Melanocytic nevi in sentinel lymph nodes: association with cutaneous nevi and clinical relevance in patients with cutaneous melanomas

Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images

Introduction

Nodal metastasis (NM) in sentinel node biopsies (SNB) is crucial for melanoma staging. However, an intra-nodal nevus (INN) may often be misclassified as NM, leading to potential misdiagnosis and incorrect staging. There is high discordance among pathologists in assessing SNB positivity, which may lead to false staging. Digital whole slide imaging offers the potential for implementing artificial intelligence (AI) in digital pathology. In this study, we assessed the capability of AI to detect NM and INN in SNBs.

Methods

A total of 485 hematoxylin and eosin whole slide images (WSIs), including NM and INN from 196 SNBs, were collected and divided into training (279 WSIs), validation (89 WSIs), and test sets (117 WSIs). A deep learning model was trained with 5,956 manual pixel-wise annotations. The AI and three blinded dermatopathologists assessed the test set, with immunohistochemistry serving as the reference standard.

Results

The AI model showed excellent performance with an area under the curve receiver operating characteristic (AUC) of 0.965 for detecting NM. In comparison, the AUC for NM detection among dermatopathologists ranged between 0.94 and 0.98. For the detection of INN, the AUC was lower for both AI (0.781) and dermatopathologists (range of 0.63-0.79).

Discussion

In conclusion, the deep learning AI model showed excellent accuracy in detecting NM, achieving dermatopathologist-level performance in detecting both NM and INN. Importantly, the AI model showed the potential to differentiate between these two entities. However, further validation is warranted.

Frequency of naevus cells in lymph nodes of melanoma and breast cancer patients

INTRODUCTION

We aimed to study the frequency (prevalence) and histology of benign melanocytic naevus cells in regional lymph nodes in relation to age and sex and nodal location.

MATERIAL AND METHODS

Histopathology reports of sentinel lymph node (SLN) biopsies from melanoma patients, 2002 - 2014, and from breast cancer patients, 2010- 2019, were obtained from records of a single hospital in England. All sections were similarly processed and examined. For standardisation, presence of naevus cells was assessed in a single node per patient: the first SLN biopsied (melanoma) or the node nearest the first SLN (breast cancer).

RESULTS

Associations were tested using Fisher's exact test. Naevus cells were found in 10% (60/585) of melanoma patients' index SLNs. Frequency varied significantly by anatomic region: 13% in axillary to 0% cervical SLNs (p = 0.03), but not by sex or age. Within nodes, naevus cells were present in capsular or pericapsular tissue (93%), or trabeculae (7%). In breast cancer patients' index axillary nodes, 6% (11/196) contained naevus cells, all intracapsular. In the predominant 40-69 years age-group, prevalence was similar in breast cancer (7%) and female melanoma (9%) patients, but in those aged 70-100, prevalence was lower in breast cancer (2%) than in female melanoma (15%) patients (p = 0.05).

CONCLUSIONS

Standard methods of assessment yielded no clear pattern of naevus cell frequency in lymph nodes by age or sex, but confirmed naevus cell location as mostly intracapsular.