Literature DB >> 29717800

Influence of variability in assessment of Breslow thickness, mitotic rate and ulceration among US pathologists interpreting invasive melanoma, for the purpose of AJCC staging.

Laura Taylor1, Kyle Hood2, Lisa Reisch3, Joann Elmore4, Michael Piepkorn5,6, Raymond Barnhill7, Stevan Knezevich8, Andrea Radick3, David Elder9.   

Abstract

BACKGROUND: Melanoma staging has depended on depth of invasion (Breslow thickness, BT), mitotic rate (MR) and ulceration. In anticipation of the AJCC's eighth edition, variability in pathologists' assessment of these factors and consequently in tumor staging was assessed.
METHODS: One-hundred and fifteen cases of invasive melanoma, established by a consensus panel, were assessed by 187 pathologists. Variation was studied in BT, the detection of mitotic figures, and ulceration. The sources of this variation and its effect on tumor staging are considered.
RESULTS: On average, participant assessments closely approached consensus BT. Greater variation was identified in the classification of mitogenicity, which (like ulceration) upstages a T1 melanoma from T1a to T1b in the seventh but not eighth edition. In cases with a T1a diagnosis by the consensus panel, 15.6% of participants identified one or more mitotic figures (indicative of a false positive); and in cases diagnosed asT1b by the consensus panel, 32.0% of participants failed to find mitotic figures (false negative).
CONCLUSION: Variability in the staging of T1 melanoma among pathologists when using the AJCC seventh edition criteria is closely related to the detection of mitotic figures, with BT playing a less prominent role. Decreased variability is expected after implementation of the eighth edition.
© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  dermatopathology; melanocytic lesions; melanoma

Mesh:

Year:  2018        PMID: 29717800      PMCID: PMC6450684          DOI: 10.1111/cup.13265

Source DB:  PubMed          Journal:  J Cutan Pathol        ISSN: 0303-6987            Impact factor:   1.587


  4 in total

1.  Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

Authors:  Prathamesh M Kulkarni; Eric J Robinson; Jing Wang; Yvonne M Saenger; Jaya Sarin Pradhan; Robyn D Gartrell-Corrado; Bethany R Rohr; Megan H Trager; Larisa J Geskin; Harriet M Kluger; Pok Fai Wong; Balazs Acs; Emanuelle M Rizk; Chen Yang; Manas Mondal; Michael R Moore; Iman Osman; Robert Phelps; Basil A Horst; Zhe S Chen; Tammie Ferringer; David L Rimm
Journal:  Clin Cancer Res       Date:  2019-10-21       Impact factor: 12.531

2.  Melanin concentration maps by label-free super-resolution photo-thermal imaging on melanoma biopsies.

Authors:  Margaux Bouzin; Mario Marini; Giuseppe Chirico; Francesca Granucci; Francesca Mingozzi; Roberto Colombo; Laura D'Alfonso; Laura Sironi; Maddalena Collini
Journal:  Biomed Opt Express       Date:  2022-02-03       Impact factor: 3.732

3.  Melanoma Prognosis: Accuracy of the American Joint Committee on Cancer Staging Manual Eighth Edition.

Authors:  Shirin Bajaj; Douglas Donnelly; Melissa Call; Paul Johannet; Una Moran; David Polsky; Richard Shapiro; Russell Berman; Anna Pavlick; Jeffrey Weber; Judy Zhong; Iman Osman
Journal:  J Natl Cancer Inst       Date:  2020-09-01       Impact factor: 13.506

4.  Histopathologic synoptic reporting of invasive melanoma: How reliable are the data?

Authors:  Laura A Taylor; Megan M Eguchi; Lisa M Reisch; Andrea C Radick; Hannah Shucard; Kathleen F Kerr; Michael W Piepkorn; Stevan R Knezevich; David E Elder; Raymond L Barnhill; Joann G Elmore
Journal:  Cancer       Date:  2021-05-04       Impact factor: 6.860

  4 in total

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