A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

MGAT4EP promotes tumor progression and serves as a prognostic marker for breast cancer

Breast cancer remains a global health challenge with varied prognoses despite treatment advancements. Therefore, this study explores the pseudogene MGAT4EP as a potential biomarker and therapeutic target in breast cancer. Using TCGA data and bioinformatics, MGAT4EP was identified as significantly overexpressed in breast cancer tissues and associated with poor prognosis. Multivariate Cox regression confirmed MGAT4EP as important prognostic factor. A clinical prediction model based on MGAT4EP expression showed high accuracy for 1-, 3-, and 5-year survival rates and was translated into a nomogram for clinical application. Functional studies revealed that silencing MGAT4EP via siRNA promoted apoptosis, inhibited migration and invasion in breast cancer cells. RNA-seq, GSEA, and GO analyses linked MGAT4EP to apoptosis and focal adhesion pathways. Notably, knock down of MGAT4EP significantly suppressed tumor growth and metastasis in xenograft and lung metastasis models. Taken together, these findings establish MGAT4EP as an attractive target for metastatic breast cancer and provide a potential a promising therapeutic target for breast cancer treatment.

Automated assessment of skin histological tissue structures by artificial intelligence in cutaneous melanoma

BACKGROUND

Prognostic histopathological features such as mitosis in melanoma are excluded from the staging systems due to inter-observer variability and time constraints. While digital pathology offers artificial intelligence-driven solutions, existing melanoma algorithms often underperform or narrowly focus on specific features, limiting their clinical utility.

OBJECTIVE

Develop and validate an automated artificial intelligence-driven segmentation framework to identify multiple histological tissue structures within cutaneous melanoma images.

METHODS

Employing 157 melanoma whole slide images, U-Net and DeepLab3+ classifiers were independently trained Oncotopix ® platform using manual annotations, to detect specific histological features, termed application. All the applications are progressively executable. The performance of each application was measured when both operating independently and with sequential detection when applied to ten independent validation set images using accuracy and F1-score as metrics. The model was further validated by applying it to 442 whole-slide melanoma images, with dermatopathologists reviewing the segmentation outputs.

RESULTS

Seven applications were developed for progressive automated detection: Whole tissue (1) and tumour microenvironment (TME) (2), Hair follicles & sebaceous gland (3) within TME, ulceration (5), and melanoma cell area (6) based on DeepLab3+. Epidermis (4) and mitosis within the tumour area (7) based on U-Net. The applications demonstrated over 92 % accuracy and F1-score surpassing 80 %, except for the ulceration application (F1-score = 75 %). The pathologist examination indicated that 92 % of the 442 images had correct segmentations.

DISCUSSION AND CONCLUSION

The developed applications demonstrated high performance, enhancing the analysis of time-consuming histological features. The model facilitates the identification of histopathological features in large datasets allowing potential refinement of melanoma staging.

A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.

Decoding pathology: the role of computational pathology in research and diagnostics

Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.

Computational pathology for breast cancer: Where do we stand for prognostic applications?

The very early days of artificial intelligence (AI) in healthcare are behind us. AI is now spreading in the healthcare sector and is gradually being implemented in routine clinical practice. Driven by the increasing digitization of microscope slides, computational pathology (CPath) is further strengthening the role of AI in the field of oncology. CPath is transforming fundamental research as well as routine clinical practice, both for diagnostic and prognostic applications. In breast cancer, CPath holds the potential to address several unmet clinical needs, particularly in the areas of biomarkers and prognostic tools. Indeed, multiple applications are on their way, ranging from predicting clinically meaningful endpoints to offering alternatives to gene-expression testing and detecting molecular alterations directly from digitized whole slide images. However, to fully harness the potential of CPath, several challenges must be overcome. These include improving the availability of multimodal patient data, advancing the digitalization of pathology laboratories, increasing adoption within the medical community, and navigating regulatory hurdles. This review offers an overview of the current landscape of CPath in breast cancer, highlighting the progress made and the hurdles that remain for its widespread clinical adoption in prognostic applications.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis

