Combining immunoscore and tumor budding in colon cancer: an insightful prognostication based on the tumor-host interface.

Background: Tumor Budding (TB) and Immunoscore are independent prognostic markers in colon cancer (CC). Given their respective representation of tumor aggressiveness and immune response, we examined their combination in association with patient disease-free survival (DFS) in pTNM stage I-III CC.

Methods: In a series of pTNM stage I-III CCs (n = 654), the Immunoscore was computed and TB detected automatically using a deep learning network.

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Background: Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides.

Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting.

Standardization through education of molecular pathology: a spotlight on the European Masters in Molecular Pathology.

Despite advancements in precision medicine, many cancer patients globally, particularly those in resource-constrained environments, face significant challenges in accessing high-quality molecular testing and targeted therapies. The considerable heterogeneity in molecular testing highlights the urgent need to harmonize practices across Europe and beyond, establishing a more standardized and consistent approach in MP laboratories. Professionals, especially molecular pathologists, must move beyond traditional education to cope with this heterogeneity.

Improving quality control of whole slide images by explicit artifact augmentation.

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming.