Automated reporting of cervical biopsies using artificial intelligence.

When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92%. Being aware of signs and symptoms of cervical cancer and early detection greatly improve the chances of successful treatment. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics. The aim is to increase overall efficiency of pathological diagnosis and to have the performance tuned to high sensitivity for malignant cases. Having a tool for triage/identifying cancer and high grade lesions may potentially reduce reporting time by identifying areas of interest in a slide for the pathologist and therefore improving efficiency. We trained and validated our algorithm on 1738 cervical WSIs with one WSI per patient. On the independent test set of 811 WSIs, we achieved 93.4% malignant sensitivity for classifying slides. Recognising a WSI, with our algorithm, takes approximately 1.5 minutes on the NVIDIA Tesla V100 GPU. Whole slide images of different formats (TIFF, iSyntax, and CZI) can be processed using this code, and it is easily extendable to other formats.

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence.

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.

An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD.

Audit of medical (non-targeted) liver biopsy specimen quality, pathology reporting and effect on patient management.

AIMS: To evaluate our medical liver pathology practice and its influence on patient management, using audit templates published by the UK Royal College of Pathologists (RCPath). METHODS: We audited medical liver biopsies reported in our centre in 2019 using RCPath proformas. Data were collected from pathology reports and corresponding electronic patient record. RESULTS: 60 cases were selected for audit from 135 eligible biopsies reported in 2019. 58/60 cases were core biopsies and 2/60 were laparoscopic wedge biopsies. 53/57 (93%) core biopsies with available data met RCPath adequacy criteria (length >15 mm and/or ≥6 portal tracts). Most reports (57/60; 95%) were judged to have helped patient management. 25/60 (42%) biopsy reports helped to clarify the clinical diagnosis and 48/60 (80%) led to altered management. CONCLUSIONS: We demonstrate the utility of the RCPath audit templates, highlighting the clinical value of medical liver biopsies in the diagnostic work-up and management of patients with liver disease.

Weakly supervised learning and interpretability for endometrial whole slide image diagnosis.

Fully supervised learning for whole slide image-based diagnostic tasks in histopathology is problematic due to the requirement for costly and time-consuming manual annotation by experts. Weakly supervised learning that utilizes only slide-level labels during training is becoming more widespread as it relieves this burden, but has not yet been applied to endometrial whole slide images, in iSyntax format. In this work, we apply a weakly supervised learning algorithm to a real-world dataset of this type for the first time, with over 85% validation accuracy and over 87% test accuracy. We then employ interpretability methods including attention heatmapping, feature visualization, and a novel end-to-end saliency-mapping approach to identify distinct morphologies learned by the model and build an understanding of its behavior. These interpretability methods, alongside consultation with expert pathologists, allow us to make comparisons between machine-learned knowledge and consensus in the field. This work contributes to the state of the art by demonstrating a robust practical application of weakly supervised learning on a real-world digital pathology dataset and shows the importance of fine-grained interpretability to support understanding and evaluation of model performance in this high-stakes use case.

Relationship between immune checkpoint proteins, tumour microenvironment characteristics, and prognosis in primary operable colorectal cancer.

The tumour microenvironment is an important factor for colorectal cancer prognosis, affecting the patient's immune response. Immune checkpoints, which regulate the immune functions of lymphocytes, may provide prognostic power. This study aimed to investigate the prognostic value of the immune checkpoints TIM-3, LAG-3 and PD-1 in patients with stage I-III colorectal cancer. Immunohistochemistry was employed to detect TIM-3, LAG-3, PD-1 and PD-L1 in 773 patients with stage I-III colorectal cancer. Immune checkpoint protein expression was assessed in tumour cells using the weighted histoscore, and in immune cells within the stroma using point counting. Scores were analysed for associations with survival and clinical factors. High tumoural LAG-3 (hazard ratio [HR] 1.45 95% confidence interval [CI] 1.00-2.09, p = 0.049) and PD-1 (HR 1.34 95% CI 1.00-1.78, p = 0.047) associated with poor survival, whereas high TIM-3 (HR 0.60 95% CI 0.42-0.84, p = 0.003), LAG-3 (HR 0.58 95% CI 0.40-0.87, p = 0.006) and PD-1 (HR 0.65 95% CI 0.49-0.86, p = 0.002) on immune cells within the stroma associated with improved survival, while PD-L1 in the tumour (p = 0.487) or the immune cells within the stroma (p = 0.298) was not associated with survival. Furthermore, immune cell LAG-3 was independently associated with survival (p = 0.017). Checkpoint expression scores on stromal immune cells were combined into a Combined Immune Checkpoint Stromal Score (CICSS), where CICSS 3 denoted all high, CICSS 2 denoted any two high, and CICSS 1 denoted other combinations. CICSS 3 was associated with improved patient survival (HR 0.57 95% CI 0.42-0.78, p = 0.001). The results suggest that individual and combined high expression of TIM-3, LAG-3, and PD-1 on stromal immune cells are associated with better colorectal cancer prognosis, suggesting there is added value to investigating multiple immune checkpoints simultaneously.

Unusual case of adult familial Menetrier disease in siblings.

Menetrier disease is a rare disease characterised by hyperplasia of the gastric epithelium and large gastric folds. We present a case of a 58-year-old woman who was referred with iron deficiency anaemia, with a family history of a sibling who had undergone gastrectomy for presumed gastric malignancy. Endoscopy showed prominent gastric mucosal folds and biopsies showed hyperplastic gastric mucosa, with prominent foveolar hyperplasia suggestive of Menetrier disease. Further information about her brother's diagnosis was sought, and it was found that his pathology after gastrectomy showed diffuse glandular hyperplasia also in keeping with Menetrier disease. Adult familial Menetrier disease has so far been a rarity in the literature-review elicits five previous cases of this presentation in siblings.