Annual Research Roundup

It’s that time of year when we bring research to you. We did the heavy lifting, scouring multiple peer-reviewed journals to find papers about the topics important to you. Let’s get reading! (Curious which papers were hot last year? Check out 2020’s research roundup.)

Digital Pathology

Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images. Lashen, A. et al. (2021) Histopathology 79, 913– 925.

Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging: A Multisite Observer Study. Marios A. Gavrielides et al. Arch Pathol Lab Med 1 December 2021; 145 (12): 1516–1525.

Validation of a Portable Whole-Slide Imaging System for Frozen Section Diagnosis. Kaushal RK et al. J Pathol Inform. 2021;12:33. Published 2021 Sep 16.

Molecular Testing & Laboratory Medicine

Eosinophilic vacuolated tumor (EVT) of kidney demonstrates sporadic TSC/MTOR mutations: next-generation sequencing multi-institutional study of 19 cases. Farcaş, M., Gatalica, Z., Trpkov, K. et al. Mod Pathol (2021).

Factors Impacting Clinically Relevant RNA Fusion Assays Using Next-Generation Sequencing. Nisha S. Ramani et al. Arch Pathol Lab Med 1 November 2021; 145 (11): 1405–1412.

Comparative analysis of nuclear and mitochondrial DNA from tissue and liquid biopsies of colorectal cancer patients. Haupts A, Vogel A, Foersch S, et al. Sci Rep. 2021;11(1):16745. Published 2021 Aug 18.

Tumor Microenvironment Profiles Reveal Distinct Therapy-Oriented Proteogenomic Characteristics in Colorectal Cancer. Wang N, Wang R, Li X, et al. Front Bioeng Biotechnol. 2021;9:757378. Published 2021 Oct 28.

Artificial Intelligence/Machine Learning/Deep Learning

Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Farahmand, S., Fernandez, A.I., Ahmed, F.S. et al. Mod Pathol (2021).

Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Wu, J., Liu, C., Liu, X. et al. Mod Pathol (2021).

Quality control stress test for deep learning-based diagnostic model in digital pathology. Schömig-Markiefka, B., Pryalukhin, A., Hulla, W. et al. Mod Pathol (2021).

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis. Azam AS, Miligy IM, Kimani PK, et al. J Clin Pathol. 2021;74(7):448-455.

Useful Review Articles

Integrating digital pathology into clinical practice. Hanna, M.G., Ardon, O., Reuter, V.E. et al. Mod Pathol (2021).

Digital pathology and artificial intelligence in translational medicine and clinical practice. Baxi, V., Edwards, R., Montalto, M. et al. Mod Pathol (2021).

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective. Browning L, Colling R, Rakha E, et al. J Clin Pathol. 2021;74(7):443-447.

Use of whole slide imaging (WSI) for distance teaching. Evans AJ et al. J Clin Pathol. 2021;74(7):425-428.

Here’s to a year of great research and we look forward to featuring more research in the future.

Instapath was founded in 2017 by the same engineers and scientists who developed the original prototypes. Our vision is to enable patients to immediately know their cancer diagnosis instead of waiting days or weeks for the results. Instapath builds microscopy platforms to improve patient care in the form of faster turnaround times and prevention of high risk and costly repeat biopsy procedures. Further, our goal is to provide users with a seamless, modernized digital pathology workflow with tools to complete all pathology evaluations needed to provide the most precise and efficient diagnoses for patients.

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