A validation study of whole slide imaging for primary diagnosis of lymphoma

Abstract

Whole slide imaging (WSI) is being increasingly used worldwide. Although previous studies have asserted the validity of WSI diagnosis, they have primarily targeted only small specimens and excluded cases requiring immunohistochemistry or special staining, such as lymphoma. The purpose of this study was to evaluate the accuracy of WSI diagnosis of lymphoma, for which 240 biopsies and resections of lymphoma cases were selected from the study set of lymphomas. All slides including H&E, immunohistochemical and special staining were digitized using a WSI image scanner. An experienced pathologist performed the WSI diagnoses, which were compared with original diagnoses based on light microscopic examinations. Discrepancy between the two interpretations were classified into three categories: concordance, minor discrepancy (no clinical significance), and major discrepancy (with clinical significance). Overall concordance between the light microscopic and WSI diagnosis was found in 223 cases (92.92%; 95%CI = 88.90–95.82), minor discrepancy in fifteen (6.25%; 95%CI = 3.54–10.10), and major discrepancy in two (0.83%; 95%CI = 0.10–2.98). Diagnosis of lymphoma using WSI appeared to be mostly accurate, suggesting that WSI may be a reliable technology for the diagnosis of lymphoma.

Density and size of lymphoid follicles are useful clues in differentiating primary intestinal follicular lymphoma from intestinal reactive lymphoid hyperplasia

“…However, most of previously published studies mainly targeted small specimens and excluded cases requiring immunohistochemistry, such as lymphoma cases. So far there is only one published report investigating the concordance rate between digital pathology and conventional microscopic histopathology and the researchers found that WSI was a reliable technology for lymphoma diagnosis [10].…”

Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

“…Accordingly, the acquisition of high-magnification images is generally recommended. Although evidence is lacking about diagnostic agreement at different scanning magnifications, results to date have shown less than 1% of major discordance between WSI-based and microscopic diagnoses using a basic 20 × scan [54]. Moreover, the recorded 6.25% of minor discordance was mostly due to differences in grading follicular lymphoma and was similar to the inter-and intra-observer diagnostic discordance in microscopic diagnosis [54].…”

“…Although evidence is lacking about diagnostic agreement at different scanning magnifications, results to date have shown less than 1% of major discordance between WSI-based and microscopic diagnoses using a basic 20× scan [ 54 ]. Moreover, the recorded 6.25% of minor discordance was mostly due to differences in grading follicular lymphoma and was similar to the inter- and intra-observer diagnostic discordance in microscopic diagnosis [ 54 ]. Because pathologic diagnosis of lymphoreticular neoplasms is almost always made in combination with the results of additional tests, such as immunohistochemical staining, diagnostic differences in the findings of H&E slide images alone do not seem to significantly affect diagnostic accuracy.…”

“…2) Samples clinically or morphologically suspected of lymphoreticular neoplasms: Lymphoreticular neoplasms have similar morphology at low magnification and similar nuclear features, such as chromatin patterns and nucleoli features, that are important for histologic diagnosis [5,37,48,54]. Accordingly, the acquisition of high-magnification images is generally recommended.…”

Is the Time Right to Start Using Digital Pathology and Artificial Intelligence for the Diagnosis of Lymphoma?

“…We highlight three recent articles that have broken the mold by expanding the application of WSI to include hematopathology and reflect on our recent relevant work. [ 5 6 7 ] The first article assessed the usefulness of WSI for the diagnosis of lymphoma[ 5 ] and the other publications employed deep learning with a convolutional neural network (CNN) algorithm to build diagnostic lymphoma models. [ 6 7 ]…”