Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm development

Abstract

Background: Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE), and image microarray maker (iMAM) enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA). We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves.

Methods: Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ) algorithm.

Results: Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM) appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic bodies, was subsequently carried out on the differing TMA-IMAs, with attainment of excellent discriminant classification between the two diagnostic classes.

Conclusion: The TMA-IMA construct enables and accelerates high-throughput multicase, multifield based image feature discovery and classification, thus simplifying the development, validation, and comparison of CAD algorithms in settings where the heterogeneity of diagnostic feature morphologic is a significant factor.

Keywords: CAD; IMA; SIVQ; TMA; WSI; dCORE; iMAM; image analysis.

Figure 1a

Multi-case, multi-field TMA-IMA construct for the identification of TBMs in FH and FL. dCORE was used to extract image cores consisting of representative fields of germinal centers from FH and FCL and were aggregated into a IMA using the IMAM tool

Figure 1b

3 ring vectors were then used to analyze the image with SIVQ and generate heatmaps, where the paint corresponds to the quality of matches. Ring vector 1 was selected to identify features with large cytoplasm with a white and pink interphase

Figure 1c

Ring vector 2 was selected to identify and search for apoptotic bodies with a blue-white interphase

Figure 1d

Ring vector 3 was selected to identify and search for nucleoli of macrophages (1D). When these 3 heatmaps are taken in aggregate, the visual estimate of all three sets of vector events confirmed a synergistic effect for both elevating sensitivity and specificity for TBM and apoptotic body detection