A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer

Assessment of cancer cell-expressed HLA class I molecules and their immunopathological implications

Immunotherapy using immune checkpoint inhibitors (ICIs) has shown superior efficacy compared with conventional chemotherapy in certain cancer types, establishing immunotherapy as the fourth standard treatment alongside surgical intervention, chemotherapy, and radiotherapy. In cancer immunotherapy employing ICIs, CD8-positive cytotoxic T lymphocytes are recognized as the primary effector cells. For effective clinical outcomes, it is essential that the targeted cancer cells express HLA class I molecules to present antigenic peptides derived from the tumor. However, cancer cells utilize various mechanisms to downregulate or lose HLA class I molecules from their surface, resulting in evasion from immune surveillance. Correlations between prognosis and the integrity of HLA class I molecules expressed by cancer cells have been consistently found across different types of cancer. This paper provides an overview of the regulatory mechanisms of HLA class I molecules and their role in cancer immunotherapy, with a particular emphasis on the significance of utilizing pathological tissues to evaluate HLA class I molecules expressed in cancer cells.

Twenty years of histochemistry in the third millennium, browsing the scientific literature

Over the last twenty years, about 240,000 articles where histochemical techniques were used have been published in indexed journals, and their yearly number has progressively increased. The histochemical approach was selected by researchers with very different scientific interests, as the journals in which these articles were published fall within 140 subject categories. The relative proportion of articles in some of these journal categories did change over the years, and browsing the table of contents of the European Journal of Histochemistry, as an example of a strictly histochemical journal, it appeared that in recent years histochemical techniques were preferentially used to mechanistically investigate natural or experimentally induced dynamic processes, with reduced attention to purely descriptive works. It may be foreseen that, in the future, histochemistry will be increasingly focused on studying the molecular pathways responsible for cell differentiation, the maintenance or loss of the differentiated state, and tissue regeneration.

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.