The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs). This causes degraded AI performance and poses a challenge for widespread clinical application, as fine-tuning algorithms for each site is impractical. Changes in the imaging workflow can also compromise diagnostic accuracy and patient safety. Physical color calibration of scanners, relying on a biomaterial-based calibrant slide and a spectrophotometric reference measurement, has been proposed for standardizing WSI appearance, but its impact on AI performance has not been investigated. We evaluated whether physical color calibration can enable robust AI performance. We trained fully supervised and foundation model-based AI systems for detecting and Gleason grading prostate cancer using WSIs of prostate biopsies from the STHLM3 clinical trial (n = 3651) and evaluated their performance in 3 external cohorts (n = 1161) with and without calibration. With physical color calibration, the fully supervised system's concordance with pathologists' grading (Cohen linearly weighted κ) improved from 0.439 to 0.619 in the Stavanger University Hospital cohort (n = 860), from 0.354 to 0.738 in the Karolinska University Hospital cohort (n = 229), and from 0.423 to 0.452 in the Aarhus University Hospital cohort (n = 72). The foundation model's concordance improved as follows: from 0.739 to 0.760 (Karolinska), from 0.424 to 0.459 (Aarhus), and from 0.547 to 0.670 (Stavanger). This study demonstrated that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in diverse clinical settings.

Targeting the initiator to activate both ferroptosis and cuproptosis for breast cancer treatment: progress and possibility for clinical application

Breast cancer is the most commonly diagnosed cancer worldwide. Metal metabolism is pivotal for regulating cell fate and drug sensitivity in breast cancer. Iron and copper are essential metal ions critical for maintaining cellular function. The accumulation of iron and copper ions triggers distinct cell death pathways, known as ferroptosis and cuproptosis, respectively. Ferroptosis is characterized by iron-dependent lipid peroxidation, while cuproptosis involves copper-induced oxidative stress. They are increasingly recognized as promising targets for the development of anticancer drugs. Recently, compelling evidence demonstrated that the interplay between ferroptosis and cuproptosis plays a crucial role in regulating breast cancer progression. This review elucidates the converging pathways of ferroptosis and cuproptosis in breast cancer. Moreover, we examined the value of genes associated with ferroptosis and cuproptosis in the clinical diagnosis and treatment of breast cancer, mainly outlining the potential for a co-targeting approach. Lastly, we delve into the current challenges and limitations of this strategy. In general, this review offers an overview of the interaction between ferroptosis and cuproptosis in breast cancer, offering valuable perspectives for further research and clinical treatment.

Use machine learning to predict bone metastasis of esophageal cancer: A population-based study

Objective

The objective of this study is to develop a machine learning (ML)-based predictive model for bone metastasis (BM) in esophageal cancer (EC) patients.

Methods

This study utilized data from the Surveillance, Epidemiology, and End Results database spanning 2010 to 2020 to analyze EC patients. A total of 21,032 confirmed cases of EC were included in the study. Through univariate and multivariate logistic regression (LR) analysis, 10 indicators associated with the risk of BM were identified. These factors were incorporated into seven different ML classifiers to establish predictive models. The performance of these models was assessed and compared using various metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F-score, precision, and decision curve analysis.

Results

Factors such as age, gender, histological type, T stage, N stage, surgical intervention, chemotherapy, and the presence of brain, lung, and liver metastases were identified as independent risk factors for BM in EC patients. Among the seven models developed, the ML model based on LR algorithm demonstrated excellent performance in the internal validation set. The AUC, accuracy, sensitivity, and specificity of this model were 0.831, 0.721, 0.787, and 0.717, respectively.

Conclusion

We have successfully developed an online calculator utilizing a LR model to assist clinicians in accurately assessing the risk of BM in patients with EC. This tool demonstrates high accuracy and specificity, thereby enhancing the development of personalized treatment plans.

Machine learning methods for histopathological image analysis: Updates in 2024

The combination of artificial intelligence and digital pathology has emerged as a transformative force in healthcare and biomedical research. As an update to our 2018 review, this review presents comprehensive analysis of machine learning applications in histopathological image analysis, with focus on the developments since 2018. We highlight significant advances that have expanded the technical capabilities and practical applications of computational pathology. The review examines progress in addressing key challenges in the field as follows: processing of gigapixel whole slide images, insufficient labeled data, multidimensional analysis, domain shifts across institutions, and interpretability of machine learning models. We evaluate emerging trends, such as foundation models and multimodal integration, that are reshaping the field. Overall, our review highlights the potential of machine learning in enhancing both routine pathological analysis and scientific discovery in pathology. By providing this comprehensive overview, this review aims to guide researchers and clinicians in understanding the current state of the pathology image analysis field and its future trajectory.

Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy

Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy. We have constructed and tested iSCLM using retrospective data from 2,387 patients across 10 medical centers and evaluated its discriminative ability in a prospective cohort (132 patients; ChiCTR2300068917). iSCLM achieves areas under receiver operating characteristic curves of 0.846-0.876 across different test cohorts. Computed tomography (CT) and pathological attention heatmaps from Shapley additive explanations and global sort pooling illustrate additional benefits for capturing morphological features through supervised contrastive learning. Specifically, pathological top-ranked tiles exhibit decreased distances to tumor-invasive borders and increased inflammatory cell infiltration in responders compared with non-responders. Moreover, CD11c expression is elevated in responders. The developed interpretable model at the molecular pathology level accurately predicts chemotherapy efficacy.

Powerful microscopy technologies decode spatially organized cellular networks that drive response to immunotherapy in humans

In tumors, immune cells organize into networks of different sizes and composition, including complex tertiary lymphoid structures and recently identified networks centered around the chemokines CXCL9/10/11 and CCL19. New commercially available highly multiplexed microscopy using cyclical RNA in situ hybridization and antibody-based approaches have the potential to establish the organization of the immune response in human tissue and serve as a foundation for future immunology research.

Targeted and cytotoxic inhibitors used in the treatment of breast cancer

Breast cancer is the most commonly diagnosed malignancy and the fifth leading cause of cancer deaths worldwide. Surgery and radiation therapy are localized therapies for early-stage and metastatic breast cancer. The management of breast cancer is determined in large part by the HER2 (human epidermal growth factor receptor 2), HR (hormone receptor), ER (estrogen receptor), and PR (progesterone receptor) status. Our views of breast cancer are evolving as its molecular hallmarks are examined, which now include immunohistochemical markers (ER, PR, HER2, and proliferation marker protein Ki-67), genomic markers (BRCA1/2 and PIK3CA), and immunomarkers (tumor-infiltrating lymphocytes and PDL1). About two-thirds of malignancies of the breast are HR-positive/HER2-negative; accordingly, endocrine-based therapy is a major treatment option for these patients. Hormonal or endocrine therapy includes selective estrogen receptor modulators (SERMs) such as raloxifene, tamoxifen and toremifene, selective estrogen-receptor degraders (SERDs) including elacestrant and fulvestrant, and aromatase inhibitors such as anastrozole, letrozole, and exemestane. A variety of cytotoxic chemotherapeutic agents are used to treat HR-negative breast cancer patients. These agents include taxanes (docetaxel, nab-paclitaxel, and paclitaxel), anthracyclines (doxorubicin, epirubicin), anti-metabolites (capecitabine, gemcitabine, fluorouracil, methotrexate), alkylating agents (carboplatin, cisplatin, and cyclophosphamide), and drugs that target microtubules (eribulin, ixabepilone, ado-trastuzumab emtansine). Patients with ER-positive tumors are treated with 5-10 years of endocrine therapy and chemotherapy. For patients with metastatic breast cancer, standard first-line and follow-up therapy options include targeted approaches such as CDK4/6 inhibitors, PI3K inhibitors, PARP inhibitors, and anti-PDL1 immunotherapy, depending on the tumor type and molecular profile.

Understanding the interplay between extracellular matrix topology and tumor-immune interactions: Challenges and opportunities

Modern cancer management comprises a variety of treatment strategies. Immunotherapy, while successful at treating many cancer subtypes, is often hindered by tumor immune evasion and T cell exhaustion as a result of an immunosuppressive tumor microenvironment (TME). In solid malignancies, the extracellular matrix (ECM) embedded within the TME plays a central role in T cell recognition and cancer growth by providing structural support and regulating cell behavior. Relative to healthy tissues, tumor associated ECM signatures include increased fiber density and alignment. These and other differentiating features contributed to variation in clinically observed tumor-specific ECM configurations, collectively referred to as Tumor-Associated Collagen Signatures (TACS) 1-3. TACS is associated with disease progression and immune evasion. This review explores our current understanding of how ECM geometry influences the behaviors of both immune cells and tumor cells, which in turn impacts treatment efficacy and cancer evolutionary progression. We discuss the effects of ECM remodeling on cancer cells and T cell behavior and review recent in silico models of cancer-immune interactions.

A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients

BACKGROUND

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME.

METHODS

We retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing.

