[Current WHO classification (2022) of neuroendocrine neoplasms]. / Aktuelle WHO-Klassifikation (2022) neuroendokriner Neoplasien.

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. All NENs are characterized by the expression of synaptophysin and chromogranin A, Ki-67 and morphology. MOLECULAR PATHOLOGY: The morphological NEN dichotomy is supported by genetic alterations. NECs show TP53 and RB1 alterations that are absent in NETs and are therefore useful for differentiating between NETs and NECs. PRACTICAL RECOMMENDATIONS: All NENs are divided into well-differentiated neuroendocrine tumor (NET G1, G2, G3) or poorly differentiated neuroendocrine carcinoma (NEC, G3). They are categorized by morphology, mitotic count and immunohistochemistry with synaptophysin, chromogranin and Ki-67.

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. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

[Explainable artificial intelligence in pathology]. / Erklärbare Künstliche Intelligenz in der Pathologie.

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).