Optimization of Complex Cancer Morphology Detection Using the SIVQ Pattern Recognition Algorithm

For personalization of medicine, increasingly clinical and demographic data are integrated into nomograms for prognostic use, while molecular biomarkers are being developed to add independent diagnostic, prognostic, or management information. In a number of cases in surgical pathology, morphometric quantitation is already performed manually or semi-quantitatively, with this effort contributing to diagnostic workup. Digital whole slide imaging, coupled with emerging image analysis algorithms, offers great promise as an adjunctive tool for the surgical pathologist in areas of screening, quality assurance, consistency, and quantitation. We have recently reported such an algorithm, SIVQ (Spatially Invariant Vector Quantization), which avails itself of the geometric advantages of ring vectors for pattern matching, and have proposed a number of potential applications. One key test, however, remains the need for demonstration and optimization of SIVQ for discrimination between foreground (neoplasm- malignant epithelium) and background (normal parenchyma, stroma, vessels, inflammatory cells). Especially important is the determination of relative contributions of each key SIVQ matching parameter with respect to the algorithm’s overall detection performance. Herein, by combinatorial testing of SIVQ ring size, sub-ring number, and inter-ring wobble parameters, in the setting of a morphologically complex bladder cancer use case, we ascertain the relative contributions of each of these parameters towards overall detection optimization using urothelial carcinoma as a use case, providing an exemplar by which this algorithm and future histology-oriented pattern matching tools may be validated and subsequently, implemented broadly in other appropriate microscopic classification settings.