Pathology Unit, IRCCS Azienda Ospedaliero Universitaria di Bologna, Italy.
Division of Dermatology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Italy; Alma Mater Studiorum, University of Bologna, Italy.
Crit Rev Oncog. 2023;28(3):37-41. doi: 10.1615/CritRevOncog.2023050220.
Cutaneous melanoma (CM) incidence has dramatically increased in the last years. Early diagnosis is of paramount importance in terms of prognosis. Artificial Intelligence (AI) tools are being proposed for clinicians and pathologists as an adjunct support in the diagnostic process. We described herein an overview of the most important parameters that a potential AI tool should take into consideration in histopathology to evaluate a skin lesion. First of all, recognition of a melanocytic or non-melanocytic nature. Furthermore, melanocytic lesions should be stratified according to at least four parameters: silhouette and asymmetry; identification and spatial distribution of the cells; mitosis count; presence of ulceration. According to the number of parameters the AI tools might stratify the risk of CM and prioritize the pathologist's work.
近年来,皮肤黑色素瘤(CM)的发病率显著上升。早期诊断对预后至关重要。人工智能(AI)工具正被提议为临床医生和病理学家提供诊断过程中的辅助支持。本文概述了潜在的 AI 工具在组织病理学评估皮肤病变时应考虑的最重要参数。首先,识别黑色素细胞或非黑色素细胞性质。此外,根据至少四个参数对黑色素细胞病变进行分层:轮廓和不对称性;细胞的识别和空间分布;有丝分裂计数;溃疡的存在。根据 AI 工具分层的参数数量,可能会对 CM 的风险进行分层,并优先考虑病理学家的工作。