Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

• Published in: Pathology, research and practice Vol. 237; p. 154014
• Main Authors: Dika, Emi, Curti, Nico, Giampieri, Enrico, Veronesi, Giulia, Misciali, Cosimo, Ricci, Costantino, Castellani, Gastone, Patrizi, Annalisa, Marcelli, Emanuela
• Format: Journal Article
• Language: English
• Published: Germany Elsevier GmbH 01.09.2022
• Subjects: Computer-aided diagnosis; Computers; Diagnostic variability, histopathology; Eosine Yellowish-(YS); Hematoxylin; Humans; Image analysis, melanocytes, melanoma; Melanoma - diagnosis; Melanoma - pathology; Melanoma, Cutaneous Malignant; Pilot Projects; Retrospective Studies; Skin cancer; Skin Neoplasms - diagnosis; Skin Neoplasms - pathology; Diagnostic variability, histopathology; Image analysis, melanocytes, melanoma; Computer-aided diagnosis; Skin cancer
• ISSN: 0344-0338; 1618-0631; 1618-0631
• DOI: 10.1016/j.prp.2022.154014

Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists’ subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion’s silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level.