RESULTS AND CONCLUSION

During the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score potentially indicated more infiltration of CD8+ T cells in the stroma as well as a greater probability of MCL-1 amplification and EP300 mutation. At the single-cell level, MCL-1 was associated with TNFA-NFKB signaling and apoptosis-related processes. Hopefully, this noninvasive model could act as a biomarker for immunotherapy, potentially facilitating precision medicine in the management of ES-SCLC.

A multimodal generative AI copilot for human pathology

Computational pathology1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making.

Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

Bridging Histopathology and Radiomics Toward Prognosis of Metastasis in Early Breast Cancer

Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.

GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer

Paclitaxel and anthracycline-based chemotherapy is one of the standard treatment options for breast cancer. However, only about 6-30% of breast cancer patients achieved a pathological complete response (pCR), and the mechanism responsible for the difference is still unclear. In this study, random forest algorithm was used to screen feature genes, and artificial neural network (ANN) algorithm was used to construct an ANN model for predicting the efficacy of neoadjuvant chemotherapy for breast cancer. Furthermore, digital pathology, cytology, and molecular biology experiments were used to verify the relationship between the efficacy of neoadjuvant chemotherapy and immune ecology. It was found that paclitaxel and doxorubicin, an anthracycline, could induce typical pyroptosis and bubbling in breast cancer cells, accompanied by gasdermin E (GSDME) cleavage. Paclitaxel with LDH release and Annexin V/PI doubule positive cell populations, and accompanied by the increased release of damage-associated molecular patterns, HMGB1 and ATP. Cell coculture experiments also demonstrated enhanced phagocytosis of macrophages and increased the levels of IFN-γ and IL-2 secretion after paclitaxel treatment. Mechanistically, GSDME may mediate paclitaxel and doxorubicin-induced pyroptosis in breast cancer cells through the caspase-9/caspase-3 pathway, activate anti-tumor immunity, and promote the efficacy of paclitaxel and anthracycline-based neoadjuvant chemotherapy. This study has practical guiding significance for the precision treatment of breast cancer, and can also provide ideas for understanding molecular mechanisms related to the chemotherapy sensitivity.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

PURPOSE

To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.

METHODS

In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.

RESULTS

Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumors as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumors as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumors, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups (N = 124) was 71.0%, with a Cohen's kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups.

CONCLUSION

The results from this clinical evaluation of image-based risk stratification shows a considerable agreement to an established gene expression assay in routine breast pathology.

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field.

SIGNIFICANCE

AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

BACKGROUND

Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.

RESULTS

tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon.

CONCLUSIONS

tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

Towards a general-purpose foundation model for computational pathology

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma

BACKGROUND

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.

RESULTS

We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017).

CONCLUSIONS

We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

Personalised graded psychological intervention on negative emotion and quality of life in patients with breast cancer

BACKGROUND

Psychological factors are a risk factor for the incidence of breast cancer and have a significant impact on patient prognosis.

OBJECTIVE

The present study aims to investigate the effects of personalised graded psychological intervention on negative emotion and quality of life in patients with breast cancer.

METHODS

A total of 200 patients with breast cancer were randomly divided into two groups: an experimental group (n= 100) and control group (n= 100). Both groups received routine nursing care. The experimental group received personalised graded psychological intervention care, and the control group received routine nursing measures. After 2 months of standard treatment, the patients' quality of life and negative emotions were evaluated using the self-rating depression scale (SDS), self-rating anxiety scale (SAS), social support rating scale (SSRS) and quality of life measurement scale (FACT-B) scoring criteria.

RESULTS

There were no significant differences in the general data between the two groups (p> 0.05). Furthermore, there were no significant differences in the SDS, SAS, SSRS and FACT-B scores between the two groups before personalised graded psychological intervention (p> 0.05). After the intervention, the experimental group exhibited an improved nursing effect compared with the control group. The SDS and SAS scores were lower in the experimental group than in the control group (p< 0.05); after the intervention, the SDS and SAS scores were significantly lower in the experimental group than in the control group (p< 0.05). The SSRS and FACT-B scores were higher in the experimental group than in the control group (p< 0.05), and the experimental group's post-intervention SSRS and FACT-B scores were significantly higher than before the intervention (p< 0.05).

CONCLUSIONS

The use of personalised graded psychological intervention for the nursing of patients with breast cancer in clinical practice can significantly reduce patients' negative emotions as well as improve positive emotions and quality of life; thus, this method can be popularised in the nursing process.