Artificial intelligence and computational pathology

Abstract

Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.

Development of a semi-automated method for tumor budding assessment in colorectal cancer and comparison with manual methods

“…It is The copyright holder for this preprint this version posted June 17, 2021. ; https://doi.org/10.1101/2021.06.17.448482 doi: bioRxiv preprint automated budding density "heat maps" are almost identical. In contrast to assessment approaches driven entirely by machine learning, which can be confounded by even subtle variations in staining or scanning (24,25), our comparatively simple thresholding method can be readily adapted to new images by adjusting a small number of intuitive parametersmaking it immediately accessible to any laboratory wishing to apply the technique. Nevertheless, it is clearly desirable to achieve a better discrimination of true buds from false positives.…”

A deep learning approach to capture the essence of Candida albicans morphologies

“…There is evidence that Candescence is able to “overcome” errors and inconsistencies between labellers. This is a well documented problem in image recognition research including computational pathology 74 . We argue that the computational techniques introduced to computational pathology are largely applicable to fungal systems.…”

Tertiary Lymphoid Structures as a Predictive Biomarker of Response to Cancer Immunotherapies

“…It is important to keep in mind that different studies used varying methods for TLS quantification, such as the presence of CD208+ DCs found exclusively in TLS (in lung cancer), presence of FDC markers CD21 and CD23 or co-localisation of CD3+ T cells and CD20+ B cells. TLS can be nowadays investigated by state-of the art digital and computational pathology utilizing methods incorporating deep-learning and artificial intelligence (35). Additionally, it is vital to consider other patient-related factors while assessing TLS presence, since co-morbidities (such as chronic inflammation) or treatments (e.g.…”