Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Long-term survival varies among hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancer patients and is seriously impaired by metastasis. Chromosomal instability (CIN) was one of the key drivers of breast cancer metastasis. Here we evaluate CIN and 10-year invasive disease-free survival (iDFS) and overall survival (OS) in HR+/HER2-- breast cancer. In this large-scale, multiple-site, retrospective study, 354 HR+/HER2- breast cancer patients were recruited. Of these, 204 patients were used for internal training, 70 for external validation, and 80 for cross-validation. All medical records were carefully reviewed to obtain the disease recurrence information. Formalin-fixed paraffin-embedded tissue samples were collected, followed by low-pass whole-genome sequencing with a median genome coverage of 1.86X using minimal 1 ng DNA input. CIN was then assessed using a customized bioinformatics workflow. Three or more instances of CIN per sample was defined as high CIN and the frequency was 42.2% (86/204) in the internal cohort. High CIN correlated significantly with increased lymph node metastasis, vascular invasion, progesterone receptor negative status, HER2 low, worse pathological type, and performed as an independent prognostic factor for HR+/- breast cancer. Patients with high CIN had shorter iDFS and OS than those with low CIN [10-year iDFS 11.1% versus 82.2%, hazard ratio (HR) = 11.12, p < 0.01; 10-year OS 45.7% versus 94.3%, HR = 14.17, p < 0.01]. These findings were validated in two external cohorts with 70 breast cancer patients. Moreover, high CIN could predict the prognosis more accurately than Adjuvant! Online score (10-year iDFS 11.1% versus 48.6%, HR = 2.71, p < 0.01). Cross-validation analysis found that high consistency (83.8%) was observed between CIN and MammaPrint score, while only 45% between CIN and Adjuvant! Online score. In conclusion, high CIN is an independent prognostic indicator for HR+/HER2- breast cancer with shorter iDFS and OS and holds promise for predicting recurrence and metastasis.

Molecular features are incorporated into the integrated diagnostic system for adult diffuse gliomas. Of these, copy number variation (CNV) markers, including both arm-level (1p/19q codeletion, +7/-10 signature) and gene-level (EGFR gene amplification, CDKN2A/B homozygous deletion) changes, have revolutionized the diagnostic paradigm by updating the subtyping and grading schemes. Shallow whole genome sequencing (sWGS) has been widely used for CNV detection due to its cost-effectiveness and versatility. However, the parallel detection of glioma-associated CNV markers using sWGS has not been optimized in a clinical setting. Herein, we established a model-based approach to classify the CNV status of glioma-associated diagnostic markers with a single test. To enhance its clinical utility, we carried out hypothesis testing model-based analysis through the estimation of copy ratio fluctuation level, which was implemented individually and independently and, thus, avoided the necessity for normal controls. Besides, the customization of required minimal tumor fraction (TF) was evaluated and recommended for each glioma-associated marker to ensure robust classification. As a result, with 1× sequencing depth and 0.05 TF, arm-level CNVs could be reliably detected with at least 99.5% sensitivity and specificity. For EGFR gene amplification and CDKN2A/B homozygous deletion, the corresponding TF limits were 0.15 and 0.45 to ensure the evaluation metrics were both higher than 97%. Furthermore, we applied the algorithm to an independent glioma cohort and observed the expected sample distribution and prognostic stratification patterns. In conclusion, we provide a clinically applicable algorithm to classify the CNV status of glioma-associated markers in parallel.

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.

