Victor Babeş Str

Abstract Accurate and efficient grading of cutaneous squamous cell carcinoma (cSCC) is critical for effective treatment and prognosis, but traditional manual grading methods are subjective and time-consuming. This study aimed to develop and validate a deep learning (DL) model for automated cSCC grading, potentially improving diagnostic accuracy and efficiency.

Abstract Cutaneous squamous cell carcinoma (cSCC) is a prevalent and aggressive skin cancer with increasing global incidence, representing a significant public health challenge. Understanding the tumor microenvironment (TME) is crucial for improving treatment strategies. This study investigated the TME of cSCC by analyzing the spatial distribution of immune and vascular markers, addressing the need for quantitative methods to characterize tumor progression.