Advances in computational image analysis and machine learning in the field of digital pathology seem poised to make transformational changes in disease diagnosis and prognosis, yet currently 99% of all histopathology slides generated in clinical practice are never digitized. We seek to incorporate digital imaging into the standard glass-slide clinical pathology workflow using relatively inexpensive modifications to traditional clinical microscopes. Here we will introduce PathCAM, a pathology computer-assisted microscope for "ambient" digitization of histology slides during the standard pathology workflow. PathCAM leverages high-frequency spatiotemporal sampling and human-machine interaction to deliver complete and accurate digital records of clinical glass-slide analysis. We will discuss the development of the PathCAM system and technical approach, and demonstrate the unique datasets and capabilities afforded by this hybrid digital-analog pathology imaging and analysis platform.
In digital pathology, the practice of scanning slides at a single resolution results in more data than is clinically useful. In contrast, using a light microscope, the pathologist adapts magnification according to specimen content. We developed a method of digitizing the analog microscope workflows of pathologists using a microscope camera and video mosaicking with the Scale Invariant Feature Transform (SIFT). Matching this digital record to the whole slide image generates a slide interaction map over time and magnification tiers. The results of the pilot studies suggest data size can be significantly reduced without diminishing the information content required for diagnosis.
Significance: Tumor heterogeneity poses a challenge for the chemotherapeutic treatment of cancer. Tissue dynamics spectroscopy captures dynamic contrast and can capture the response of living tissue to applied therapeutics, but the current analysis averages over the complicated spatial response of living biopsy samples.
Aim: To develop tissue dynamics spectroscopic imaging (TDSI) to map the heterogeneous spatial response of tumor tissue to anticancer drugs.
Approach: TDSI is applied to tumor spheroids grown from cell lines and to ex vivo living esophageal biopsy samples. Doppler fluctuation spectroscopy is performed on a voxel basis to extract spatial maps of biodynamic biomarkers. Functional images and bivariate spatial maps are produced using a bivariate color merge to represent the spatial distribution of pairs of signed drug-response biodynamic biomarkers.
Results: We have mapped the spatial variability of drug responses within biopsies and have tracked sample-to-sample variability. Sample heterogeneity observed in the biodynamic maps is associated with histological heterogeneity observed using inverted selective-plane illumination microscopy.
Conclusion: We have demonstrated the utility of TDSI as a functional imaging method to measure tumor heterogeneity and its potential for use in drug-response profiling.
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