In digital pathology, accurate mitosis detection in histopathological images is critical for cancer diagnosis and prognosis. However, this remains challenging due to the inherent variability in cell morphology and the domain shift problem. This study introduces CNMI-YOLO (ConvNext Mitosis Identification-YOLO), a new two-stage deep learning method that uses the YOLOv7 architecture for cell detection and the ConvNeXt architecture for cell classification. The goal is to improve the identification of mitosis in different types of cancer. We utilized the MIDOG 2022 dataset in the experiments to ensure the model\'s robustness and success across various scanners, species, and cancer types. The CNMI-YOLO model demonstrates superior performance in accurately detecting mitotic cells, significantly outperforming existing models in terms of precision, recall, and F1-score. The CNMI-YOLO model achieved an F1-score of 0.795 on the MIDOG 2022 and demonstrated robust generalization with F1-scores of 0.783 and 0.759 on the external melanoma and sarcoma test sets, respectively. Additionally, the study included ablation studies to evaluate various object detection and classification models, such as Faster R-CNN and Swin Transformer. Furthermore, we assessed the model\'s robustness performance on unseen data, confirming its ability to generalize and its potential for real-world use in digital pathology, using soft tissue sarcoma and melanoma samples not included in the training dataset.

The development of reliable artificial intelligence (AI) algorithms in pathology often depends on ground truth provided by annotation of whole slide images (WSI), a time-consuming and operator-dependent process. A comparative analysis of different annotation approaches is performed to streamline this process. Two pathologists annotated renal tissue using semi-automated (Segment Anything Model, SAM)) and manual devices (touchpad vs mouse). A comparison was conducted in terms of working time, reproducibility (overlap fraction), and precision (0 to 10 accuracy rated by two expert nephropathologists) among different methods and operators. The impact of different displays on mouse performance was evaluated. Annotations focused on three tissue compartments: tubules (57 annotations), glomeruli (53 annotations), and arteries (58 annotations). The semi-automatic approach was the fastest and had the least inter-observer variability, averaging 13.6 ± 0.2 min with a difference (Δ) of 2%, followed by the mouse (29.9 ± 10.2, Δ = 24%), and the touchpad (47.5 ± 19.6 min, Δ = 45%). The highest reproducibility in tubules and glomeruli was achieved with SAM (overlap values of 1 and 0.99 compared to 0.97 for the mouse and 0.94 and 0.93 for the touchpad), though SAM had lower reproducibility in arteries (overlap value of 0.89 compared to 0.94 for both the mouse and touchpad). No precision differences were observed between operators (p = 0.59). Using non-medical monitors increased annotation times by 6.1%. The future employment of semi-automated and AI-assisted approaches can significantly speed up the annotation process, improving the ground truth for AI tool development.

The incorporation of novel therapeutic agents such as antibody-drug conjugates, radio-conjugates, T-cell engagers, and chimeric antigen receptor cell therapies represents a paradigm shift in oncology. Cell-surface target quantification, quantitative assessment of receptor internalization, and changes in the tumor microenvironment (TME) are essential variables in the development of biomarkers for patient selection and therapeutic response. Assessing these parameters requires capabilities that transcend those of traditional biomarker approaches based on immunohistochemistry, in situ hybridization and/or sequencing assays. Computational pathology is emerging as a transformative solution in this new therapeutic landscape, enabling detailed assessment of not only target presence, expression levels, and intra-tumor distribution but also of additional phenotypic features of tumor cells and their surrounding TME. Here, we delineate the pivotal role of computational pathology in enhancing the efficacy and specificity of these advanced therapeutics, underscoring the integration of novel artificial intelligence models that promise to revolutionize biomarker discovery and drug development.