Homologous recombination deficiency (HRD) represents an impairment in the homologous recombination repair (HRR) pathway, crucial for repairing DNA double-strand breaks and contributing to genomic instability in cancer. The HRD score may be a more reliable biomarker than HRR-related gene mutations for identifying patients sensitive to poly(ADP-ribose) polymerase inhibitors. Despite its relevance in various cancers, the HRD score remains underexplored in esophageal squamous cell carcinoma (ESCC). We retrospectively analyzed HRD scores in 96 ESCC patients, examining correlations with clinical characteristics and survival outcomes, and validated our findings using the TCGA dataset. Genomic sequencing utilized a custom superHRD next-generation sequencing panel, and HRD scores were calculated from 54,000 single-nucleotide polymorphisms using Kruskal-Wallis rank-sum tests and two cut-off points for analysis. Higher HRD scores correlated with advanced tumor stages, recurrence, and mutations in TP53 and ABCB1, while APC mutations were linked to lower HRD scores. Patients with high HRD scores had significantly shorter disease-free survival (p = 0.013) and a trend toward shorter overall survival (OS) (p = 0.005), particularly those not receiving adjuvant therapy. Conversely, HRD-high patients undergoing adjuvant therapy showed a trend toward longer OS (p = 0.015). Multivariate analysis identified HRD as an independent prognostic factor (hazard ratio = 2.814 for recurrence, p = 0.015). Validation with the TCGA dataset supported these findings. This study highlights the associations between HRD scores, clinical characteristics, and genomic mutations in ESCC, suggesting HRD as a potential prognostic biomarker. HRD assessment may aid in patient stratification and personalized treatment strategies, warranting further investigation to validate the therapeutic implications of HRD scores in ESCC.

The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro-oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro-oncology cases from free-text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT-4o, Claude-3.5-sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval-Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice.

Currently, the available literature provides insufficient support to differentiate between primary ovarian neuroendocrine tumors (PON) and neuroendocrine ovarian metastases (NOM) in patients. For this reason, patients with a well-differentiated ovarian neuroendocrine tumor (NET) were identified through electronic patient records and a nationwide search between 1991 and 2023. Clinical characteristics were collected from electronic patient files. This resulted in the inclusion of 71 patients with NOM and 17 patients with PON. Histologic material was stained for Ki67, SSTR2a, CDX2, PAX8, TTF1, SATB2, ISLET1, OTP, PDX1, and ARX. DNA methylation analysis was performed on a subset of cases. All PON were unilateral and nine were found within a teratoma (PON-T+). A total of 78% of NOM were bilateral, and none were associated with a teratoma. PON without teratomous components (PON-T-) displayed a similar insular growth pattern and immunohistochemistry as NOM (p > 0.05). When compared with PON-T+, PON-T- more frequently displayed ISLET1 positivity and were larger, and patients were older at diagnosis (p < 0.05). Unsupervised analysis of DNA methylation profiles from tumors of ovarian (n = 16), pancreatic (n = 22), ileal (n = 10), and rectal (n = 7) origin revealed that four of five PON-T- clustered together with NOM and ileal NET, whereas four of five PON-T+ grouped with rectum NET. In conclusion, unilateral ovarian NET within a teratoma should be treated as a PON. Ovarian NET localizations without teratomous components have a molecular profile analogous to midgut NET metastases. For these patients, a thorough review of imaging should be performed to identify a possible undetected midgut NET and a corresponding follow-up strategy may be recommended.

Despite the existence of established standards and guidelines for pathology reporting, many pathology reports are still written in unstructured free text. Extracting information from these reports and formatting it according to a standard is crucial for consistent interpretation. Automated information extraction from unstructured pathology reports is a challenging task, as it requires accurately interpreting medical terminologies and context-dependent details. In this work, we present a practical approach for automatically extracting information from unstructured pathology reports or scanned paper reports utilising a large multimodal model. This framework uses context-aware prompting strategies to extract values of individual fields, such as grade, size, etc. from pathology reports. A unique feature of the proposed approach is that it assigns a confidence value indicating the correctness of the model's extraction for each field and generates a structured report in line with national pathology guidelines in human and machine-readable formats. We have analysed the extraction performance in terms of accuracy and kappa scores, and the quality of the confidence scores assigned by the model. We have also evaluated the prognostic value of the extracted fields and feature embeddings of the raw text. Results showed that the model can accurately extract information with an accuracy and kappa score up to 0.99 and 0.98, respectively. Our results indicate that confidence scores are an effective indicator of the correctness of the extracted information achieving an area under the receiver operating characteristic curve up to 0.93 thus enabling automatic flagging of extraction errors. Our analysis further reveals that, as expected, information extracted from pathology reports is highly prognostically relevant. The framework demo is available at: https://labieb.dcs.warwick.ac.uk/. Information extracted from pathology reports of colorectal cancer cases in the cancer genome atlas using the proposed approach and its code are available at: https://github.com/EtharZaid/Labieb.

