spectroscopy

Cardiac diseases are the leading cause of mortality in the United States, accounting for every one in seven deaths. There are a large proportion of cardiac diseases that need histopathological examination by pathologists for a conclusive diagnosis, but this technique hasn’t been improved upon in the past decade. In this work, we have attempted to advance the current state of histology by developing stainless staining protocols using infrared spectroscopy. The current gold standard to identify cardiovascular complications such as ischemia, fibrosis, alcoholic cardiomyopathy and transplant rejection is biopsy followed by histology. This approach lacks in many aspects. Major challenges faced by pathologists are: addressing inter-observer variability and experimental variations in stain development, and developing approaches for in-situ histopathology. Specifically, in the case of cardiac transplants, regular monitoring of the transplant is required in order to ensure that the body accepts the transplant. This is done by collecting tissue biopsies at specific time intervals. The presence of lymphocytic infiltration and accompanying fibrosis is indicative of transplant rejection. A prompt clinical action is required if rejection is identified in the biopsy. Cardiac transplant patients can benefit from techniques that can identify lymphocytic infiltration and fibrosis with high accuracy, complementing current pathology practice and giving greater opportunity to pathologists to study complex cases. In the first part of this work, we used infrared spectroscopy coupled with supervised Bayesian classification to identify lymphocytic infiltration and fibrosis in the myocardium in endomyocardial biopsy samples. This classifier was robust and could be easily applied to identify lymphocytes in the tissue and to differentiate between fibrosis in endocardium with fibrosis in myocardium which stains similarly in hematoxylin and eosin stain (H&E). Repeated biopsy procedures can cause significant trauma to the patient, and often the surgeons require real time histopathology information of the tissue during surgeries. This cannot be accomplished by traditional histology where the tissue sample needs to be excised, sectioned and stained for analysis. Since infrared spectroscopy in stainless, probe-based instruments can be developed to provide detection in-situ but were earlier limited by the speed of imaging using Fourier Transform infrared spectrometers. The problem of speed can be overcome by using quantum cascade laser-based discrete frequency infrared (DFIR) imaging instruments. In the second part of this work, we analyzed data collected on recently developed discrete frequency instruments and compared it to data collected on FT-IR imaging instruments. This was done by unsupervised data clustering to observe histological classes in both types of data. After establishing that the data collected in DFIR mode retained spectral differences between the histological classes to enable their differentiation, in part three of this work we have done extensive analysis of classification approaches that can be applied to the DFIR data. This study will be relevant to many of the previously built Bayesian classification models that need to be evaluated for their applicability on data collected in discrete frequency mode. In addition, we identified specific spectral features that could be used to differentiate between fibrosis and normal tissue in cardiac biopsy samples computationally at high speed using discrete frequency approach. This can give way to utilization of this model in fiber optic probe-based technology for on-site detection of fibrosis in patients

Quantitative chemical imaging: A top-down systems pathology approach to predict colon cancer patient survival

Colon cancer is the second deadliest cancer, affecting the quality of life in older patients. Prognosis is useful in developing an informed disease management strategy, which can improve mortality as well as patient comfort. Morphometric assessment provides diagnosis, grade, and stage information. However, it is subjective, requires multi-step sample processing, and annotations by pathologists. In addition, morphometric techniques offer minimal molecular information that can be crucial in determining prognosis. The interaction of the tumor with its surrounding stroma, comprised of several biomolecular factors and cells is a critical determinant of the behavior of the disease. To evaluate this interaction objectively, we need biomolecular profiling in spatially specific context. In this work, we achieved this by analyzing tissue microarrays using infrared spectroscopic imaging. We developed supervised classification algorithms that were used to reliably segment colon tissue into histological components, including differentiation of normal and desmoplastic stroma. Thus, infrared spectroscopic imaging enabled us to map the stromal changes around the tumor. This supervised classification achieved >0.90 area under the curve of the receiver operating characteristic curve for pixel level classification. Using these maps, we sought to define evaluation criteria to assess the segmented colon images to determine prognosis. We measured the interaction of tumor with the surrounding stroma containing activated fibroblast in the form of mathematical functions that took into account the structure of tumor and the prevalence of reactive stroma. Using these functions, we found that the interaction effect of large tumor size in the presence of a high density of activated fibroblasts provided patients with worse outcome. The overall 6-year probability of survival in patient groups that were classified as “low-risk” was 0.73 whereas in patients that were “high-risk” was 0.54 at p-value <0.0003. Remarkably, the risk score defined in this work was independent of patient risk assessed by stage and grade of the tumor. Thus, objective evaluation of prognosis, which adds to the current clinical regimen, was achieved by a completely automated analysis of unstained patient tissue to determine the risk of 6-year death. In this work, we demonstrate that quantitative chemical imaging using infrared spectroscopic imaging is an effective method to measure tumor-tumor microenvironment interactions. As a top-down systems pathology approach, our work integrated morphometry based spatial constraints and biochemistry based stromal changes to identify markers that gave us mechanistic insights into the tumor behavior. Our work shows that while the tumor microenvironment changes are prognostic, an interaction model that takes into account both the extent of microenvironment modifications, as well as the tumor morphology, is a better predictor of prognosis. Finally, we also developed automated tumor grade determination using deep learning based infrared image analysis. Thus, the computational models developed in this work provide an objective, processing-free and automated way to predict tumor behavior