Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Subclinical Atherosclerosis: Part 1: What Is it? Can it Be Defined at the Histological Level?

While coronary artery disease remains a major cause of death, it is preventable. Therefore, the focus needs to shift to the early detection and prevention of atherosclerosis. Asymptomatic atherosclerosis is widely termed subclinical atherosclerosis, which is an early indicator of atherosclerotic burden, and understanding this disease is important because timely intervention could prevent future cardiovascular morbidity and mortality. We histologically recognize the earliest lesion of atherosclerosis as pathological intimal thickening, which is characterized by the presence of lipid pools. The difference between clinical atherosclerosis and subclinical atherosclerosis is whether the presence of atherosclerosis results in the clinical symptoms of ischemia, such as stroke, myocardial infarction, or chronic limb-threatening ischemia. In the absence of thrombosis, there are various types of histological plaque that encompass subclinical atherosclerosis: pathological intimal thickening, fibroatheroma, thin-cap fibroatheroma, plaque rupture, healed plaque ruptures, and fibrocalcific plaque. Plaque morphology that is most frequently responsible for acute coronary thrombosis is plaque rupture. Calcification of coronary arteries is the hallmark of atherosclerosis and is a predictor of future coronary events. Atherosclerosis occurs in other vascular beds and is most frequent in arteries of the lower extremity, followed by carotid, aorta, and coronary arteries, and the mechanisms leading to clinical symptoms are unique for each location.

Efficient transmission of human prion diseases to a glycan-free prion protein-expressing host.

It is increasingly evident that the association of glycans with the prion protein (PrP), a major post-translational modification, significantly impacts the pathogenesis of prion diseases. A recent bioassay study has provided evidence that the presence of PrP glycans decreases spongiform degeneration and disease-related PrP (PrPD) deposition in a murine model. We challenged (PRNPN181Q/197Q) transgenic (Tg) mice expressing glycan-free human PrP (TgGlyc-), with isolates from sporadic Creutzfeldt-Jakob disease subtype MM2 (sCJDMM2), sporadic fatal insomnia and familial fatal insomnia, three human prion diseases that are distinct but share histotypic and PrPD features. TgGlyc- mice accurately replicated the basic histotypic features associated with the three diseases but the transmission was characterized by high attack rates, shortened incubation periods and a greatly increased severity of the histopathology, including the presence of up to 40 times higher quantities of PrPD that formed prominent deposits. Although the engineered protease-resistant PrPD shared at least some features of the secondary structure and the presence of the anchorless PrPD variant with the wild-type PrPD, it exhibited different density gradient profiles of the PrPD aggregates and a higher stability index. The severity of the histopathological features including PrP deposition appeared to be related to the incubation period duration. These findings are clearly consistent with the protective role of the PrP glycans but also emphasize the complexity of the conformational changes that impact PrPD following glycan knockout. Future studies will determine whether these features apply broadly to other human prion diseases or are PrPD-type dependent.

Human Listeriosis.

Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that can cause severe invasive infections upon ingestion with contaminated food. Clinically, listerial disease, or listeriosis, most often presents as bacteremia, meningitis or meningoencephalitis, and pregnancy-associated infections manifesting as miscarriage or neonatal sepsis. Invasive listeriosis is life-threatening and a main cause of foodborne illness leading to hospital admissions in Western countries. Sources of contamination can be identified through international surveillance systems for foodborne bacteria and strains' genetic data sharing. Large-scale whole genome studies have increased our knowledge on the diversity and evolution of L. monocytogenes, while recent pathophysiological investigations have improved our mechanistic understanding of listeriosis. In this article, we present an overview of human listeriosis with particular focus on relevant features of the causative bacterium, epidemiology, risk groups, pathogenesis, clinical manifestations, and treatment and prevention.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Local, Quantitative Morphometry of Fibroproliferative Lung Injury Using Laminin.

