Capsular nevus versus metastatic malignant melanoma – a diagnostic dilemma

Davis, J. Wade; Patil, Jeevitha; Aydin, Nail; Mishra, Archana; Misra, Subhasis

doi:10.1016/j.ijscr.2016.10.040

Cited by 17 publications

(23 citation statements)

References 17 publications

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“…Previous studies have shown that these cells, when present in small clusters in the capsule, can be diagnostically challenging on H&E alone when using glass slides. 22,23 They can be even more difficult when reviewed in a digital format where color contrast and focus can be slightly different than when reviewing the corresponding glass slides using a microscope.…”

Section: False-positive Regions Of Interestmentioning

confidence: 99%

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu

Kohlberger

Norouzi

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

317

220

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Context.-Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci.Objective.-To evaluate the application and clinical implementation of a state-of-the-art deep learning-based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies.Design.-Whole slide images were obtained from hematoxylin-eosin-stained lymph nodes from 399 patients (publicly available Camelyon16 challenge dataset). LYNA was developed by using 270 slides and evaluated on the remaining 129 slides. We compared the findings to those obtained from an independent laboratory (108 slides from 20 patients/86 blocks) using a different scanner to measure reproducibility.Results.-LYNA achieved a slide-level area under the receiver operating characteristic (AUC) of 99% and a tumor-level sensitivity of 91% at 1 false positive per patient on the Camelyon16 evaluation dataset. We also identified 2 ''normal'' slides that contained micrometastases. When applied to our second dataset, LYNA achieved an AUC of 99.6%. LYNA was not affected by common histology artifacts such as overfixation, poor staining, and air bubbles.Conclusions.-Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide, achieving higher tumor-level sensitivity than, and comparable slidelevel performance to, pathologists. These techniques may improve the pathologist's productivity and reduce the number of false negatives associated with morphologic detection of tumor cells. We provide a framework to aid practicing pathologists in assessing such algorithms for adoption into their workflow (akin to how a pathologist assesses immunohistochemistry results).

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Section: False-positive Regions Of Interestmentioning

confidence: 99%

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu

Kohlberger

Norouzi

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

317

220

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“…2,4,7 The incidence of BCN has been described in as high as 25% of excised lymph nodes (including SLN biopsies as well as lymphadenectomies) but has more often been reported between 0% and 12%. 2,4,[6][7][8][9][10][11][12][13][14][15][16] BCN have been identified in all lymph node basins but are most frequently described in the axilla. 2,4,5 There are two major hypotheses regarding the presence of BCN: the first proposes that they occur as neural crest progenitors, which migrate and arrest in LNs and the second that benign cutaneous melanocytic lesions are transported through the lymph system (termed benign mechanical transport or benign metastasis).…”

Section: Introductionmentioning

confidence: 99%

“…2,4,5 There are two major hypotheses regarding the presence of BCN: the first proposes that they occur as neural crest progenitors, which migrate and arrest in LNs and the second that benign cutaneous melanocytic lesions are transported through the lymph system (termed benign mechanical transport or benign metastasis). 2,4,5,[8][9][10]12,15,17 Differentiating BCN from micrometastasis of cutaneous melanoma can be challenging, and relies on morphology as well as immunohistochemical (IHC) assays and stains. 4,10,[18][19][20] The clinical significance and prognostic implication of incidentally detected BCN in SLNs of patients with melanoma remains unknown.…”

Section: Introductionmentioning

confidence: 99%

Clinical significance of SLN benign capsular nevi in patients with melanoma

Yamashita

Pockaj

Bagaria

et al. 2020

Journal of Surgical Oncology

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Background: Benign capsular nevi (BCN) are not infrequent in sentinel lymph nodes (SLN) of patients with melanoma. Their prognostic significance is unknown and the literature is limited. This study evaluated the clinical significance of incidentally found BCN in these patients. Methods: A multi-institutional retrospective review of patients undergoing SLN biopsy for cutaneous melanoma between 2000 and 2016. Patients were divided into the following groups: (a) negative SLN and no BCN, (b) negative SLN and presence of BCN, (c) positive SLN seen only on immunohistochemistry (IHC), and (d) positive SLN via hematoxylin and eosin (H&E). Outcomes measured were overall survival and any recurrence. Results: A total of 1253 patients were identified (group 1 = 978, group 2 = 56, group 3 = 32, and group 4 = 187). Fifty-seven percent were male and the mean age was 59.3 years. BCN was identified in 77 patients (6.2%), of which the majority was in the node-negative group (72%). Multivariable analysis showed that BCN was associated with lower recurrence rates, though not statistically significant (hazard ratio [HR] = 0.5; P = .06). IHC-and H&E-positive SLNs were associated with a higher risk of recurrence (HR = 2.4; P = .02 and 2.0, P < .0001, respectively). Conclusion: Patients with BCN and negative SLN had lower recurrence rates than patients with negative SLN and no BCN. Our data suggest a possible protective effect against recurrence.

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“…Most previous studies provided single‐centred data, where the same pathologists observed the histology of the SLNB, perhaps resulting in more awareness for INN . INN mostly occur in the capsule or trabeculae of the lymph node, but in rare cases INN occur in the parenchyma of the lymph node which is similar to the location of metastatic melanoma . Consequently, this could provide difficulties in distinguishing INN from metastatic melanoma, certainly for less experienced pathologists, and may lead to an underestimation of the true frequency of INN in our cohort.…”

Section: Discussionmentioning

confidence: 97%

Intra‐nodal nevi in sentinel node‐negative patients with cutaneous melanoma does not influence survival

Beer

Diest

Sigurdsson

et al. 2019

Acad Dermatol Venereol

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Background Melanoma patients with intra‐nodal nevi (INN) and without melanoma metastasis in the sentinel lymph node biopsy (SLNB) are generally treated as patients with negative SLNB. However, diagnosis of INN may be difficult and nodal melanoma metastases may falsely be regarded as INN. Objectives Our aim was to evaluate the clinical significance of INN in the SLNB in patients with primary cutaneous melanoma on a nationwide level in The Netherlands by comparing survival between three groups: patients with INN and without nodal melanoma metastasis (INN group), patients without INN and without nodal melanoma metastasis (negative SLNB group) and patients with nodal melanoma metastasis irrespective of INN (positive SLNB group). Methods Data were obtained from ‘PALGA’, the Dutch Nationwide Network and Registry of Histopathology and Cytopathology, yielding a cohort of adults with histologically proven, primary, invasive cutaneous melanoma patients in The Netherlands diagnosed between 2000 and 2014 who underwent SLNB. Clinical and pathological variables were extracted from the pathology text files. Differences between patients with INN, negative SLNB and positive SLNB were analysed using Kaplan–Meier analysis. Results A total of 11 274 patients were eligible for inclusion. The prevalence of INN in the SLNB was 5.0%. Melanomas with INN had similar median Breslow thickness compared to melanomas with negative SLNB and were more frequently located on trunk and upper limbs and observed in younger patients compared to melanomas with negative and positive SLNB. Overall survival of patients with INN showed no significant difference compared with negative SLNB (median follow‐up of 5.7 years of all patients). Conclusions As there seems to be no difference in overall survival between patients with INN and negative SLNB, the diagnosis of INN seems to be reliable. Current practice to treat patients with INN as patients with negative SLNB appears to be appropriate.

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Capsular nevus versus metastatic malignant melanoma – a diagnostic dilemma

Cited by 17 publications

References 17 publications

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Clinical significance of SLN benign capsular nevi in patients with melanoma

Intra‐nodal nevi in sentinel node‐negative patients with cutaneous melanoma does not influence survival

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