Cargando…
Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm
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...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871065/ https://www.ncbi.nlm.nih.gov/pubmed/35204526 http://dx.doi.org/10.3390/diagnostics12020436 |
_version_ | 1784656907198267392 |
---|---|
author | Sturm, Bart Creytens, David Smits, Jan Ooms, Ariadne H. A. G. Eijken, Erik Kurpershoek, Eline Küsters-Vandevelde, Heidi V. N. Wauters, Carla Blokx, Willeke A. M. van der Laak, Jeroen A. W. M. |
author_facet | Sturm, Bart Creytens, David Smits, Jan Ooms, Ariadne H. A. G. Eijken, Erik Kurpershoek, Eline Küsters-Vandevelde, Heidi V. N. Wauters, Carla Blokx, Willeke A. M. van der Laak, Jeroen A. W. M. |
author_sort | Sturm, Bart |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8871065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88710652022-02-25 Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm Sturm, Bart Creytens, David Smits, Jan Ooms, Ariadne H. A. G. Eijken, Erik Kurpershoek, Eline Küsters-Vandevelde, Heidi V. N. Wauters, Carla Blokx, Willeke A. M. van der Laak, Jeroen A. W. M. Diagnostics (Basel) Article 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. MDPI 2022-02-08 /pmc/articles/PMC8871065/ /pubmed/35204526 http://dx.doi.org/10.3390/diagnostics12020436 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sturm, Bart Creytens, David Smits, Jan Ooms, Ariadne H. A. G. Eijken, Erik Kurpershoek, Eline Küsters-Vandevelde, Heidi V. N. Wauters, Carla Blokx, Willeke A. M. van der Laak, Jeroen A. W. M. Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title_full | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title_fullStr | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title_full_unstemmed | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title_short | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm |
title_sort | computer-aided assessment of melanocytic lesions by means of a mitosis algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871065/ https://www.ncbi.nlm.nih.gov/pubmed/35204526 http://dx.doi.org/10.3390/diagnostics12020436 |
work_keys_str_mv | AT sturmbart computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT creytensdavid computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT smitsjan computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT oomsariadnehag computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT eijkenerik computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT kurpershoekeline computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT kustersvandeveldeheidivn computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT wauterscarla computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT blokxwillekeam computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm AT vanderlaakjeroenawm computeraidedassessmentofmelanocyticlesionsbymeansofamitosisalgorithm |