Investigations into the mechanisms of injury and repair in fibroproliferative disease require consideration of the spatial heterogeneity inherent in the disease. Most scoring of fibrotic remodeling in preclinical animal models relies on the modified Ashcroft score, which is an ordinal rubric of macroscopic resolution. The obvious limitations of manual histopathologic scoring have generated an unmet need for unbiased, repeatable scoring of fibroproliferative burden in tissue. Using computer vision approaches on immunofluorescence imaging of the extracellular matrix component laminin, we generated a robust and repeatable quantitative remodeling scorer. In the bleomycin lung injury model, the quantitative remodeling scorer shows significant agreement with the modified Ashcroft scale. This antibody-based approach is easily integrated into larger multiplex immunofluorescence experiments, which we demonstrate by testing the spatial apposition of tertiary lymphoid structures to fibroproliferative tissue, a poorly characterized phenomenon observed in both human interstitial lung diseases and preclinical models of lung fibrosis. The tool reported in this article is available as a stand-alone application that is usable without programming knowledge.

Hepatoprotective activity of Lactéol® forte and quercetin dihydrate against thioacetamide-induced hepatic cirrhosis in male albino rats.

Liver cirrhosis is a silent disease in humans and is experimentally induced by many drugs and toxins as thioacetamide (TAA) in particular, which is the typical model for experimental induction of hepatic fibrosis. Thus, the objective of the present study was to elucidate the possible protective effects of lactéol® forte (LF) and quercetin dihydrate (QD) against TAA-induced hepatic damage in male albino rats. Induction of hepatotoxicity was performed by TAA injection (200 mg/kg I/P, twice/ week) in rats. LF (1 × 109 CFU/rat 5 times/week) and QD (50 mg/kg 5 times/week) treated groups were administered concurrently with TAA injection (200 mg/kg I/P, twice/ week). The experimental treatments were conducted for 12 weeks. Hepatotoxicity was evaluated biochemically by measuring alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyl transferase (GGT) in the serum and histopathologically with the scoring of histopathological changes besides histochemical assessment of collagen by Masson's trichrome and immunohistochemical analysis for α-smooth muscle actin (α-SMA), Ki67 and caspase-3 expression in liver sections. Our results indicated that LF and QD attenuated some biochemical changes and histochemical markers in TAA-mediated hepatotoxicity in rats by amelioration of biochemical markers and collagen, α-SMA, Ki67 and caspase3 Immunoexpression. Additionally, LF and QD supplementation downregulated the proliferative, necrotic, fibroblastic changes, eosinophilic intranuclear inclusions, hyaline globules and Mallory-like bodies that were detected histopathologically in the TAA group. In conclusion, LF showed better hepatic protection than QD against TAA-induced hepatotoxicity in rats by inhibiting inflammatory reactions with the improvement of some serum hepatic transaminases, histopathological picture and immunohistochemical markers.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.

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. CONCLUSION: 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.

CNMI-YOLO: Domain Adaptive and Robust Mitosis Identification in Digital Pathology.

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.

Histopathological phenotyping of cancers in PTEN Hamartoma Tumor Syndrome for improved recognition: A single-center study.

PTEN hamartoma tumor syndrome (PHTS) has a broad clinical spectrum including various benign and malignant tumors at varying age of diagnosis. Many patients remain unrecognized, unaware of their increased cancer risk. We aimed to describe the cancer spectrum, age of onset and histopathological cancer characteristics to assess whether specific cancer characteristics could improve PHTS recognition. Genetic testing results and pathology reports were collected for patients tested for germline PTEN variants between 1997 and 2020 from the diagnostic laboratory and the Dutch nationwide pathology databank (Palga). The cancer spectrum and age of onset were assessed in patients with (PTENpos) and without (PTENneg) a germline PTEN variant. Histopathological cancer characteristics were assessed in a nested cohort. 341 PTENpos patients (56% females) and 2882 PTENneg patients (66% females) were included. PTENpos patients presented mostly with female breast (BC, 30%), endometrial (EC, 6%), thyroid (TC, 4%) or colorectal cancer (4%). PTENpos were significantly younger at cancer onset (43 vs. 47 years) and had more often (46% vs. 18%) a second BC than PTENneg. PTEN detection rates were highest for BC <40 years (9%), TC <20 years (15%) and EC <50 years (28%), and dropped to 6%, 4%, and 15% by age 60. Histopathological characteristics were similar between groups. No histopathological cancer characteristics were distinctive for PHTS. However, PTENpos were significantly younger at cancer onset. Therefore early-onset BC, EC, or TC warrants consideration of PHTS diagnostics either through a pre-screen for other PHTS features or direct germline testing.

