RESUMO
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms that objectively categorise the most relevant features in order of importance. The data set comprises 122 tumours (39 benign, 44 atypical and 39 malignant) from four different countries. BRAF and NRAS mutation status was evaluated in 51. Analysis of variance score was performed to rank 22 clinicopathological variables. The Gaussian naive Bayes algorithm achieved in distinguishing Spitz naevus from malignant spitzoid tumours with an accuracy of 0.95 and kappa score of 0.87, utilising the 12 most important variables. For benign versus non-benign Spitz tumours, the test reached a kappa score of 0.88 using the 13 highest-scored features. Furthermore, for the atypical Spitz tumours (AST) versus Spitz melanoma comparison, the logistic regression algorithm achieved a kappa value of 0.66 and an accuracy rate of 0.85. When the three categories were compared most AST were classified as melanoma, because of the similarities on histological features between the two groups. Our results show promise in supporting the histological classification of these tumours in clinical practice, and provide valuable insight into the use of ML to improve the accuracy and objectivity of this process while minimising interobserver variability. These proposed algorithms represent a potential solution to the lack of a clear threshold for the Spitz/spitzoid tumour classification, and its high accuracy supports its usefulness as a helpful tool to improve diagnostic decision-making.
Assuntos
Aprendizado de Máquina , Melanoma , Nevo de Células Epitelioides e Fusiformes , Neoplasias Cutâneas , Humanos , Nevo de Células Epitelioides e Fusiformes/patologia , Nevo de Células Epitelioides e Fusiformes/diagnóstico , Nevo de Células Epitelioides e Fusiformes/genética , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/diagnóstico , Neoplasias Cutâneas/genética , Masculino , Feminino , Melanoma/patologia , Melanoma/diagnóstico , Melanoma/genética , Adulto , Adolescente , Adulto Jovem , Criança , Pessoa de Meia-Idade , Pré-Escolar , Proteínas Proto-Oncogênicas B-raf/genética , Proteínas de Membrana/genética , GTP Fosfo-Hidrolases/genética , Lactente , Mutação , IdosoRESUMO
BACKGROUND: Cysts are very common in the routine of dermatopathology but follicular germinative (trichoblastic) differentiation in cysts is seen rarely. The presence of follicular germinative differentiation in a cyst alerts to consider the possibility of a basal cell carcinoma (BCC) arising in a cyst. METHODS: Five cystic lesions with zones of follicular germinative differentiation were collected. Hematoxylin and eosin sections were reassessed for architecture, types of follicular differentiation and stromal characteristics; immunohistochemical studies with Ber-EP4 were analyzed. Articles about follicular germinative differentiation in cystic lesions were reviewed. RESULTS: Cystic lesions with follicular germinative differentiation have been described in the literature under various names including trichoblastic infundibular cyst, cystic trichoblastoma, cystic panfolliculoma (CPF), dermoid cyst with basaloid proliferations, folliculosebaceous cystic hamartoma and BCC occurring in infundibular cysts. The lesions presented by us could be classified as three cystic trichoblastomas, one CPF and one cystic hamartoma with follicular germinative differentiation. CONCLUSIONS: Histopathologically, cystic trichoblastomas can be separated from CPFs. Some lesions defy classification and may be regarded as cystic follicular hamartomas. The presence of follicular papillae and bulb-like structures, advanced follicular differentiation like that of inner and outer root sheath exclude the differential diagnosis of BCC arising in a cyst.