Angiogenesis is recognized as a hallmark of cancer, and vascular endothelial growth factor (VEGF) is a key regulator of the angiogenic process and is related to cancer progression. Anti-VEGF therapy has been tried but with limited success and without useful stratification for angiogenesis markers. Further, the landscape of VEGF single nucleotide polymorphisms (SNPs) in breast cancer and their clinical relevance is not well studied, and their relation to tissue-based angiogenesis markers has not been explored. Here, we studied a selection of VEGFA SNPs in nontumor lymph nodes from a population-based breast cancer cohort (n = 544), and their relation to clinicopathologic variables, vascular tissue metrics, and breast cancer-specific survival. Two of the SNP candidates (rs833068GA genotype and rs25648CC genotype) showed associations with angiogenesis tissue markers, and the VEGFA rs833068GA genotype was associated with breast cancer-specific survival among ER-negative cases. We also found trends of association between the rs699947CA genotype and large tumor diameter and ER-negative tumors, and between the rs3025039CC genotype and large tumor diameter. Our findings indicate some associations between certain VEGF SNPs, in particular the rs833068GA genotype, and both vascular metrics and patient survival. These findings and their potential implications need to be validated by independent studies.

Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.

Antibody-drug conjugates (ADCs) directed to trophoblast cell surface antigen 2 (TROP2) have gained approval as a therapeutic option for advanced triple-negative breast cancer, and TROP2 expression has been linked to unfavourable outcomes in various malignancies. In colorectal carcinoma (CRC), there is still a lack of comprehensive studies on its expression frequency and its prognostic implications in relation to the main clinicopathological parameters. We examined the expression of TROP2 in a large cohort of 1,052 CRC cases and correlated our findings with histopathological and molecular parameters, tumour stage, and patient outcomes. TROP2 was heterogeneously expressed in 214/1,052 CRCs (20.3%), with only a fraction of strongly positive tumours. TROP2 expression significantly correlated with an invasive histological phenotype (e.g. increased tumour budding/aggressive histopathological subtypes), advanced tumour stage, microsatellite stable tumours, and p53 alterations. While TROP2 expression was prognostic in univariable analyses of the overall cohort (e.g. for disease-free survival, p < 0.001), it exhibited distinct variations among important clinicopathological subgroups (e.g. right- versus left-sided CRC, microsatellite stable versus unstable CRC, Union for International Cancer Control [UICC] stages) and lost its significance in multivariable analyses that included stage and CRC histopathology. In summary, TROP2 is quite frequently expressed in CRC and associated with an aggressive histopathological phenotype and microsatellite stable tumours. Future clinical trials investigating anti-TROP2 ADCs should acknowledge the observed intratumoural heterogeneity, given that only a subset of TROP2-expressing CRC show strong positivity. The prognostic implications of TROP2 are complex and show substantial variations across crucial clinicopathological subgroups, thus indicating that TROP2 is a suboptimal parameter to predict patient prognosis.