Injection of Adipose-Derived Mesenchymal Stem/Stromal Cells Suppresses Muscle Atrophy Markers and Adipogenic Markers in a Rat Fatty Muscle Degeneration Model.

Fatty muscle degeneration and muscle atrophy have not been successfully treated due to their irreversible pathology. This study evaluated the efficacy of rat adipose-derived mesenchymal stem/stromal cells (ADP MSCs) in treating fatty muscle degeneration (FD). A total of 36 rats were divided into three groups: the control (C) group (n = 12); FD model group, generated by sciatic nerve crushing (n = 12); and the group receiving ADP MSC treatment for FD (FD+MSCs) (n = 12). In Group FD+MSCs, ADP MSCs were injected locally into the gastrocnemius muscle one week after the FD model was created (Day 8). On Day 22 (n = 18) and Day 43 (n = 18), muscle morphology, histopathology, and molecular analyses (inflammation, muscle atrophy, adipocytes, and muscle differentiation markers) were performed. In Group FD+MSCs, the formation of immature myofibers was observed on Day 22, and mitigation of fatty degeneration and muscle atrophy progression was evident on Day 43. Gene expression of muscle atrophy markers (FBXO32, TRIM63, and FOXO1) and adipogenic markers (ADIPOQ, PPARG, FABP4, and PDGFRA) was lower in Group FD+MSCs than Group FD on Day 43. ADP MSCs induce anti-inflammatory effects, inhibit fat accumulation, and promote muscle regeneration, highlighting their potential as promising therapy for FD and atrophy.

Blueprints from plane to space: outlook of next-generation three-dimensional histopathology.

Here, we summarize the literature relevant to recent advances in three-dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light-sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross-disciplinary collaboration and innovation.

The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update.

Significant advances in artificial intelligence (AI) over the past decade potentially may lead to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI implementation. We describe several current needs within the world of gastrointestinal histopathology, and outline, using currently studied models, how AI potentially can address them. We also highlight pitfalls as AI makes inroads into clinical practice.

Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis.

BACKGROUND & AIMS: Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis. METHODS: A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity, prognostic prediction through Kaplan-Meier, and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients. RESULTS: The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort. CONCLUSION: We developed and validated an AI model that distinguishes histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials.

Microcalcifications in benign breast biopsies: association with lesion type and risk.

PURPOSE: To characterize associations of microcalcifications (calcs) with benign breast disease lesion subtypes and assess whether tissue calcs affect risks of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC). METHODS: We analyzed detailed histopathologic data for 4,819 BBD biopsies from a single institution cohort (2002-2013) followed for DCIS or IBC for a median of 7.4 years for cases (N = 338) and 11.2 years for controls. Natural language processing was used to identify biopsies containing calcs based on pathology reports. Univariable and multivariable regression models were applied to assess associations with BBD lesion type and age-adjusted Cox proportional hazard regressions were performed to model risk of IBC or DCIS stratified by the presence or absence of calcs. RESULTS: Calcs were identified in 2063 (42.8%) biopsies. Calcs were associated with older age at BBD diagnosis (56.2 versus 49.0 years; P < 0.001). Overall, the risk of developing IBC or DCIS did not differ significantly between patients with calcs (HR 1.13, 95% CI 0.90, 1.41) as compared to patients without calcs. Stratification by BBD severity or subtype, age at BBD biopsy, outcomes of IBC versus DCIS, and mammography technique (screen-film versus full-field digital mammography) did not significantly alter association between calcs and risk. CONCLUSION: Our analysis of calcs in BBD biopsies did not find a significant association between calcs and risk of breast cancer.

