Stealthy Invader: Clinically Inapparent But Rapidly Progressive Multifocal Metastatic Melanoma Unveiled as Root Cause of Liver Failure.

Background: Acute liver failure (ALF) secondary to metastatic melanoma presents a rare and diagnostically challenging clinical scenario.

Case Report: We report the case of a 57-year-old male who succumbed to fulminant liver failure attributed to hepatic infiltration by malignant melanoma. Despite extensive diagnostic evaluation, the underlying cause of ALF remained elusive until postmortem examination revealed multifocal metastatic melanoma.

[Current WHO classification (2022) of neuroendocrine neoplasms].

Clinical Issue: After the first description of the "carcinoid tumors" by the pathologist Siegfried Oberndorfer in Munich, the classification system of neuroendocrine neoplasms (NENs) is still a challenge and an evolving concept.

Methodical Innovations: The new WHO classification system proposed a framework for universal classification.

Achievements: The new WHO classification system recognizes two distinct families distinguished by genetic, morphology and clinical behaviour: Well differentiated NENs are defined as neuroendocrine tumor (NET G1, G2, G3), while poorly differentiated ones are defined as neuroendocrine carcinoma (NEC, G3) and further subdivided into small and large cell carcinoma.

[Explainable artificial intelligence in pathology].

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

Toward Explainable Artificial Intelligence for Precision Pathology.

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond.