The application of artificial intelligence to improve the access of cancer patients to high-quality medical care is one of the goals of modern medicine. Pathology constitutes the foundation of modern oncologic treatment, and its role has expanded far beyond diagnosis into predicting treatment response and overall survival. However, the funding of pathology is often an afterthought in resource-scarce medical systems. The increased digitalization of pathology has paved the way towards the potential use of artificial intelligence tools for improving pathologist efficiency and extracting more information from tissues. In this review, we provide an overview of the main research directions intersecting with artificial intelligence and pathology in relation to oncology, such as tumor classification, the prediction of molecular alterations, and biomarker quantification. We then discuss examples of tools that have matured into clinical products and gained regulatory approval for clinical use. Finally, we highlight the main hurdles that stand in the way of the digitalization of pathology and the application of artificial intelligence in pathology while also discussing possible solutions.

Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.

Background/Objectives: Small-cell lung cancer (SCLC) is a highly aggressive malignancy with an emerging molecular classification based on the expression of the transcription factors ASCL1, NEUROD1, and POU2F3. This study aimed to explore the relationship between these novel subtypes and the tumor immune microenvironment (TIME), particularly CD8+ and CD4+ tumor-infiltrating lymphocytes (TILs). Methods: In 51 cases of patients with SCLC, immunohistochemical (IHC) stains for ASCL1, NEUROD1, POU2F3, CD56, Ki67, CD8, and CD4 were performed. H-scores for the novel transcription factors were calculated to determine tumor subtype. CD8+ and CD4+ TIL counts were averaged across 10 high-power fields. The Kruskal–Wallis test and subsequent post hoc Dunn tests were used to determine the differences in transcription factor expression and TILs across subtypes. Results: In our cohort, 68.62% of our cases were SCLC-A, 9.80% were SCLC-N, 7.84% were SCLC-P, and 13.72% were SCLC-I. Significant differences were observed in the expression of ASCL1, NEUROD1, and POU2F3 across subtypes. CD8+ TILs were more abundant in SCLC-P and SCLC-I. CD8+ TILs were negatively correlated with ASCL1 expression (p < 0.05) and positively correlated with POU2F3 expression (p < 0.005). Conclusions: This study highlights the need to integrate the novel SCLC classification with data regarding the TIME to better inform patient prognosis and treatment.

Summary: COVID-19 has spread in a matter of months to most countries in the world. Various social and economic factors determine the time in which a pandemic reaches a country. This time is essential, because it allows countries to prepare their response. This study considered a gravity model that expressed time to first case as a function of multiple socio-economic factors. First, Kaplan-Meier analysis was performed for each variable in the model by dividing countries into two groups according to the median of the respective variable. In order to measure the effect of these variables, parameters of the gravity model were estimated using accelerated failure time (AFT) survival analysis. In the Kaplan-Meier analysis the differences between high and low value groups were significant for every variable except population. The AFT analysis determined that increased personal freedom had the largest effect on lowering the survival time, controlling for detection capacity. Higher GDP per capita and a larger population also reduced survival time, while a greater distance from the outbreak source increased it. Understanding the influence of factors affecting time to index case can help us understand disease spread in the early stages of a pandemic.

Molecular pathology-driven oncologic diagnostics—genomic profiling, transcriptomics, proteomics, and, increasingly, artificial intelligence applied to tissue and liquid biopsy—can only be as accurate as the biospecimens on which they are performed. Biobanks are the infrastructures that secure this foundation, linking donors, biological samples, and data to discovery and, through the pathology interface, back to diagnosis. Their scientific and diagnostic value, however, is determined less by the number of specimens stored than by the rigor of the guidelines under which those specimens are collected, processed, annotated, governed, and shared. The normative landscape has changed considerably in the last three years: the ISBER Best Practices reached their fifth edition (2023), the NCI Best Practices were comprehensively revised (2026), the Standard PREanalytical Code (SPREC) was updated to version 4.0 (2024/2025), MIABIS Core reached version 3.0 (2024), the 2024 revision of the Declaration of Helsinki explicitly anchored biobank governance to the Declaration of Taipei, the European Health Data Space Regulation (EU) 2025/327 entered into force, and ISO 20387—the accreditation standard for biobanks—is undergoing its first full revision. This review synthesizes the current (2026) status of international biobanking standards, ethical and legal frameworks, pre-analytical and quality management requirements, data and interoperability standards, and sustainability models, drawing on more than 200 sources with emphasis on the 2023–2026 literature. Standards are presented not as an inventory but as an operational system, organized along the biobanking workflow from donor consent to sample distribution and impact tracking. On this basis, we propose a phased, guideline-anchored roadmap and a start-up checklist to help new teams establish a real-world biobank—particularly hospital-integrated biobanks in settings without a mature national biobanking infrastructure—and we review comprehensively the financial, operational, regulatory, and societal challenges that determine whether a new biobank thrives or stalls. Particular attention is given to the specimen classes and quality controls on which molecular tumor diagnostics depend—FFPE tissue and its sequencing artifacts, fresh-frozen tissue, liquid biopsy analytes, and DV200-gated derivative quality—and to the interface between research biobanking and the accredited diagnostic laboratory, which is set out explicitly because the two are governed by different standards. We further provide explicit, endpoint-specific operational control of warm and cold ischemia, a quality-control framework for advanced patient-derived models (xenografts and organoids) and their interface with dedicated model cores, an explicit statement of the evidence underlying the practical recommendations, and a dedicated limitations section. The review is intended as a reference piece for biobankers, pathologists, clinician-researchers, quality managers, and institutional decision-makers building the biobanks on which the coming decade of precision oncology will depend.

Lung squamous cell carcinoma in situ (SCIS) is the preinvasive precursor lesion of lung squamous cell carcinoma (SCC). Only around two-thirds of these lesions progress to invasive cancer, while one-third undergo spontaneous regression, which presents a significant clinical challenge due to the risk of overtreatment. The ability to predict the evolution of SCIS lesions can significantly impact patient management. Our study explores the use of computational pathology in predicting the evolution of SCIS. We used a dataset consisting of 112 H&E-stained whole slide images (WSIs) that were obtained from the Image Data Resource public repository. The dataset corresponded to tumors of patients who underwent biopsies of SCIS lesions and were subsequently followed up by bronchoscopy and CT scans to monitor for progression to SCC. We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. The performance of both approaches in predicting progression was assessed. The pathomics-based ridge classifier model obtained an F1-score of 0.77, precision of 0.80, and recall of 0.77. The deep learning model performance was similar, with a WSI-level F1-score of 0.80, precision of 0.71, and recall of 0.90. These findings highlight the potential of computational pathology approaches in providing insights into the evolution of SCIS. Larger datasets will be required in order to train highly accurate models. In the future, computational pathology could be used in predicting outcomes in other preinvasive lesions.