Variability in mitotic figures in serial sections of thin melanomas

doi:10.1016/j.jaad.2014.07.056

Journal of the American Academy of Dermatology

Volume 71, Issue 6, December 2014, Pages 1204-1211

https://doi.org/10.1016/j.jaad.2014.07.056 Get rights and content

Background

T1 melanoma staging is significantly affected by tissue sampling approaches, which have not been well characterized.

Objective

We sought to characterize presence of mitotic figures across a minimum of 5 sequential sections of T1 melanomas.

Methods

A cohort of T1 melanomas with either 5 (single section per slide) or 10 (2 sections per slide) sequential sections (5-μm thickness) per case were prepared and examined for mitotic figures.

Results

In all, 44 of 82 T1 melanomas (54%) were classified as T1b. The number of sections with a mitotic figure present ranged from only 1 of 5 sections (n = 5 of 44 cases, 11.4%) to all 5 (n = 20 of 44 cases, 45.5%). A sequential approach versus a nonsequential approach did not appear to matter.

Limitation

Cases were taken from a single pathology practice in the Pacific Northwest, which may not generalize to other populations in the United States.

Conclusion

The variation in the presence of mitotic figures within sequential sections supports reviewing 3 to 5 sections to fulfill American Joint Committee on Cancer recommendations. The prognostic significance of a T1b melanoma with a rare mitotic figure on a single section versus a T1b melanoma with mitotic figures on multiple sections deserves more attention to see if further subclassification is possible or even necessary.

Section snippets

Patient cohort

Skin lesions biopsied in 2010 through 2011 were identified from a private dermatopathology practice in Washington State using their in-house database of patient records. New slides were made for each case, with 5-μm sequential sections transferred onto new slides for each case. All procedures were Health Insurance Portability and Accountability Act compliant, and approval was obtained from the University of Washington Institutional Review Board (#41700).

Identification of thin melanomas–panel dermatopathologists' independent reviews and consensus panel review

Development of study materials, including

Patient characteristics

The characteristics of 82 patients with thin primary melanomas are shown in Table I, with 38 patients (46%) classified as T1a and 44 patients (54%) as T1b. Additional characteristics include anatomic site, type of biopsy performed, corresponding Breslow depth, and type of melanoma. None of the melanomas had epidermal ulceration.

Mitotic variability across sequential sections of thin melanomas

A mitotic figure was noted on all 5 sections for 20 of the 44 T1b cases (45.5%), whereas the remainder had mitotic figures on 4 or fewer sections (Fig 1).

Discussion

The objective of this study was to characterize variability in the presence of mitotic figures across a minimum of 5 sequential sections among T1 melanomas. Our review of T1b melanomas showed that approximately half (46%) had a mitotic figure on all 5 sequential sections. We note a general increase in likelihood of finding a mitotically active melanoma, in addition to finding mitoses on most sections, with increasing Breslow depth. This generalization is met with important outliers that

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Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, their precise detection is an important factor for clinical care. Convolutional Neural Networks (CNNs) have shown remarkable performance on several recognition tasks. Utilizing CNNs for mitosis classification may aid pathologists to improve the detection accuracy.
We studied two state-of-the-art CNN-based models, ESPNet and DenseNet, for mitosis classification on six whole slide images of skin biopsies and compared their quantitative performance in terms of sensitivity, specificity, and F-score. We used raw RGB images of mitosis and non-mitosis samples with their corresponding labels as training input. In order to compare with other work, we studied the performance of these classifiers and two other architectures, ResNet and ShuffleNet, on the publicly available MITOS breast biopsy dataset and compared the performance of all four in terms of precision, recall, and F-score (which are standard for this data set), architecture, training time and inference time.
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Citation Excerpt :
There is also a debate concerning the ability to predict SLNB positivity. These studies have all been complicated by the variability in observing and documenting mitotic figures, which supports the need to adhere to a standardized detection method when studying this characteristic (Knezevich et al., 2014). A large meta-analysis studied the factors predicting a positive SLNB in 3651 thin melanomas and showed that mitotic rate did not correlate with a positive SLNB (Warycha et al., 2009).
Melanoma incidence and mortality are on the rise and although most new cases of melanoma are thin, a significant percentage of these patients still experience disease progression. The American Joint Committee on Cancer publishes staging criteria for melanoma, which were recently updated to the 8^th edition. The most significant revision from the 7^th edition affects the T1b classification, which now includes melanomas with a Breslow depth of 0.8 mm to 1.0 mm. The second major revision eliminates mitoses as a criterion to upstage a thin melanoma to T1b. Although mitotic figures have been established as an independent prognostic factor, they do not have a significant correlation with sentinel lymph node (SLN) biopsy positivity. SLN status remains the most important independent prognostic factor in thin melanomas. Nonetheless, the identification of patients who are at the highest risk for having a positive SLN test result remains difficult. Importantly, a positive SLN test result has high positive predictive value, but a negative one has very low negative predictive value. Since there is no proven survival benefit in performing an SLN biopsy in T1 disease, dermatologists need to have a personalized discussion with patients with thin melanomas to review expected risks and benefits before undertaking this procedure.
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Detailed information about the study design and data collection is provided elsewhere.8-12
Diagnostic interpretations of melanocytic skin lesions vary widely among pathologists, yet the underlying reasons remain unclear.
Identify pathologist characteristics associated with rates of accuracy and reproducibility.
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On the other hand, certain variability between observers was noted along with a low reproducibility of the mitotic index. This made it necessary for 3 to 5 serial sections to be taken for the assessment of mitosis to be reliable,43 and it was therefore not considered a good parameter for making therapeutic decisions.44 In fact, some multivariate analyses were unable to demonstrate its true prognostic relevance.45,46
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: The National Cancer Institute (R01 CA151306 and K05 CA104699) supported this work.

: Conflicts of interest: None declared.

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DermatopathologyVariability in mitotic figures in serial sections of thin melanomas

Background

Objective

Methods

Results

Limitation

Conclusion

Section snippets

Patient cohort

Identification of thin melanomas–panel dermatopathologists' independent reviews and consensus panel review

Patient characteristics

Mitotic variability across sequential sections of thin melanomas

Discussion

J Am Acad Dermatol

J Am Acad Dermatol

J Invest Dermatol

Impact of thorough block sampling in the histologic evaluation of melanomas

Arch Dermatol

Histologic evaluation of melanomas

Arch Dermatol

Malignant melanoma microstaging: history, premises, methods, problems, and recommendations–a call for standardization

Pathol Annu

Dermatopathology
Variability in mitotic figures in serial sections of thin melanomas