Filosa Alessandra, Cazzato Gerardo, Bartoli Elisa, Antaldi Elena, Giantomassi Federica, Santoni Matteo, Goteri Gaia
Biomedical Science a Public Health Department, Section of Pathological Anatomy, Polytechnic University of Marche Region, 60121 Ancona, Italy.
Department of Precision and Regenerative Medicine and Ionian Area, Bari University, 70121 Bari, Italy.
Diagnostics (Basel). 2025 Apr 25;15(9):1089. doi: 10.3390/diagnostics15091089.
: We herein review the most important clinico-pathological features of mycosis fungoides (MF). These evolving clinico-pathological aspects are paired with innovative therapeutic schemes. Moreover, we indicate cutaneous lymphomas as a new frontier of artificial intelligence application. : We encompass new diagnostic and prognostic data derived from the recent medical literature describing the possible histological features which could be the targets of deep learning in conjunction with available clinical data. : In spite of decades of research, MF diagnosis still represents the most challenging debate from a dermatopathologist's point of view. Genetic alterations have been identified mainly in late stages of the disease, and their importance for disease initiation is still unclear. The exploration of the genome-wide expression of individual genes in skin samples may be useful in elucidating MF pathogenesis and improving early diagnosis, while artificial intelligence could offer the possibility of searching for biomarkers of disease progression. : MF still deserves the name of the 'great imitator', both clinically and histopathologically. The goal of summing up all the clinico-pathological information before reaching a final diagnosis is the approach needed to reach diagnostic accuracy, especially in early MF cases. It is advisable to think of the most common clinical presentations, to be aware of the most common histopathological features, and to interpret the results of ancillary studies only in the right clinico-pathological context.
我们在此回顾蕈样肉芽肿(MF)最重要的临床病理特征。这些不断演变的临床病理方面与创新的治疗方案相结合。此外,我们指出皮肤淋巴瘤是人工智能应用的一个新前沿领域。我们纳入了近期医学文献中得出的新诊断和预后数据,这些数据描述了可能的组织学特征,这些特征可能是结合现有临床数据进行深度学习的目标。尽管经过了数十年的研究,但从皮肤病理学家的角度来看,MF诊断仍然是最具挑战性的难题。基因改变主要在疾病晚期被发现,其对疾病起始的重要性仍不清楚。探索皮肤样本中单个基因的全基因组表达可能有助于阐明MF的发病机制并改善早期诊断,而人工智能可以提供寻找疾病进展生物标志物的可能性。MF在临床和组织病理学上仍然堪称“伟大的模仿者”。在做出最终诊断前总结所有临床病理信息的目标,是实现诊断准确性所需的方法,尤其是在早期MF病例中。建议考虑最常见的临床表现,了解最常见的组织病理学特征,并仅在正确的临床病理背景下解读辅助检查结果。