Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Artificial Intelligence-Based Single-Cell Analysis as a Next-Generation Histologic Grading Approach in Colorectal Cancer: Prognostic Role and Tumor Biology Assessment

The management of colorectal carcinoma (CRC) relies on pathological interpretation. Digital pathology approaches allow for development of new potent artificial intelligence-based prognostic parameters. The study aimed to develop an artificial intelligence-based image analysis platform allowing fully automatized, quantitative, and explainable tumor microenvironment analysis and extraction of prognostic information from hematoxylin and eosin-stained whole-slide images of CRC patients. Three well--characterized, multi-institutional patient cohorts were included (patient n = 1438, whole-slide image n > 2400). The developed image analysis platform implements quality control and established algorithms to segment tissue and detect cell types. It enabled systematic analysis of immune infiltrate, assessing its prognostic relevance, intratumoral heterogeneity, and biological concepts across multiple survival end points. Analyzing single-cell types and their combinations reveals independent, prognostic parameters, highlighting significant intratumoral heterogeneity, especially in the biopsy setting, which must be accounted for. A key morphologic concept related to tumor control by the immune system is described, resulting in a capable, independent prognostic parameter (tumor "out of control"). Our findings have direct clinical implications and can be used as a foundation for updating the existing CRC grading systems.

Pathomics Image Analysis of Tumor Infiltrating Lymphocytes (TILs) in Colon Cancer

We developed a deep learning Pathomics image analysis workflow to generate spatial Tumor-TIL maps to visualize and quantify the abundance and spatial distribution of tumor infiltrating lymphocytes (TILs) in colon cancer. Colon cancer and lymphocyte detection in hematoxylin and eosin (H&E) stained whole slide images (WSIs) has revealed complex immuno-oncologic interactions that form TIL-rich and TIL-poor tumor habitats, which are unique in each patient sample. We compute Tumor%, total lymphocyte%, and TILs% as the proportion of the colon cancer microenvironment occupied by intratumoral lymphocytes for each WSI. Kaplan-Meier survival analyses and multivariate Cox regression were utilized to evaluate the prognostic significance of TILs% as a Pathomics biomarker. High TILs% was associated with improved overall survival (OS) and progression-free interval (PFI) in localized and metastatic colon cancer and other clinicopathologic variables, supporting the routine use of Pathomics Tumor-TIL mapping in biomedical research, clinical trials, laboratory medicine, and precision oncology.

Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning

The density of tumor-infiltrating lymphocytes (TILs) serves as a valuable indicator for predicting anti-tumor responses, but its broad impact across various types of cancers remains underexplored. We introduce TILScout, a pan-cancer deep-learning approach to compute patch-level TIL scores from whole slide images (WSIs). TILScout achieved accuracies of 0.9787 and 0.9628, and AUCs of 0.9988 and 0.9934 in classifying WSI patches into three categories-TIL-positive, TIL-negative, and other/necrotic-on validation and independent test sets, respectively, surpassing previous studies. The biological significance of TILScout-derived TIL scores across 28 cancers was validated through comprehensive functional and correlational analyses. A consistent decrease in TIL scores with an increase in cancer stage provides direct evidence that the lower TIL content may stimulate cancer progression. Additionally, TIL scores correlated with immune checkpoint gene expression and genomic variation in common cancer driver genes. Our comprehensive pan-cancer survey highlights the critical prognostic significance of TILs within the tumor microenvironment.

Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer

BACKGROUND

The presence of tumour-infiltrating lymphocytes (TILs) is a well-established prognostic biomarker across multiple cancer types, with higher TIL counts being associated with lower recurrence rates and improved patient survival. We aimed to examine whether an automated intraepithelial TIL (iTIL) assessment could stratify patients by risk, with the ability to generalise across independent patient cohorts, using routine H&E slides of colorectal cancer (CRC). To our knowledge, no other existing fully automated iTIL system has demonstrated this capability.

