Biobanking in the digital pathology era

Abstract

Digital Pathology is becoming more and more important to achieve the goal of precision medicine. Advances in whole-slide imaging, software integration, and the accessibility of storage solutions have changed the pathologists' clinical practice, not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis. In parallel with the pathology setting advancement, translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence (AI). Indeed, the increased usage of biobanks' datasets in research provided new challenges for AI applications, such as advanced algorithms, and computer-aided techniques. In this scenario, machine learningbased approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational datasets. To date, evidence on how to implement digital biobanks in translational medicine is still lacking. This viewpoint article summarizes the currently available literature that supports the biobanks' role in the digital pathology era, and to provide possible practical applications of digital biobanks.

Unlocking the potential of big data and AI in medicine: insights from biobanking

“…Not only are data not always perfect due to inherent finite categorization of potentially infinite diversity, but their capacity to represent should always be continuously problematized . Against the biobanking professionals’ concerns, the tendency to see biobanks as data repositories and medicine as increasingly digital ( 27 , 63 ) can result in a false sense of security in the imaginary of increasing data interoperability and connectedness at the peril of ignoring what D’Ignazio and Klein ( 77 ) rightly note the existence of “problems that cannot be represented—or addressed—by data alone” (p. 10). Risks accompany the opportunities in a datafied world.…”

“…In attempts towards datafication, practices around samples such as in pathology are also being transformed, exemplified by “digital pathology” where whole slide images that are once created may decrease the need to store samples or increase the findability by turning images into data collected ( 27 ). Scholars observe along a trend of consolidation emergence of virtual biobanks brings together resources from multiple biobanks ( 28 , 29 ), though such cataloging examples also include efforts of broader research infrastructures, such as BBMRI-ERIC ( 30 ).…”

Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking

“…The data must be securely stored, readily accessible, and traceable to effectively manage multiple projects concurrently. Furthermore, another crucial facet of biobanking management is the implementation of an integrated Laboratory Information Management System (LIMS) software that can integrate clinical records and patient data in addition to registering all samples-related data and sample processing data [ 21 , 22 , 23 ]. To secure this integration, it is critical to obtain the informed consent of all patients for legal and ethical requirements [ 19 ].…”

“…These novel technologies enable personalized and preventive medical approaches, addressing challenges such as demographic change, healthcare accessibility, and sustainability. In oncology, the utilization of biomarkers and digital pathology through Digital Biobanks facilitates personalized diagnostic-therapeutic approaches, improving patient outcomes and resource management [ 21 ]. Integrating AI and digital pathology enhances the speed, accuracy, and remote capabilities of pathology diagnostics relying on novel small devices [ 44 , 45 ], enabling multidisciplinary consultations and supporting clinical trials.…”

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

“…These AI applications, particularly in digital and computational pathology, allow for the extraction of subvisual morphometric phenotypes, potentially leading to diagnostic breakthroughs [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. However, their application both in clinical studies and real-world clinical practice faces several unresolved challenges [ 24 ]. This article aims to explore the intersection of traditional pathology and AI in the definition of novel and more reproducible algorithms for EBC risk profiling.…”