The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility of the results. First, we discuss some of the most used tools, including QuPath, HistoQC, and HistomicsTK, and provide an updated overview of machine learning approaches and their application in pathology. Later, we report how these tools may simplify the automation of WSI analyses, also reducing manual workload and inter-observer variability. A novel aspect of this review is its focus on open-source tools, presented in a way that may help the adoption process for pathologists. Furthermore, we highlight the major benefits of these technologies, with the aim of making this review a practical guide for clinicians seeking to implement machine learning-based solutions in their specific workflows. Moreover, this review also emphasizes some crucial limitations related to data quality and the interpretability of the models, giving insight into future directions for research. Overall, this work tries to bridge the gap between the more recent technological progress in computer science and traditional clinical practice, supporting a broader, yet smooth, adoption of machine learning approaches in digital pathology.

With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists.

Background: Oxidative stress plays a key role in the pathogenesis of complications in preterm infants, including bronchopulmonary dysplasia (BPD). Thymosin β4 (Tβ4) and thymosin β10 (Tβ10) are proteins involved in tissue repair and responses to oxidative stress, but their role in extremely preterm neonates remains poorly understood. Methods: A total of 149 saliva samples from 18 infants with gestational age < 30 weeks were analyzed. Salivary proteins and their proteoforms were characterized using an integrated proteomic platform based on nano-HPLC-ESI-MS. Relative quantification was performed using extracted ion current (XIC) peak areas. Associations with postmenstrual age, oxygen requirement, and BPD development were assessed, including longitudinal analysis using generalized estimating equation (GEE) models. Results: Significant correlations were found between postmenstrual age and total Tβ4 (p = 0.001), oxidized Tβ4 percentage (p = 0.025), and total Tβ10 (p = 0.043). Higher oxygen requirement was associated with lower levels and percentages of oxidized Tβ10 (p = 0.005; p < 0.001). No significant differences were observed during the first week of life between neonates who later developed BPD and those who did not. However, longitudinal analysis showed that in neonates without BPD, total and oxidized Tβ10 and total Tβ4 increased over time, whereas in neonates with BPD, these biomarkers remained stable or decreased. The increase in oxidized Tβ10 percentage was slower in infants with BPD. Conclusions: Although no early differences were detected, longitudinal trajectories of Tβ4 and Tβ10 differed between infants with and without BPD. Postnatal changes in these proteins may be associated with differences in clinical course and exposure to postnatal oxidative stress.