METHODS

An automated method employing deep neural networks was developed to enumerate iTILs in H&E slides of CRC. The method was applied to a Stage III discovery cohort (n = 353) to identify an optimal threshold of 17 iTILs per-mm2 tumour for stratifying relapse-free survival. Using this threshold, patients from two independent Stage II-III validation cohorts (n = 1070, n = 885) were classified as "TIL-High" or "TIL-Low".

RESULTS

Significant stratification was observed in terms of overall survival for a combined validation cohort univariate (HR 1.67, 95%CI 1.39-2.00; p < 0.001) and multivariate (HR 1.37, 95%CI 1.13-1.66; p = 0.001) analysis. Our iTIL classifier was an independent prognostic factor within proficient DNA mismatch repair (pMMR) Stage II CRC cases with clinical high-risk features. Of these, those classified as TIL-High had outcomes similar to pMMR clinical low risk cases, and those classified TIL-Low had significantly poorer outcomes (univariate HR 2.38, 95%CI 1.57-3.61; p < 0.001, multivariate HR 2.17, 95%CI 1.42-3.33; p < 0.001).

CONCLUSIONS

Our deep learning method is the first fully automated system to stratify patient outcome by analysing TILs in H&E slides of CRC, that has shown generalisation capabilities across multiple independent cohorts.

In triple-negative breast cancer, fibrotic focus, the mitotic activity index and tumour-infiltrating lymphocytes have independent prognostic value: an observational population-based cohort study with very long follow-up

AIMS

Triple-negative breast cancer (TNBC) is prognostically and therapeutically heterogeneous. The mitotic activity index (MAI) and fibrotic focus (FF) have been established as predictors in non-TNBC but not in TNBC. Late distant metastases occur in TNBC, but previous studies had short follow-up. High stromal tumour-infiltrating lymphocytes (sTILs) are prognostically favourable, but prognostic sTILs-thresholds are not well assessed. We evaluated prognostic/predictive characteristics in an observational population-based cohort of 231 consecutive TNBC patients with long follow-up.

METHODS

MAI, FF, sTILs and other characteristics were analysed with standard receiver operating characteristic curve analysis, percentile-derived prognostic thresholds, univariate and multivariate survival methods. A TNBC index and decision tree were assessed for distant metastasis-free survival.

RESULTS

Long follow-up was decisive: 7% of patients developed late distant metastases. In agreement with the aggressive nature of TNBC, the strongest prognostic MAI-threshold was 5 (p=0.001), lower than that for non-TNBC phenotypes. Lymph-node (LN) status (p=0.0003), FF (p=0.002), MAI5 (p=0.009) and sTILs (threshold 40%, p=0.003) were multivariable based significant and independent prognosticators, but no other characteristics (age, tumour size and grade). LN status was the strongest prognosticator, followed by FF, MAI5 and sTILs40. Subgroup analyses of patients undergoing adjuvant chemotherapy (ACT) showed that only FF and sTILs had significant prognostic value, while LN-positivity and the combination of LN-positivity and MAI≥5 could be a predictive factor for ACT outcome.

CONCLUSIONS

LN status, MAI5, FF and sTILs40 are prognostic factors in TNBC patients. In TNBC patients who have undergone ACT, the combination of LN-positivity and MAI5 is predictive for response to treatment.

Tumor infiltrating lymphocytes (TILs) - Pathologia, quo vadis? - A global survey

Tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment have become increasingly important in cancer research, and immunotherapy has achieved major breakthroughs in improving patient outcomes. Despite the significant role of the pathologist in identifying, subtyping and reporting TILs, the implementation and assessment of TILs in pathology routine remains vague. To assess the actual use of TILs in routine clinical practice, a formal standardized questionnaire was disseminated on two social media platforms ("X" and LinkedIn) and by email in June 2024. Based on the results, we conducted a literature review on TILs via Medline/Pubmed in the two most scored and reported entities, namely malignant melanoma and colorectal cancer (CRC). 77 participants from 24 different countries around the world, mostly pathologists (n = 63, 82.0 %), completed the survey. More than half of the participants do not assess or report TILs in their daily (clinical) practice, a trend consistent across the countries included in the study. A variety of methods are used to report TILs, ranging from Artificial Intelligence (AI)-based scoring algorithms to quantification by eyeballing. Despite recognizing the importance of TIL assessment in clinical routine, many participants find it time-consuming and express a strong preference for AI-based quantification. Our survey reflects the perspective of mostly early career pathologists who recognize the importance of TILs in cancer but face challenges in implementation. The development of AI tools and consensus guidelines could alleviate these barriers. In addition, increasing the visibility and understanding of the role of pathologists within the medical community remains critical.