Gastric cancer is the fifth most common and fourth deadliest cancer worldwide, with a bleak 5-year survival rate of about 20%. Despite significant research into its pathobiology, prognostic predictability remains insufficient due to pathologists\' heavy workloads and the potential for diagnostic errors. Consequently, there is a pressing need for automated and precise histopathological diagnostic tools. This study leverages Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. By utilizing both handcrafted and deep features and shallow learning classifiers on the GasHisSDB dataset, we conduct a comparative analysis to identify the most effective combinations of features and classifiers for differentiating normal from abnormal histopathological images without employing fine-tuning strategies. Our methodology achieves an accuracy of 95% with the SVM classifier, underscoring the effectiveness of feature fusion strategies. Additionally, cross-magnification experiments produced promising results with accuracies close to 80% and 90% when testing the models on unseen testing images with different resolutions.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postoperative liver metastasis in panNET patients.
METHODS: Clinical data, pathology slides, and radiographic images were collected from 163 panNET patients post-R0 resection at Fudan University Shanghai Cancer Center (FUSCC) and FUSCC Pathology Consultation Center. Digital image analysis and deep learning identified liver metastasis-related features in Ki67-stained whole slide images (WSIs) and enhanced CT scans to create a nomogram. The model\'s performance was validated in both internal and external test cohorts.
RESULTS: Multivariate logistic regression identified nerve infiltration as an independent risk factor for liver metastasis (p < 0.05). The Pathomics score, which was based on a hotspot and the heterogeneous distribution of Ki67 staining, showed improved predictive accuracy for liver metastasis (AUC = 0.799). The deep learning-radiomics (DLR) score achieved an AUC of 0.875. The integrated nomogram, which combines clinical, pathological, and imaging features, demonstrated outstanding performance, with an AUC of 0.985 in the training cohort and 0.961 in the validation cohort. High-risk group had a median recurrence-free survival of 28.5 months compared to 34.7 months for the low-risk group, showing significant correlation with prognosis (p < 0.05).
CONCLUSIONS: A new predictive model that integrates computational pathologic scores and deep learning-radiomics can better predict postoperative liver metastasis in panNET patients, aiding clinicians in developing personalized treatments.

BACKGROUND: Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.
METHODS: This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.
RESULTS: In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups.
CONCLUSIONS: The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

The practice of cytopathology has been significantly refined in recent years, largely through the creation of consensus rule sets for the diagnosis of particular specimens (Bethesda, Milan, Paris, and so forth). In general, these diagnostic systems have focused on reducing intraobserver variance, removing nebulous/redundant categories, reducing the use of \"atypical\" diagnoses, and promoting the use of quantitative scoring systems while providing a uniform language to communicate these results. Computational pathology is a natural offshoot of this process in that it promises 100% reproducible diagnoses rendered by quantitative processes that are free from many of the biases of human practitioners.

In early gastric cancer (EGC), the presence of lymph node metastasis (LNM) is a crucial factor for determining the treatment options. Endoscopic resection is used for treatment of EGC with minimal risk of LNM. However, owing to the lack of definitive criteria for identifying patients who require additional surgery, some patients undergo unnecessary additional surgery. Considering that histopathologic patterns are significant factor for predicting lymph node metastasis in gastric cancer, we aimed to develop a machine learning algorithm which can predict LNM status using hematoxylin and eosin (H&E)-stained images. The images were obtained from several institutions. Our pipeline comprised two sequential approaches including a feature extractor and a risk classifier. For the feature extractor, a segmentation network (DeepLabV3+) was trained on 243 WSIs across three datasets to differentiate each histological subtype. The risk classifier was trained with XGBoost using 70 morphological features inferred from the trained feature extractor. The trained segmentation network, the feature extractor, achieved high performance, with pixel accuracies of 0.9348 and 0.8939 for the internal and external datasets in patch level, respectively. The risk classifier achieved an overall AUC of 0.75 in predicting LNM status. Remarkably, one of the datasets also showed a promising result with an AUC of 0.92. This is the first multi-institution study to develop machine learning algorithm for predicting LNM status in patients with EGC using H&E-stained histopathology images. Our findings have the potential to improve the selection of patients who require surgery among those with EGC showing high-risk histological features.