Performance of rapid on-site evaluation of touch imprints of bronchoscopic biopsies or lung tissue biopsies for the diagnosis of invasive pulmonary filamentous fungi infections in non-neutropenic patients.

The diagnosis of invasive pulmonary fungal disease depends on histopathology and mycological culture; there are few studies on touch imprints of bronchoscopic biopsies or lung tissue biopsies for the diagnosis of pulmonary filamentous fungi infections. The purpose of the present study was to explore the detection accuracy of rapid on-site evaluation of touch imprints of bronchoscopic biopsies or lung tissue biopsies for the filamentous fungi, and it aims to provide a basis for initiating antifungal therapy before obtaining microbiological evidence. We retrospectively analyzed the diagnosis and treatment of 44 non-neutropenic patients with invasive pulmonary filamentous fungi confirmed by glactomannan assay, histopathology, and culture from February 2017 to December 2023. The diagnostic positive rate and sensitivity of rapid on-site evaluation for these filamentous fungi identification, including diagnostic turnaround time, were calculated. Compared with the final diagnosis, the sensitivity of rapid on-site evaluation was 81.8%, and the sensitivity of histopathology, culture of bronchoalveolar lavage fluid, and glactomannan assay of bronchoalveolar lavage fluid was 86.4%, 52.3%, and 68.2%, respectively. The average turnaround time of detecting filamentous fungi by rapid on-site evaluation was 0.17 ± 0.03 hours, which was significantly faster than histopathology, glactomannan assay, and mycological culture. A total of 29 (76.3%) patients received earlier antifungal therapy based on ROSE diagnosis and demonstrated clinical improvement. Rapid on-site evaluation showed good sensitivity and accuracy that can be comparable to histopathology in identification of pulmonary filamentous fungi. Importantly, it contributed to the triage of biopsies for further microbial culture or molecular detection based on the preliminary diagnosis, and the decision on early antifungal therapy before microbiological evidence is available.

Adenoviral-vectored universal influenza vaccines administered intranasally reduce lung inflammatory responses upon viral challenge 15 months post-vaccination.

IMPORTANCE: Vaccines targeting highly conserved proteins can protect broadly against diverse viral strains. When a vaccine is administered to the respiratory tract, protection against disease is especially powerful. However, it is important to establish that this approach is safe. When vaccinated animals later encounter viruses, does reactivation of powerful local immunity, including T cell responses, damage the lungs? This study investigates the safety of mucosal vaccination of the respiratory tract. Non-replicating adenoviral vaccine vectors expressing conserved influenza virus proteins were given intranasally. This vaccine-induced protection persists for at least 15 months. Vaccination did not exacerbate inflammatory responses or tissue damage upon influenza virus infection. Instead, vaccination with nucleoprotein reduced cytokine responses and histopathology, while neutrophil and T cell responses resolved earlier. The results are promising for safe vaccination at the site of infection and thus have implications for the control of influenza and other respiratory viruses.

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions.

Generative adversarial networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images, whereas the discriminator determines their authenticity by comparing them with real examples. Through iterative training, the generator allows the creation of images that are indistinguishable from real ones, leading to high-quality image generation. Considering their success in computer vision, GANs hold great potential for medical diagnostic applications. In the medical field, GANs can generate images of rare diseases, aid in learning, and be used as visualization tools. GANs can leverage unlabeled medical images, which are large in size, numerous in quantity, and challenging to annotate manually. GANs have demonstrated remarkable capabilities in image synthesis and have the potential to significantly impact digital histopathology. This review article focuses on the emerging use of GANs in digital histopathology, examining their applications and potential challenges. Histopathology plays a crucial role in disease diagnosis, and GANs can contribute by generating realistic microscopic images. However, ethical considerations arise because of the reliance on synthetic or pseudogenerated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, digital histopathology has seen an emerging use of GANs for image enhancement, such as color (stain) normalization, virtual staining, and ink/marker removal. GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.

A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology.

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.