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 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.

Validation of Whole-slide Digitally Imaged Melanocytic Lesions: Does Z-Stack Scanning Improve Diagnostic Accuracy?

BACKGROUND: Accurate diagnosis of melanocytic lesions is challenging, even for expert pathologists. Nowadays, whole-slide imaging (WSI) is used for routine clinical pathology diagnosis in several laboratories. One of the limitations of WSI, as it is most often used, is the lack of a multiplanar focusing option. In this study, we aim to establish the diagnostic accuracy of WSI for melanocytic lesions and investigate the potential accuracy increase of z-stack scanning. Z-stack enables pathologists to use a software focus adjustment, comparable to the fine-focus knob of a conventional light microscope. MATERIALS AND METHODS: Melanocytic lesions (n = 102) were selected from our pathology archives: 35 nevi, 5 spitzoid tumors of unknown malignant potential, and 62 malignant melanomas, including 10 nevoid melanomas. All slides were scanned at a magnification comparable to use of a ×40 objective, in z-stack mode. A ground truth diagnosis was established on the glass slides by four academic dermatopathologists with a special interest in the diagnosis of melanoma. Six nonacademic surgical pathologists subspecialized in dermatopathology examined the cases by WSI. RESULTS: An expert consensus diagnosis was achieved in 99 (97%) of cases. Concordance rates between surgical pathologists and the ground truth varied between 75% and 90%, excluding nevoid melanoma cases. Concordance rates of nevoid melanoma varied between 10% and 80%. Pathologists used the software focusing option in 7%-28% of cases, which in 1 case of nevoid melanoma resulted in correcting a misdiagnosis after finding a dermal mitosis. CONCLUSION: Diagnostic accuracy of melanocytic lesions based on glass slides and WSI is comparable with previous publications. A large variability in diagnostic accuracy of nevoid melanoma does exist. Our results show that z-stack scanning, in general, does not increase the diagnostic accuracy of melanocytic.

The evaluation of colon biopsies using virtual microscopy is reliable.

AIMS: Virtual microscopy offers major advantages for pathology practice, separating slide evaluation from slide production. The aim of this study was to investigate the reliability of using whole slide images as compared with routine glass slides for diagnostic purposes. METHODS AND RESULTS: Colon biopsies (n = 295) were assessed using both glass slides and whole slide images by four pathologists and two residents. Two pathologists scored the digital images of biopsies in a primary diagnostic setting. For each case, the consensus diagnosis was defined as the majority diagnosis on the study's glass slides. All diagnoses were grouped into seven main diagnostic categories, and further divided into subgroups. The overall concordance rates were 89.6% for whole slide images and 91.6% for light microscopy. The concordance rates of the subgroups 'adenoma' and 'adenocarcinoma' between whole slide images and conventional microscopy showed only small variability. The intraobserver (whole slide images versus glass slide) agreement, including subgroups, was substantial, with a mean κ-value of 0.78, and was higher than the interobserver agreement for glass slides (interobserver κ-value of 0.69). CONCLUSIONS: This study shows good diagnostic accuracy and reproducibility for virtual microscopy, indicating that this technology can reliably be used for pathological evaluation of colon biopsies in a primary clinical setting.

Interobserver variability and the effect of education in the histopathological diagnosis of differentiated vulvar intraepithelial neoplasia.

No published data concerning intraobserver and interobserver variability in the histopathological diagnosis of differentiated vulvar intraepithelial neoplasia (DVIN) are available, although it is widely accepted to be a subtle and difficult histopathological diagnosis. In this study, the reproducibility of the histopathological diagnosis of DVIN is evaluated. Furthermore, we investigated the possible improvement of the reproducibility after providing guidelines with histological characteristics and tried to identify histological characteristics that are most important in the recognition of DVIN. A total number of 34 hematoxylin and eosin-stained slides were included in this study and were analyzed by six pathologists each with a different level of education. Slides were reviewed before and after studying a guideline with histological characteristics of DVIN. Kappa statistics were used to compare the interobserver variability. Pathologists with a substantial agreement were asked to rank items by usefulness in the recognition of DVIN. The interobserver agreement during the first session varied between 0.08 and 0.54, which slightly increased during the second session toward an agreement between -0.01 and 0.75. Pathologists specialized in gynecopathology reached a substantial agreement (kappa 0.75). The top five of criteria indicated to be the most useful in the diagnosis of DVIN included: atypical mitosis in the basal layer, basal cellular atypia, dyskeratosis, prominent nucleoli and elongation and anastomosis of rete ridges. In conclusion, the histopathological diagnosis of DVIN is difficult, which is expressed by low interobserver agreement. Only in experienced pathologists with training in gynecopathology, kappa values reached a substantial agreement after providing strict guidelines. Therefore, it should be considered that specimens with an unclear diagnosis and/or clinical suspicion for DVIN should be revised by a pathologist specialized in gynecopathology. When adhering to suggested criteria the diagnosis of DVIN can be made easier.