Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.

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KMT2G has not been previously implicated in mesenchymal neoplasms, gene fusions involving another member of the KMT2 family (KMT2A/MLL) have been recently described in aggressive sarcomas in young adults, suggesting a broader role for KMT2 family of histone methyltransferases in the pathogenesis of mesenchymal tumour. 12 In summary, we have identified a novel SETD1B-FOSB gene fusion in a case of intravascular EH of deep soft tissue with uncommon histological features. This supports the association of certain unusual morphological features of EH with recurrent genetic rearrangements in the FOS gene family, hinting at a distinct subgroup defined by FOS gene family alterations within this heterogenous entity.

Synovial metastases are rare for any malignancy. This case report discusses a case of synovial metastasis from urothelial carcinoma of the renal pelvis presenting with recurrent hemarthrosis. The diagnosis of malignant synovitis can be obtained by synovial fluid aspiration, which is a quick and minimally invasive method, especially when imaging is unyielding or unspecific. Unfortunately, the diagnosis is associated with a poor prognosis of about five months, and treatment is often palliative. While no clinical guidelines exist, a multimodal and multidisciplinary management approach can help address the physical and psychosocial losses suffered.