Concurrent Neoadjuvant Chemotherapy and Radiation in Locally Advanced Breast Cancer: Impact on Locoregional Recurrence Rates

Neoadjuvant chemoradiation therapy (NCRT) is an underutilized treatment in breast cancer but may improve outcomes by impacting the tumor immune microenvironment. The aim of this study was to evaluate NCRT's impact on recurrence and the role of tumor-infiltrating lymphocytes (TILs) in treatment response. We hypothesized that NCRT reduces recurrence by upregulating TILs. Patients with locally advanced breast cancer (LABC) were treated with NCRT. Stage IIB to III patients with any molecular subtypes were eligible. The patients were matched for age, stage, and molecular subtype by a propensity score to a concurrent cohort receiving standard neoadjuvant chemotherapy (NCT) followed by adjuvant radiation. The objective of this study was to assess the patients in terms of the pathological complete response (pCR), TIL counts prior to and following treatment, and locoregional recurrence. The median follow-up was 7.2 years. Thirty NCRT patients were successfully matched 1:3 to ninety NCT patients. The NCRT cohort had no regional and locoregional recurrences (p = 0.036, (hazard ratio) HR [0.25], 95% confidence interval (CI) [0.06-0.94] and p = 0.013, HR [0.25], 95% CI [0.08-0.76], respectively), compared to 17.8% of the NCT cohort. The NCRT group had significantly more pCRs, and TILs were increased in the post-treatment pCR specimens. NCRT can improve outcomes in LABC patients, with a higher pCR and significantly lower locoregional recurrence/higher recurrence-free survival. Further trials are needed to evaluate the role of NCRT in all breast cancer patients.

A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks

BACKGROUND

Melanoma is an aggressive form of skin cancer in which tumor-infiltrating lymphocytes (TILs) are a biomarker for recurrence and treatment response. Manual TIL assessment is prone to interobserver variability, and current deep learning models are not publicly accessible or have low performance. Deep learning models, however, have the potential of consistent spatial evaluation of TILs and other immune cell subsets with the potential of improved prognostic and predictive value. To make the development of these models possible, we created the Panoptic Segmentation of nUclei and tissue in advanced MelanomA (PUMA) dataset and assessed the performance of several state-of-the-art deep learning models. In addition, we show how to improve model performance further by using heuristic postprocessing in which nuclei classes are updated based on their tissue localization.

RESULTS

The PUMA dataset includes 155 primary and 155 metastatic melanoma hematoxylin and eosin-stained regions of interest with nuclei and tissue annotations from a single melanoma referral institution. The Hover-NeXt model, trained on the PUMA dataset, demonstrated the best performance for lymphocyte detection, approaching human interobserver agreement. In addition, heuristic postprocessing of deep learning models improved the detection of noncommon classes, such as epithelial nuclei.

CONCLUSION

The PUMA dataset is the first melanoma-specific dataset that can be used to develop melanoma-specific nuclei and tissue segmentation models. These models can, in turn, be used for prognostic and predictive biomarker development. Incorporating tissue and nuclei segmentation is a step toward improved deep learning nuclei segmentation performance. To support the development of these models, this dataset is used in the PUMA challenge.

