Analysis of application of digital image analysis in histopathology quality control

A correct histopathological diagnosis is dependent on an array of technical variables. The quality and completeness of a histological section on a slide is extremely prudent for correct interpretation. However, this is mostly done manually and depends largely on the expertise of histotechnician. In this study, we analysed the application of digital image analysis for quality control of histological section as a proof-of-concept. Images of 1000 histological sections and their corresponding blocks were captured. Area of the section was measured from these digital images of tissue block (Digiblock) and slide (Digislide). The data was analysed to calculate DigislideQC score, dividing the area of tissue on the slide by the tissue area on the block and it was compared with the number of recuts done for incomplete section. Digislide QC score ranged from 0.1 to 0.99. It showed an area under curve (AUC) of 98.8%. A cut-off value of 0.65 had a sensitivity of 99.6% and a specificity of 96.7%. Digiblock and Digislide images can provide information about quality of sections. DigislideQC score can correctly identify the slides which require recuts before it is sent for reporting and potentially reduce histopathologists’ slide screening effort and ultimately turnaround time. These can be incorporated in routine histopathology workflows and lab information systems. This simple technology can also improve future digital pathology and telepathology workflows. •The purpose the of the present study was to identify whether digital image analysis can be used for histopathology quality control.•Our objectives were to measure and compare the area of tissue section on slide (Digislide) with that of the paraffin block (Digiblock) using image analysis. A simple tool was developed to capture the images for analysis.•One thousand tissue blocks with its corresponding slides were analysed. DigislideQC score which is a ratio of area of Digislide and Digiblock was calculated for each slide. DigislideQC score was found to have a sensitivity of 99.6% and specificity of 96.7%, area under the curve 98.8% for a cut-off value of 0.65.•It can prevent submission of suboptimal slides for reporting and reduce turnaround time.•It can be easily incorporated with routine histopathology workflows and lab information systems. This simple technology can benefit future digital workflows, reporting, and telepathology.