Variability in the Histopathological Diagnosis of Nonmelanocytic Lesions Excised to Exclude Melanoma

Ian Katz; Tony Azzi; Alister Lilleyman; Blake O'Brien; Brian Schapiro; Curtis Thompson; Tarl Prow

doi:10.5826/dpc.1104a94

Variability in the Histopathological Diagnosis of Nonmelanocytic Lesions Excised to Exclude Melanoma

Authors

Ian Katz Southern Sun Pathology, Thornleigh, NSW, & School of Medicine, University of Queensland, QLD, Australia
Tony Azzi Newcastle Skin Check, Charlestown, NSW, & School of Medicine, University of Queensland, QLD, Australia
Alister Lilleyman Newcastle Skin Check, Charlestown, NSW, & School of Medicine, University of Queensland, QLD, Australia
Blake O'Brien Sullivan Nicolaides Pathology, Bowen Hills, & School of Medicine, University of Queensland, QLD, Australia
Brian Schapiro CTA Pathology, Oregon, USA
Curtis Thompson CTA Pathology, Oregon, USA; Oregon Health and Sciences University, Oregon, USA
Tarl Prow Future Industries Institute, University of South Australia, SA, Australia; Skin Research Centre, York Biomedical Research Institute, Hull York Medical School, University of York, York, United Kingdom

Keywords:

melanoma, seborrheic keratosis, artificial intelligence, AI, large cell acanthoma, diagnosis

Abstract

Introduction. The differential diagnosis of lesions excised to exclude melanoma include a variety of benign and malignant melanocytic and non-melanocytic lesions.

Objectives. We examined the variability between pathologists in diagnosing non-melanocytic lesions.

Methods. As part of a larger study prospectively examining the diagnosis of lesions excised to exclude melanoma in 198 patients at a primary care skin cancer clinic in Newcastle, Australia, we compared diagnosis made by 5 experienced dermatopathologists, of 44 non-melanocytic lesions in 44 patients aged 22-90.

Results. Forty-four lesions (out of 217 in total) were non-melanocytic. Among the 5 pathologists who examined each case there was marked variability in the terminology used to diagnose each case. The most common variability was found between seborrheic keratosis, large cell acanthoma, solar lentigo, and lichenoid keratosis. The diagnosis made by the majority of the pathologists was deemed to be the reference diagnosis. Versus majority diagnosis, 4% of benign lesions were considered malignant, and 7% of malignant diagnoses were considered as benign.

Conclusions. The different terminology adopted and lack of consensus in the diagnosis of these non-melanocytic lesions in this setting suggests that training AI systems using gold standards may be problematic. We propose a new management classification scheme called MOLEM (Management of Lesions Excised to exclude Melanoma) which expands the previously described MPATH-dx to include non-melanocytic lesions.

References

Marchetti, MA, A Yu, J Nanda, et al. Number needed to biopsy ratio and diagnostic accuracy for melanoma detection. J Am Acad Dermatol. 2020; 83(3):780-787. DOI: 10.1016/j.jaad.2020.04.109. PMID: 32360723. PMCID: PMC7484328.

Scolyer, RA, HP Soyer, JW Kelly, et al. Improving diagnostic accuracy for suspicious melanocytic skin lesions: New Australian melanoma clinical practice guidelines stress the importance of clinician/pathologist communication. Aust J Gen Pract. 2019; 48(6):357-362. DOI: 10.31128/AJGP-11-18-4759. PMID:31220881.

Calonje, E. Non-melanocytic lesions mimicking melanocytic lesions. Pathology. 2004; 36(5):387-95. DOI: 10.1080/00313020412331283833. PMID:15370107

Kamil, ZS, LC Tong, AA Habeeb, and D Ghazarian. Non-melanocytic mimics of melanoma: part I: intraepidermal mimics. J Clin Pathol. 2009; 62(2):120-7. DOI: 10.1136/jcp.2008.060863. PMID: 18930985.

Piepkorn, MW, RL Barnhill, DE Elder, et al. The MPATH-Dx reporting schema for melanocytic proliferations and melanoma. J Am Acad Dermatol. 2014; 70(1):131-41. DOI: 10.1016/j.jaad.2013.07.027. PMID: 24176521. PMCID:PMC3992990.

Fraga, GR and SM Amin. Large cell acanthoma: a variant of solar lentigo with cellular hypertrophy. J Cutan Pathol. 2014; 41(9):733-9. DOI: 10.1111/cup.12369.PMID: 24917472.

Patsatsi, A, E Lazaridou, C Fotiadou, A Kyriakou, and D Sotiriadis. Large cell acanthoma: a debate throughout the decades. Dermatol Pract Concept. 2014; 4(1):43-5. DOI: 10.5826/dpc.0401a05. PMID: 24520512. PMCID: PMC3919838.

Rahbari, H and H Pinkus. Large cell acanthoma. One of the actinic keratoses. Arch Dermatol. 1978; 114(1):49-52. DOI: 10.1001/archderm.114.1.49. PMID: 619783.

Roewert, HJ and AB Ackerman. Large-cell acanthoma is a solar lentigo. Am J Dermatopathol. 1992; 14(2):122-32. DOI: 10.1097/00000372-199204000-00006. PMID: 1533104.

Sanchez Yus, E, E del Rio, and L Requena. Large-cell acanthoma is a distinctive condition. Am J Dermatopathol. 1992; 14(2):140-7; discussion 148. DOI: 10.1097/00000372-199204000-00011. PMID: 1566974.

Ramos-Ceballos, FI, ST Ounpraseuth, and TD Horn. Diagnostic concordance among dermatopathologists using a three-tiered keratinocytic intraepithelial neoplasia grading scheme. J Cutan Pathol. 2008; 35(4):386-91. DOI: 10.1111/j.1600-0560.2007.00827.x. PMID: 18333899.

Adamson, AS and HG Welch. Machine Learning and the Cancer-Diagnosis Problem - No Gold Standard. N Engl J Med. 2019; 381(24):2285-2287. DOI: 10.1056/NEJMp1907407. PMID: 31826337.

Cimpean, I, I Theate, and O Vanhooteghem. Seborrheic keratosis evolution into squamous cell carcinoma: A truly modified sun-related tumor? A case report and review of the literature. Dermatol Reports. 2019; 11(1): 7999. DOI: 10.4081/dr.2019.7999.PMC6477930. PMID:31044056 PMCID:PMC6477930

Bahrani, E, P Sitthinamsuwan, TH McCalmont, and LB Pincus. Ki-67 and p16 Immunostaining Differentiates Pagetoid Bowen Disease From "Microclonal" Seborrheic Keratosis. Am J Clin Pathol. 2019; 151(6):551-560. DOI: 10.1093/ajcp/aqz001.PMID: 30852607.

Richey, JD, AC Deng, K Dresser, P O'Donnell, and KM Cornejo. Distinguishing between irritated seborrheic keratosis and squamous cell carcinoma in situ using BCL-2 and IMP3 immunohistochemistry. J Cutan Pathol. 2018; 45(8):603-609. DOI: 10.1111/cup.13269. PMID: 29726030.

Brinker, TJ, A Hekler, AH Enk, et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019; 113: 47-54. DOI: 10.1016/j.ejca.2019.04.001. PMID: 30981091.

Richens, JG, CM Lee, and S Johri. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun. 2020; 11(1): 3923. DOI: 10.1038/s41467-020-17419-7. PMID:32782264 PMCID:PMC7419549