Lineage plasticity in small cell lung carcinoma (SCLC) causes therapeutic difficulties. This study aimed to investigate the pathological findings of plasticity in SCLC, focusing on combined SCLC, and elucidate the involvement of YAP1 and other transcription factors. We analysed 100 surgically resected SCLCs through detailed morphological observations and immunohistochemistry for YAP1 and other transcription factors. Component-by-component next-generation sequencing (n = 15 pairs) and immunohistochemistry (n = 35 pairs) were performed on the combined SCLCs. Compared with pure SCLCs (n = 65), combined SCLCs (n = 35) showed a significantly larger size, higher expression of NEUROD1, and higher frequency of double-positive transcription factors (p = 0.0009, 0.04, and 0.019, respectively). Notably, 34% of the combined SCLCs showed morphological mosaic patterns with unclear boundaries between the SCLC and its partner. Combined SCLCs not only had unique histotypes as partners but also represented different lineage plasticity within the partner. NEUROD1-dominant combined SCLCs had a significantly higher proportion of adenocarcinomas as partners, whereas POU2F3-dominant combined SCLCs had a significantly higher proportion of squamous cell carcinomas as partners (p = 0.006 and p = 0.0006, respectively). YAP1 expression in SCLC components was found in 80% of combined SCLCs and 62% of pure SCLCs, often showing mosaic-like expression. Among the combined SCLCs with component-specific analysis, the identical TP53 mutation was found in 10 pairs, and the identical Rb1 abnormality was found in 2 pairs. On immunohistochemistry, the same abnormal p53 pattern was found in 34 pairs, and Rb1 loss was found in 24 pairs. In conclusion, combined SCLC shows a variety of pathological plasticity. Although combined SCLC is more plastic than pure SCLC, pure SCLC is also a phenotypically plastic tumour. The morphological mosaic pattern and YAP1 mosaic-like expression may represent ongoing lineage plasticity. This study also identified the relationship between transcription factors and partners in combined SCLC. Transcription factors may be involved in differentiating specific cell lineages beyond just 'neuroendocrine'.

Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory.

Homologous recombination deficiency (HRD) score is a reliable indicator of genomic instability. The significance of HRD in nasopharyngeal carcinoma (NPC), particularly its influence on prognosis and the immune microenvironment, has yet to be adequately explored. Understanding HRD status comprehensively can offer valuable insights for guiding precision treatment. We utilised three cohorts to investigate HRD status in NPC: the Zhujiang cohort from local collection and the Hong Kong (SRA288429) and Singapore (SRP035573) cohorts from public datasets. The GATK (genome analysis toolkit) best practice process was employed to investigate germline and somatic BRCA1/2 mutations and various bioinformatics tools and algorithms to examine the association between HRD status and clinical molecular characteristics. We found that individuals with a negative HRD status (no-HRD) exhibited a higher risk of recurrence [hazard ratio (HR), 1.43; 95% confidence interval (CI), 2.03-333.76; p = 0.012] in the Zhujiang cohort, whereas, in the Singapore cohort, they experienced a higher risk of mortality (HR, 26.04; 95% CI, 1.43-34.21; p = 0.016) compared with those in the HRD group. In vitro experiments demonstrated that NPC cells with BRCA1 knockdown exhibit heightened sensitivity to chemoradiotherapy. Furthermore, the HRD group showed significantly higher tumour mutational burden and tumour neoantigen burden levels than the no-HRD group. Immune infiltration analysis indicated that HRD tissues tend to have a non-inflamed tumour microenvironment. In conclusion, patients with HRD exhibit a comparatively favourable prognosis in NPC, possibly associated with a non-inflammatory immune microenvironment. These findings have positive implications for treatment stratification, enabling the selection of more precise and effective therapeutic approaches and aiding in the prediction of treatment response and prognosis to a certain extent.

