A groundbreaking advancement in dermatological diagnostics has emerged from the University of Gothenburg, where researchers have developed a streamlined artificial intelligence (AI) model capable of evaluating the aggressiveness of cutaneous squamous cell carcinoma (cSCC) with a proficiency rivaling seasoned dermatologists. This achievement holds tremendous promise for enhancing preoperative assessment of this prevalent form of skin cancer, potentially revolutionizing clinical decision-making processes worldwide.
Cutaneous squamous cell carcinoma ranks as the second most common skin malignancy in Sweden, trailing only basal cell carcinoma. Its incidence is rising sharply, with over 10,000 new cases diagnosed annually across the country. cSCC primarily affects sun-exposed anatomical regions such as the face and neck, where cumulative ultraviolet (UV) radiation inflicts molecular damage over many years, initiating oncogenic mutations in keratinocytes—the predominant cell type in the epidermis.
Dr. Sam Polesie, an associate professor specializing in dermatology and venereology at the University of Gothenburg, led the research team that spearheaded the AI model’s development. “The pathogenesis of squamous cell carcinoma is intimately linked to chronic UV exposure, which induces mutagenic alterations in skin cells. Clinically, these tumors often present on sun-damaged skin characterized by irregular pigmentation, thickened, ulcerated lesions, and compromised elasticity,” explains Polesie. Despite the relative ease of recognizing the disease itself, stratifying tumors according to their biological aggressiveness remains a formidable clinical challenge.
Current medical protocols in Sweden and many other countries generally forgo preoperative punch biopsies in suspected cSCC cases. Instead, patients undergo surgery based solely on clinical suspicion, and the excised tissue undergoes subsequent histopathological examination to confirm diagnosis and guide follow-up care. However, without a biopsy guiding initial treatment, clinicians face significant uncertainty regarding the tumor’s growth potential, complicating surgical planning. Aggressive tumors necessitate prompt, extensive surgical excision with wider margins to minimize recurrence risk, whereas less aggressive lesions may be addressed using more conservative approaches.
Acknowledging these diagnostic hurdles, the research team focused on leveraging machine learning to analyze a robust dataset comprising 1,829 close-up clinical images of pathologically confirmed squamous cell carcinomas. The AI was trained to categorize tumors into three distinct tiers of aggressiveness based on morphological features extracted through advanced image processing algorithms. Subsequent validation utilized an independent test set of 300 images, comparing the AI’s predictive accuracy directly against evaluations performed by a cohort of seven experienced dermatologists.
The study, published in the Journal of the American Academy of Dermatology, revealed that the AI model’s performance in differentiating tumor aggressiveness was statistically indistinguishable from that of the expert clinicians. Intriguingly, the interobserver variability among dermatologists themselves was only moderate, highlighting inherent subjectivity and complexity in human assessments. These findings illustrate that AI can standardize and potentially enhance diagnostic reliability in preoperative settings, offering consistent and swift decision support.
Among the salient clinical indicators identified as correlating strongly with aggressive tumor phenotypes were the presence of ulcerations and flat, non-elevated skin surfaces. Tumors with these characteristics exhibited more than double the likelihood of belonging to higher aggressiveness categories. This insight underscores the importance of integrating specific morphological cues into computational models to refine prognostic accuracy.
Despite mounting enthusiasm for AI applications in dermatology, practical integration into routine clinical workflows has been limited to date. Polesie emphasizes that successful adoption hinges upon targeting AI development toward well-defined clinical problems where enhanced decision-making can tangibly improve patient outcomes. “Our focus has been the preoperative assessment of suspected skin cancers, an area ripe for AI’s capabilities. While our model requires further validation and optimization, its value lies in augmenting—not replacing—clinical expertise,” he asserts.
The AI employed convolutional neural networks (CNNs), a deep learning architecture adept at recognizing intricate spatial patterns within images. By training on a sizeable and diverse image repository gathered between 2015 and 2023 at the Sahlgrenska University Hospital dermatology department, the system learned to discern subtle textural and color variations indicative of tumor biology. This data-driven approach represents a leap beyond traditional diagnostic heuristics, harnessing computational power to harness cellular and tissue-level heterogeneity captured visually.
From a technical perspective, the model’s success rests on sophisticated preprocessing steps—such as normalization, segmentation, and augmentation—that enhance image quality while mitigating noise and variability inherent to clinical photography. The subsequent feature extraction phase employs filters designed to capture edges, gradients, and color contrasts associated with pathological changes. Finally, classification layers map these features onto clinically relevant aggressiveness labels, delivering probabilistic outputs that clinicians can interpret alongside their assessments.
Looking forward, the integration of this AI tool into teledermatology platforms and mobile diagnostic applications could democratize access to expert-level evaluation, particularly in resource-limited settings or remote areas. Rapid, noninvasive tumor characterization facilitated by image-based algorithms might reduce unnecessary surgeries or expedite intervention for high-risk lesions, substantially improving patient care efficiency.
Given the complexity and variability of cSCC presentation, additional studies encompassing larger, multi-center image datasets and diverse patient populations are essential. Moreover, longitudinal analyses linking AI-predicted aggressiveness with actual clinical outcomes will validate the prognostic utility of these computational assessments.
In conclusion, this research from the University of Gothenburg marks a pivotal step toward harnessing artificial intelligence as an adjunct diagnostic tool in dermatological oncology. By matching expert dermatologist performance in gauging squamous cell carcinoma aggressiveness through noninvasive imaging, the AI model demonstrates immense potential to refine surgical planning, optimize resource allocation, and ultimately improve prognosis for thousands of patients afflicted by this widespread cancer. Continued technological refinement paired with clinical validation will pave the way for AI’s meaningful integration into standard dermatological practice.
Subject of Research: People
Article Title: Assessing Differentiation in Cutaneous Squamous Cell Carcinoma: A Machine Learning Approach
News Publication Date: 1-Aug-2025
Web References:
10.1016/j.jdin.2025.07.004
References:
Journal of the American Academy of Dermatology
Image Credits: Photo: Johan Wingborg
Keywords: artificial intelligence, squamous cell carcinoma, skin cancer, dermatology, machine learning, tumor aggressiveness, convolutional neural networks, preoperative assessment, medical imaging, cancer diagnostics
Tags: accuracy of AI in medicineAI in dermatologychronic UV exposure effects on skinclinical decision-making in oncologycutaneous squamous cell carcinomadermatological advancements in Swedenimpact of AI on healthcareoncogenic mutations in keratinocytespreoperative assessment of skin cancerskin cancer diagnosticsskin cancer prevalence in SwedenUV radiation and skin cancer
