Abstract
An increasing number of pathology laboratories are now fully digitised, using whole slide imaging (WSI) for routine diagnostics. WSI paves the road to use artificial intelligence (AI) that will play an increasing role in computer-aided diagnosis (CAD). In melanocytic skin lesions, the presence of a dermal mitosis may be an important clue for an intermediate or a malignant lesion and may indicate worse prognosis. In this study a mitosis algorithm primarily developed for breast carcinoma is applied to melanocytic skin lesions. This study aimed to assess whether the algorithm could be used in diagnosing melanocytic lesions, and to study the added value in diagnosing melanocytic lesions in a practical setting. WSI’s of a set of hematoxylin and eosin (H&E) stained slides of 99 melanocytic lesions (35 nevi, 4 intermediate melanocytic lesions, and 60 malignant melanomas, including 10 nevoid melanomas), for which a consensus diagnosis was reached by three academic pathologists, were subjected to a mitosis algorithm based on AI. Two academic and six general pathologists specialized in dermatopathology examined the WSI cases two times, first without mitosis annotations and after a washout period of at least 2 months with mitosis annotations based on the algorithm. The algorithm indicated true mitosis in lesional cells, i.e., melanocytes, and non-lesional cells, i.e., mainly keratinocytes and inflammatory cells. A high number of false positive mitosis was indicated as well, comprising melanin pigment, sebaceous glands nuclei, and spindle cell nuclei such as stromal cells and neuroid differentiated melanocytes. All but one pathologist reported more often a dermal mitosis with the mitosis algorithm, which on a regular basis, was incorrectly attributed to mitoses from mainly inflammatory cells. The overall concordance of the pathologists with the consensus diagnosis for all cases excluding nevoid melanoma (n = 89) appeared to be comparable with and without the use of AI (89% vs. 90%). However, the concordance increased by using AI in nevoid melanoma cases (n = 10) (75% vs. 68%). This study showed that in general cases, pathologists perform similarly with the aid of a mitosis algorithm developed primarily for breast cancer. In nevoid melanoma cases, pathologists perform better with the algorithm. From this study, it can be learned that pathologists need to be aware of potential pitfalls using CAD on H&E slides, e.g., misinterpreting dermal mitoses in non-melanotic cells.
Highlights
Digital pathology is a dynamic, image-based environment that enables the acquisition, management, and interpretation of pathology information generated from a digitised glass slide, i.e., whole slide images (WSI), that can be assessed on a computer screen
The cases were obtained from the archive of the Pathology Department of the Radboud UMC in Nijmegen, The Netherlands, and concerned 35 benign nevi, 5 intermediate lesions, and 62 malignant melanomas, including 10 nevoid melanomas
The set of whole slide imaging (WSI) assessed by the study pathologists contained 99 cases for which consensus could be achieved by 3 academic pathologists based on the glass slides (35 benign melanocytic lesions, 4 intermediate cases, and 60 melanomas, including 10 nevoid melanomas) [36]
Summary
Digital pathology is a dynamic, image-based environment that enables the acquisition, management, and interpretation of pathology information generated from a digitised glass slide, i.e., whole slide images (WSI), that can be assessed on a computer screen. Digital pathology offers all kinds of benefits, including digital archiving, consultation, and showcasing at tumour boards [1,2]. It is an innovation committed to the improvement of operational efficiency, including decreasing turn-a-round times with the reduction of laboratory expenses [2,3]. Since several WSI scanners are approved in Europe, given the European Conformity mark, in the United States of America by the Food and Drug Administration, and in Japan, by the Pharmaceuticals and Medical Devices Agency [4], enormous opportunities have arisen to analyse the sheer amount of slides with visual quantitative computer techniques, i.e., computational pathology (CP), based on machine learning (ML) [2]. A range of different ML techniques are available of which in recent years, algorithms based on convolutional neural networks appear to dominate [5]
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