A novel semi-quantitative scoring method for CD8+ tumor-infiltrating lymphocytes based on infiltration sites in gastric cancer

No established method currently exists for evaluating tumor-infiltrating lymphocytes (TILs) in gastric cancer (GC), and their clinical significance based on infiltration site in GC remains unclear. In this study, we developed a method to evaluate TILs according to their infiltration site as a prognostic marker for GC. We retrospectively analyzed 103 patients with advanced GC who underwent curative resection. TILs located at the invasive margin (TILIM) and the center of tumors (TILCT) were scored semi-quantitatively using immunohistochemical staining of CD8+ T cells. The sum of the TILIM and TILCT scores was defined as the TILs score. Based on this score, patients were classified into low and high TILs groups. Quantitative TILs were also assessed to validate the semi-quantitative scoring method. Furthermore, we confirmed a tumor suppressive effect due to CD8+ T cells co-cultured in GC cell lines in vitro. In the univariate analysis, patients with low TILIM were significantly more likely to be female, younger, and have undifferentiated histological types and deeper tumor invasion compared to those with high TILIM. Similarly, patients with low TILCT had significantly more positive lymph node metastases than those with high TILCT. In the multivariate analysis, deeper tumor invasion and positive lymph node metastasis were identified as independent risk factors for patients with low TILIM and low TILCT, respectively. According to our semi-quantitative TILs scoring method, the low TILs group had significantly poorer prognoses compared to the high TILs group. This group had significantly larger tumor diameters, deeper tumor invasion, and more positive lymph node metastases. Additionally, deeper tumor invasion was an independent risk factor for the low TILs group. Quantitative TILs analysis revealed that the low TILs group had significantly lower TIL levels compared to the high TILs group. In vitro, CD8+ T cells induced apoptosis in GC cells in a concentration-dependent manner. Furthermore, these cells significantly suppressed the proliferative, migratory, and invasive capacities of GC cells. Our simple and versatile semi-quantitative scoring method for CD8+ TILs indicates that CD8+ TILs are sensitive prognostic markers. The low TILs group accurately reflects the low quantitative TIL levels and is associated with poor oncological prognosis.

An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study

Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss' Kappa (BWFK), addresses tissue segmentation predictions, focusing on mitigating disagreements along tissue boundaries. BWFK enhances the accuracy of stromal segmentation, providing a nuanced assessment of inter-observer agreement. The second proposed method, the Distance Based Cell Agreement Algorithm (DBCAA), eliminates the need for ground truth annotations in cell detection predictions. This innovative approach offers versatility across histopathological analyses, overcoming data availability challenges. Both methods were applied to assess inter-observer agreement using a clinical image dataset consisting of 25 images of invasive ductal breast carcinoma tissue, each annotated by four pathologists, serving as a proof-of-principle. Experimental investigations demonstrated that the BWFK method yielded gains of up to 32% compared to the standard Fleiss' Kappa model. Furthermore, a procedure for conducting clinical validations of artificial intelligence (AI) based cell detection methods was elucidated. Thoroughly validated on a clinical dataset, the framework contributes to standardized, reliable, and interpretable inter-observer agreement assessments. This study is the first examination of inter-observer agreements in stromal segmentation and lymphocyte detection for the TILs scoring problem. The study emphasizes the potential impact of these measures in advancing histopathological image analysis, fostering consensus in TILs scoring, and ultimately improving breast cancer diagnostics and treatment planning. The source code and implementation guide for this study are accessible on our GitHub page, and the full clinical dataset is available for academic and research purposes on Kaggle.

Targeting low-risk triple-negative breast cancer: a review on de-escalation strategies for a new era

Triple-negative breast cancer (TNBC) is a subtype of breast cancer lacking estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. Comprising 15-20% of breast cancers, TNBC is typically high-grade, affects younger women, and has a poor prognosis. However, TNBC is heterogeneous, encompassing different molecular subtypes and histological types with distinct molecular drivers, prognoses, and treatment responses. Among these, a subset of low-risk diseases associated with a lower risk of recurrence led to the exploration of de-escalation strategies. This review presents the clinicopathological characteristics of special TNBC with a better prognosis that could be spared from aggressive systemic treatment. We searched the PubMed database for articles that could support treatment de-escalation using the keywords "early-stage", "TNBC", and "low-risk". This article addresses four subgroups of low-risk TNBC: special histological types, tumors with high tumor-infiltrating lymphocytes (TILs), low Ki-67 levels, and early-stage tumors that achieved pathological complete response (pCR). The discussion explores the optimization of treatment options ranging from the omission of any systemic treatment to anthracycline-free possibilities and/or immunotherapies. Identifying these tumors can help personalize treatment, reduce costs and unnecessary toxicity, and contribute to a better quality of life for patients with favorable prognoses. Further studies should explore reliable biomarkers to identify these low-risk diseases precisely.

Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care

Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the BRCA1 and 45% with the BRCA2 gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving HER2 and Ki67 assessments. Personalised medicine benefits from AI's predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.

Tumor-Infiltrating Lymphocyte Scoring in Neoadjuvant-Treated Breast Cancer

Neoadjuvant chemotherapy (NAC) is now the standard of care for patients with locally advanced breast cancer (BC). TIL scoring is prognostic and adds predictive value to the residual cancer burden evaluation after NAC. However, NAC induces changes in the tumor, and the reliability of TIL scoring in post-NAC samples has not yet been studied. H&E- and dual CD3/CD20 chromogenic IHC-stained tissues were scored for stromal and intra-tumoral TIL by two experienced pathologists on pre- and post-treatment BC tissues. Digital TIL scoring was performed using the HALO® image analysis software (version 2.2). In patients with residual disease, we show a good inter-pathologist correlation for stromal TIL on H&E-stained tissues (CCC value 0.73). A good correlation for scoring with both staining methods (CCC 0.81) and the digital TIL scoring (CCC 0.77) was also observed. Overall concordance for TIL scoring in patients with a complete response was however poor. This study reveals there is good reliability for TIL scoring in patients with detectable residual tumors after NAC treatment, which is comparable to the scoring of untreated breast cancer patients. Based on the good consistency observed with digital TIL scoring, the development of a validated algorithm in the future might be advantageous.

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has not been used in routine clinical practice because of reproducibility issues. The current study developed two convolutional neural network models to detect TILs and to determine their spatial location in whole slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8+ T cells and immune gene expressions. Patients with a higher density of intraepithelial TILs had a significantly prolonged overall survival and progression-free survival in multiple cohorts. On the basis of the density of intraepithelial and stromal TILs, patients were classified into three immunophenotypes: immune inflamed, excluded, and desert. The immune-desert subgroup showed the worst prognosis. Gene expression analysis showed that the immune-desert subgroup had lower immune cytolytic activity and T-cell-inflamed gene-expression profile scores, whereas the immune-excluded subgroup had higher expression of interferon-γ and programmed death 1 receptor signaling pathway. The established evaluation method provided detailed and comprehensive quantification of intraepithelial and stromal TILs throughout hematoxylin and eosin-stained slides. It has potential for clinical application for personalized treatment of patients with ovarian cancer.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice

Breast cancer is a prevalent malignancy in the present day, particularly affecting women as one of the most common forms of cancer. A significant portion of patients initially present with localized disease, for which curative treatments are pursued. Conversely, another substantial segment is diagnosed with metastatic disease, which has a worse prognosis. Recent years have witnessed a profound transformation in the prognosis for this latter group, primarily due to the discovery of various biomarkers and the emergence of targeted therapies. These biomarkers, encompassing serological, histological, and genetic indicators, have demonstrated their value across multiple aspects of breast cancer management. They play crucial roles in initial diagnosis, aiding in the detection of relapses during follow-up, guiding the application of targeted treatments, and offering valuable insights for prognostic stratification, especially for highly aggressive tumor types. Molecular markers have now become the keystone of metastatic breast cancer diagnosis, given the diverse array of chemotherapy options and treatment modalities available. These markers signify a transformative shift in the arsenal of therapeutic options against breast cancer. Their diagnostic precision enables the categorization of tumors with elevated risks of recurrence, increased aggressiveness, and heightened mortality. Furthermore, the existence of therapies tailored to target specific molecular anomalies triggers a cascade of changes in tumor behavior. Therefore, the primary objective of this article is to offer a comprehensive review of the clinical, diagnostic, prognostic, and therapeutic utility of the principal biomarkers currently in use, as well as of their clinical impact on metastatic breast cancer. In doing so, our goal is to contribute to a more profound comprehension of this complex disease and, ultimately, to enhance patient outcomes through more precise and effective treatment strategies.

Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.