Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.

Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence-based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.

Nuclear morphology in breast lesions: refining its assessment to improve diagnostic concordance.

AIMS: Although evaluation of nuclear morphology is important for the diagnosis and categorisation of breast lesions, the criteria used to assess nuclear atypia rely upon the subjective evaluation of several features that may result in inter- and intraobserver variation. This study aims to refine the definitions of cytonuclear features in various breast lesions. METHODS AND RESULTS: ImageJ was used to assess the nuclear morphological features including nuclear diameter, axis length, perimeter, area, circularity and roundness in 160 breast lesions comprising ductal carcinoma in situ (DCIS), invasive breast carcinoma of no special type (IBC-NST), tubular carcinoma, usual ductal hyperplasia (UDH), columnar cell change (CCC) and flat epithelial atypia (FEA). Reference cells included normal epithelial cells, red blood cells (RBCs) and lymphocytes. Reference cells showed size differences not only between normal epithelial cells and RBCs but also between RBCs in varied-sized blood vessels. Nottingham grade nuclear pleomorphism scores 1 and 3 cut-offs in IBC-NST, compared to normal epithelial cells, were < ×1.2 and > ×1.4 that of mean maximum Feret's diameter and < ×1.6 and > ×2.4 that of mean nuclear area, respectively. Nuclear morphometrics were significantly different in low-grade IBC-NST versus tubular carcinoma, low-grade DCIS versus UDH and CCC versus FEA. No differences in the nuclear features between grade-matched DCIS and IBC-NST were identified. CONCLUSION: This study provides a guide for the assessment of nuclear atypia in breast lesions, refines the comparison with reference cells and highlights the potential diagnostic value of image analysis tools in the era of digital pathology.

Atypia in breast pathology: what pathologists need to know.

Despite the importance of atypia in diagnosing and classifying breast lesions, the definition of atypia varies depending on the context, with a lack of consistent and objective criteria for assessment. Atypia in breast pathology may be cytonuclear and/or architectural with different applications and implications. Cytonuclear atypia is used to assist the distinction of various intraductal epithelial proliferative lesions including usual ductal hyperplasia (UDH) versus atypical ductal hyperplasia (ADH) or ductal carcinoma in situ (DCIS), and to grade DCIS. In invasive carcinoma, nuclear atypia (i.e., nuclear pleomorphism) is a component of the histological grading system. Stromal cell cytonuclear atypia is one of the key features used to distinguish fibroadenoma from phyllodes tumour (PT) and to classify PT as benign, borderline or malignant. Similarly, cytonuclear atypia is used in the evaluation of myoepithelial cell alterations in the breast. Architectural atypia is used to differentiate flat epithelial atypia (FEA) from ADH or DCIS. In addition to the inherent subjectivity in the interpretation of atypia, which presents as a morphological continuum reflecting a biological spectrum, the lack of standardisation in defining atypia augments diagnostic discordance in breast pathology, with potential implications for patient management. Evidence to date suggests that the traditional criteria used to assess atypia may require modification in the era of digital pathology primary diagnosis. This review aims to provide a comprehensive review of atypia in breast pathology with reference to inconsistencies, challenges and limitations.

UK policy initiatives and the effect on increasing organ donation.

Organ donation has developed since the Human Tissue Act 1961, and even since the Human Tissue Act 2004, which replaced it. Given the demand for organ transplants, there have been various attempts to increase the number of people on the Organ Donation Register, including awareness campaigns and celebrity endorsement. However, as the UK-wide strategy Taking Organ Transplantation to 2020 indicates, increasing the number of donations will require more than simply increasing the number of registered donors. This article reviews the changes in policies relating to organ donation and the associated issues.