A new approach to evaluate veterinary histopathological tissue sample with image and ultrasound data

In many proliferative lesions, arising from superficial and deep tissues, macroscopic and micro-scopic evaluations are essential in the diagnosis (e.g. hypertrophy, hyperplasia, dysplasia or neo-plasia). The term virtual biopsy is gaining ground. This would transform the biopsy from a physi-cal and invasive act to a virtual act, while still providing very precise diagnostic and prognostic in-formation, always considering the traditional physical biopsy as the gold standard. A innovative rotary stage-based scanning system and a manual ultrasound platform were used to obtain, respec-tively, image and ultrasound data of tissue lesions, fixed in buffered formalin and used for classical histopathological examination. Fifty-nine samples (49 dogs, 9 cats, 1 rodent) of various fixed pathological tissues were analyzed as follows: 33 skin, 9 subcutaneous, 9 mammary, 3 oral muco-sal, 2 intestinal, 1 renal, 1 bladder, and 1 splenic lesions. Image data were collected with the cus-tomized semi-automatic scanning platform, and ultrasound data from the same neoformations were collected using the custom-assembled ultrasound platform. The morphometric findings acquired by the device were then compared with manual measurements of the same masses, using a caliper. Finally, the histological section of the proliferate tissue, mirroring the one scanned by the device, was microscopically evaluated by expert pathologists, and a correct diagnosis was issued. The set of numerical data collected by the device was then compared with the morphometric data collect-ed by the pathologist, to interpret the examined mass in terms of cellular damage and proliferative pattern. A 16 MHz high-frequency ultrasound probe was used to collect data, which allowed for greater resolution and more precise measurement of the target tissues. The quality of the collected data was assessed based on the clear discernibility of a reflection peak in the received ultrasound signal. A link between ultrasound and image data obtained from the camera can be established by identifying the scanner image whose position is closest to the ultrasound image. Finally, a data ar-chive was created to process and visualize the obtained data set. Results show interesting indica-tions that can be extrapolated from the data set, allowing to identify, based on the specific acoustic properties of the examined tissue or on the depth of the evaluated area, high quality signals that in the case of neoplasms allowed to distinguish the pathological areas from the healthy ones. Alt-hough preliminary, these data demonstrate that potential surrogates of macro-and microscopic as-sessments of tissue proliferations can be pursued through the new imaging metrics. [1] N. Brancati, A.M. Anniciello, P. Pati, D. Riccio, et al, “Bracs: A dataset for breast carcinoma subtyping in h&e histology images”, Database (Ox-ford), 2022, baac093. [2] O. Gemeinhardt, F.G. Poch, B Hiebl, U. Kunz-Zurbuchen, et al, “Comparison of bipolar radiofrequency ablation zones in an in vivo porcine mod-el: Correlation of histology and gross pathological findings”, Clin. Hemorheol. Microcirc, vol 64, pp 491-499, 2016. [3] S.A. Goss, R.L. Johnston and F. Dunn, “Comprehensive compilation of empirical ultrasonic properties of mammalian tissues”, The J. Acoust. Soc. Am, vol 64, pp. 423–457, 1978 [4] J. Hau, “Animal models for human diseases: an overview”, Sourceb. models for biomedical research, pp. 3–8, 2008. [5] A. Suvarna, R. Vempati, R. Chacko, G. Srinivasan, et al, “DeltaAI: Semi-autonomous tissue grossing measurements and recommendations using neural radiance fields for rapid, complete intraoperative histological assessment of tumor margins”, bioRxiv preprint doi: https://doi.org/10.1101/2023.08.07.552349, 2023.