Harnessing Coded Ptychography to Deliver AI-powered Evaluation of Unstained Lung Biopsies at the Point-Of-Care

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Project Summary This Small Business Innovation Research (SBIR) Phase I project aims to develop an imaging platform to analyze lung biopsy samples at the point of care. The system will immediately digitize unstained specimens and utilize computer-assisted detection/diagnostics to enable quick and efficient evaluations of samples by pathologists. The broad, long-term objective of this proposed research is to enable real-time diagnostics of pathology samples at the point-of-care. Early diagnosis and treatment of lung cancer is essential; the survival rate is low and is highly dependent on the stage of the disease. Primary diagnoses through microscopic analysis of biopsies (FNAs) have a 20-40% failure rate due to inadequate specimens. As a result, repeat biopsies must be performed causing delays in diagnosis and treatment up to 90 days, which is enough time for cancer to upstage and can reduce survivability by as much as 20%. Rapid- onsite (ROSE) assessment of the adequacy by a pathologist can guarantee the diagnostic quality of biopsies but is performed in <10% of lung biopsies due to financial and operational barriers. In this project, we propose to overcome the historical barriers to ROSE to standardize the procedure for lung biopsies. This can be accomplished with two key innovations: (i) the Application of a novel microscopy modality (coded ptychography microscopy - CPM) for digital pathology; and (ii) imaging and automated analysis of unstained lung FNAs using Artificial Intelligence (AI) object detection algorithms. Preliminary work on thyroid FNAs indicates that CPM can produce super-resolution quantitative phase images (QPIs) with well-visualized cellularity on unstained slides. A multiwavelength compact prototype will be built and tested to optimize image quality and speed on unstained lung FNAs. After demonstrating reproducible high-quality images, object detection algorithms will be trained and validated on unstained QPIs, and will direct pathologists to areas of interest for quick analysis. Adequacy assessments made with and without AI object detection assistance will be compared to demonstrate the clinical validity. The data produced in this feasibility study will fast-track product development and serve as the foundation for clinical validation. Successful completion of this work is a key step in the long-term goal of increasing the availability of pathology services worldwide in a concerted effort to reduce cancer-associated mortality.