The gold standard for enrollment and endpoint assessment in metabolic dysfunction-associated steatosis clinical trials is histologic assessment of a liver biopsy performed on glass slides. However, obtaining the evaluations from several expert pathologists on glass is challenging, as shipping the slides around the country or around the world is time-consuming and comes with the hazards of slide breakage. This study demonstrated that pathologic assessment of disease activity in steatohepatitis, performed using digital images on the AISight whole slide image management system, yields results that are comparable to those obtained using glass slides. The accuracy of scoring for steatohepatitis (nonalcoholic fatty liver disease activity score ≥4 with ≥1 for each feature and absence of atypical features suggestive of other liver disease) performed on the system was evaluated against scoring conducted on glass slides. Both methods were assessed for overall percent agreement with a consensus "ground truth" score (defined as the median score of a panel of three pathologists' glass slides). Each case was also read by three different pathologists, once on glass and once digitally with a minimum 2-week washout period between the modalities. It was demonstrated that the average agreement across three pathologists of digital scoring with ground truth was noninferior to the average agreement of glass scoring with ground truth [noninferiority margin: -0.05; difference: -0.001; 95% CI: (-0.027, 0.026); and p < 0.0001]. For each pathologist, there was a similar average agreement of digital and glass reads with glass ground truth (pathologist A, 0.843 and 0.849; pathologist B, 0.633 and 0.605; and pathologist C, 0.755 and 0.780). Here, we demonstrate that the accuracy of digital reads for steatohepatitis using digital images is equivalent to glass reads in the context of a clinical trial for scoring using the Clinical Research Network scoring system.

Evidence for the tumour-supporting capacities of the tumour stroma has accumulated rapidly in colorectal cancer (CRC). Tumour stroma is composed of heterogeneous cells and components including cancer-associated fibroblasts (CAFs), small vessels, immune cells, and extracellular matrix proteins. The present study examined the characteristics of CAFs and collagen, major components of cancer stroma, by immunohistochemistry and Sirius red staining. The expression status of five independent CAF-related or stromal markers, decorin (DCN), fibroblast activation protein (FAP), podoplanin (PDPN), alpha-smooth muscle actin (ACTA2), and collagen, and their association with clinicopathological features and clinical outcomes were analysed. Patients with DCN-high tumours had a significantly worse 5-year survival rate (57.3% versus 79.0%; p = 0.044). Furthermore, hierarchical clustering analyses for these five markers identified three groups that showed specific characteristics: a solid group (cancer cell-rich, DCNPDPN); a PDPN-dominant group (DCNPDPN); and a DCN-dominant group (DCNPDPN), with a significant association with patient survival (p = 0.0085). Cox proportional hazards model identified the PDPN-dominant group (hazard ratio = 0.50, 95% CI = 0.26-0.96, p = 0.037) as a potential favourable factor compared with the DCN-dominant group. Of note, DCN-dominant tumours showed the most advanced pT stage and contained the lowest number of CD8+ and FOXP3+ immune cells. This study has revealed that immunohistochemistry and special staining of five stromal factors with hierarchical clustering analyses could be used for the prognostication of patients with CRC. Cancer stroma-targeting therapies may be candidate treatments for patients with CRC.

Mesonephric-type (or -like) adenocarcinomas (MAs) of the ovary are an uncommon and aggressive histotype. They appear to arise through transdifferentiation from Müllerian lesions creating diagnostic challenges. Thus, we aimed to develop a histologic and immunohistochemical (IHC) approach to optimize the identification of MA over its histologic mimics, such as ovarian endometrioid carcinoma (EC). First, we screened 1,537 ovarian epithelial neoplasms with a four-marker IHC panel of GATA3, TTF1, ER, and PR followed by a morphological review of EC to identify MA in retrospective cohorts. Interobserver reproducibility for the distinction of MA versus EC was assessed in 66 cases initially without and subsequently with IHC information (four-marker panel). Expression of PAX2, CD10, and calretinin was evaluated separately, and survival analyses were performed. We identified 23 MAs from which 22 were among 385 cases initially reported as EC (5.7%) and 1 as clear cell carcinoma. The interobserver reproducibility increased from fair to substantial (κ = 0.376-0.727) with the integration of the four-marker IHC panel. PAX2 was the single most sensitive and specific marker to distinguish MA from EC and could be used as a first-line marker together with ER/PR and GATA3/TTF1. Patients with MA had significantly increased risk of earlier death from disease (hazard ratio = 3.08; 95% CI, 1.62-5.85; p < 0.0001) compared with patients with EC, when adjusted for age, stage, and p53 status. A diagnosis of MA has prognostic implications for stage I disease, and due to the subtlety of morphological features in some tumors, a low threshold for ancillary testing is recommended.

