The goal of this project is to develop image analysis tools that can be deployed as a smartphoneapplication to aid in the real time assessment of biopsy adequacy during percutaneous renal biopsy (PRB)procedures. PRB remains an essential tool for the diagnosis and treatment of patients with medical kidneydisease and involves obtaining a small needle core of tissue from the kidney with the use of a biopsy gun. Abiopsy is deemed "adequate" if sufficient renal cortical tissue is obtained to meet criteria for rendering ahistopathologic diagnosis. If insufficient tissue is obtained, or if the tissue does not contain renal cortex withenough glomeruli, then the core is deemed "inadequate". Over the last 15 years, the rate of kidney biopsies thatare inadequate for complete diagnosis due to insufficient tissue has risen to 15%, representing a significant drainon the health care system as well as a direct risk to patients who must be re-biopsied or go without an accuratediagnosis. Ideally, adequacy can be assessed at the time of biopsy by examination of the obtained cores undera microscope by a pathologist. Such real time assessment enables the biopsy physician to obtain additionalcores of tissue if existing cores are deemed to be inadequate. However, resource constraints have markedlylimited the availability of adequacy evaluation within many biopsy suites. With the widespread adoption ofsmartphones, there is now an opportunity to evaluate biopsy tissue via macro images instead of a microscope.An additional step is to build image analysis capabilities into an application on the smartphone, thus enablingthe biopsy physician to obtain reliable instantaneous feedback about whether the material they have collected issufficient or whether the biopsy "missed", and more tissue needs to be obtained. A recent study of 123,372 nativekidney biopsies found that the miss rate in PRB increased markedly from 2% in 2005 to 14% in 2020, largelyattributed to the increased involvement of radiologists performing the biopsies and their preference for smallerdiameter biopsy needles. This increase in kidney biopsy miss rate significantly impacts patient care in themanagement of medical kidney disease and highlights the need for improved tools for assessing theadequacy of tissue collected before the patient leaves the biopsy suite. In Phase I, Arkana Laboratorieswill collaborate with the Kolachalama laboratory at Boston University using biopsy cores taken from unuseddeceased donor kidneys to develop a deep neural network that can accurately classify which portions of a biopsycore represent renal cortex vs non-cortex tissue, which may include renal medulla, perirenal fat, fibrous capsule,and non-renal tissue. In Phase II, we will extend these efforts using images taken from kidney biopsies receivedby Arkana to optimize the algorithmic assessment of adequacy and then use these results to develop aprofessional version of the smartphone application that will be available to all kidney biopsy practitioners usingvirtually any smartphone camera.
Public Health Relevance Statement: PROJECT NARRATIVE. Kidney biopsy remains an essential tool for the diagnosis and treatment of patients with medical kidney disease and involves obtaining a small core of tissue from the kidney using a biopsy needle. Over the last 15 years, the rate of kidney biopsies that are deficient for diagnosis due to insufficient tissue has risen to 15%, representing a significant drain on the health care system as well as a direct risk to patients who must be re-biopsied or go without an accurate diagnosis. This project proposes to develop image analysis tools that can be deployed on smartphones to obtain high quality images of fresh biopsy tissue and determine whether the tissue is likely to be adequate for diagnosis, thereby informing the biopsy physician whether additional cores should be obtained and decreasing the patient's risk of re-biopsy.
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