Welcome to our first Quarterly Research Roundup! In addition to the end-of-year Research Roundup we brought you in 2020 and 2021, each quarter we will be serving up the hottest papers about the topics important to you. Let’s get reading!
Milross L, et al. Post-mortem lung tissue: the fossil record of the pathophysiology and immunopathology of severe COVID-19. Lancet Respir Med. 2022;10(1):95-106. https://doi.org/10.1016/S2213-2600(21)00408-2
Clarke E, et al. Faster than light (microscopy): superiority of digital pathology over microscopy for assessment of immunohistochemistry. J Clin Pathol. 2022 Jan 17. https://doi.org/10.1136/jclinpath-2021-207961
Mutter GL, et al. Measuring Digital Pathology Throughput and Tissue Dropouts. J Pathol Inform. 2022;13:8. Published 2022 Jan 8. doi:10.4103/jpi.jpi_5_21
Salama AM, et al. Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners. Mod Pathol 35, 52–59 (2022). https://doi.org/10.1038/s41379-021-00908-5
Krishnamurthy S, et al. Feasibility of using digital confocal microscopy for cytopathological examination in clinical practice. Mod Pathol 35, 319–325 (2022). https://doi.org/10.1038/s41379-021-00925-4
Artificial Intelligence/Machine Learning/Deep Learning
McKay F, et al. The ethical challenges of artificial intelligence-driven digital pathology. J Pathol Clin Res. 2022 Feb 17. https://doi.org/10.1002/cjp2.263.
McAlpine E, et al. Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology, Journal of the American Society of Cytopathology, 2022, https://doi.org/10.1016/j.jasc.2022.02.001
Nguyen HG, et al. Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer. Mod Pathol 35, 240–248 (2022). https://doi.org/10.1038/s41379-021-00894-8
Shaban M, et al. A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma. J. Pathol. 2022, 256: 174-185. https://doi.org/10.1002/path.5819
Schrammen PL, et al. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol. 2022, 256: 50-60. https://doi.org/10.1002/path.5800
Hondelink LM, et al. Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer. Histopathology 2022; 80, 635– 647. https://doi.org/10.1111/his.14571
Molecular Testing & Laboratory Medicine
Ronchi A, et al. Diagnostic performance of melanocytic markers for immunocytochemical evaluation of lymph-node melanoma metastases on cytological samples, Journal of Clinical Pathology 2022;75:45-49. https://jcp.bmj.com/content/75/1/45.long
Salas C, et al. Real-world biomarker testing rate and positivity rate in NSCLC in Spain: Prospective Central Lung Cancer Biomarker Testing Registry (LungPath) from the Spanish Society of Pathology (SEAP), Journal of Clinical Pathology 2022;75:193-200. https://jcp.bmj.com/content/75/3/193.abstract
Omilian AR, et al. Multiplexed digital spatial profiling of invasive breast tumors from Black and White women. Mol Oncol. 2022 Jan;16(1):54-68. https://doi: 10.1002/1878-0261.13017
Useful Review Articles
Andrews AR, et al. Histologic Findings in Surgical Pathology Specimens From Individuals Taking Feminizing Hormone Therapy for the Purpose of Gender Transition: A Systematic Scoping Review. Arch Pathol Lab Med 1 February 2022; 146 (2): 252–261. https://doi.org/10.5858/arpa.2020-0704-RA
Luchini C, et al. Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring. Mod Pathol. 2022 Mar 5. https://doi.org/10.1038/s41379-022-01055-1
Zuraw A, et al. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol. 2022 Jan;59(1):6-25. https://doi.org/10.1177/03009858211040484
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. To learn more about us, visit www.instapathbio.com or email email@example.com.