Tumor structure is heterogeneous and complex, and it is difficult to obtain complete characteristics by two-dimensional analysis. The aim of this study was to visualize and characterize volumetric vascular information of clear cell renal cell carcinoma (ccRCC) tumors using whole tissue phenotyping and three-dimensional light-sheet microscopy. Here, we used the diagnosing immunolabeled paraffin-embedded cleared organs pipeline for tissue clearing, immunolabeling, and three-dimensional imaging. The spatial distributions of CD34, which targets blood vessels, and LYVE-1, which targets lymphatic vessels, were examined by calculating three-dimensional density, vessel length, vessel radius, and density curves, such as skewness, kurtosis, and variance of the expression. We then examined those associations with ccRCC outcomes and genetic alteration state. Formalin-fixed paraffin-embedded tumor samples from 46 ccRCC patients were included in the study. Receiver operating characteristic curve analyses revealed the associations between blood vessel and lymphatic vessel distributions and pathological factors such as a high nuclear grade, large tumor size, and the presence of venous invasion. Furthermore, three-dimensional imaging parameters stratified ccRCC patients regarding survival outcomes. An analysis of genomic alterations based on volumetric vascular information parameters revealed that PI3K-mTOR pathway mutations related to the blood vessel radius were significantly different. Collectively, we have shown that the spatial elucidation of volumetric vasculature information could be prognostic and may serve as a new biomarker for genomic alterations. High-end tissue clearing techniques and volumetric immunohistochemistry enable three-dimensional analysis of tumors, leading to a better understanding of the microvascular structure in the tumor space.

Oxidative stress and the immune microenvironment both contribute to the pathogenesis of esophageal squamous cell carcinoma (ESCC). However, their interrelationships remain poorly understood. We aimed to examine the status of key molecules involved in oxidative stress and the immune microenvironment, as well as their relationships with each other and with clinicopathological features and prognosis in ESCC. The expression of programmed death-ligand 1 (PD-L1), CD8, nuclear factor erythroid-2 related factor-2 (NRF2), and NAD(P)H quinone oxidoreductase 1 (NQO1) was detected using immunohistochemistry in tissue samples from 176 patients with ESCC. We employed both combined positive score (CPS) and tumor proportion score (TPS) to evaluate PD-L1 expression and found a positive correlation between CPS and TPS. Notably, PD-L1 expression, as assessed by either CPS or TPS, was positively correlated with both NRF2 nuclear score and NQO1 score in stage II-IV ESCC. We also observed a positive correlation between the density of CD8+ T cells and PD-L1 expression. Furthermore, high levels of PD-L1 CPS, but not TPS, were associated with advanced TNM stage and lymph node metastases. Moreover, both PD-L1 CPS and the nuclear expression of NRF2 were found to be predictive of shorter overall survival in stage II-IV ESCC. By using the Mandard-tumor regression grading (TRG) system to evaluate the pathological response of tumors to neoadjuvant chemotherapy (NACT), we found that the TRG-5 group had higher NRF2 nuclear score, PD-L1 CPS, and TPS in pre-NACT biopsy samples compared with the TRG-3 + 4 group. The NQO1 scores of post-NACT surgical specimens were significantly higher in the TRG-5 group than in the TRG 3 + 4 group. In conclusion, the expression of PD-L1 is associated with aberrant NRF2 signaling pathway, advanced TNM stage, lymph node metastases, and unfavorable prognosis. The dysregulation of PD-L1 and aberrant activation of the NRF2 signaling pathway are implicated in resistance to NACT. Our findings shed light on the complex interrelationships between oxidative stress and the immune microenvironment in ESCC, which may have implications for personalized therapies and improved patient outcomes.