ABSTRACT
BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer in the Caucasian population. Currently, invasive biopsy is the only way of establishing the histological subtype (HST) that determines the treatment options. Our study aimed to evaluate whether optically guided high-frequency ultrasound (OG-HFUS) imaging could differentiate aggressive HST BCCs from low-risk tumors. METHODS: We conducted prospective clinical and dermoscopic examinations of BCCs, followed by 33 MHz OG-HFUS imaging, surgical excision, and a histological analysis. We enrolled 75 patients with 78 BCCs. In total, 63 BCCs were utilized to establish a novel OG-HFUS risk classification algorithm, while 15 were employed for the validation of this algorithm. The mean age of the patients was 72.9 ± 11.2 years. Histology identified 16 lesions as aggressive HST (infiltrative or micronodular subtypes) and 47 as low-risk HST (superficial or nodular subtypes). To assess the data, we used a one-sided Fisher's exact test for a categorical analysis and a Receiver Operating Characteristic (ROC) curve analysis to evaluate the diagnostic accuracy. RESULTS: OG-HFUS distinguished aggressive BCC HSTs by their irregular shape (p < 0.0001), ill-defined margins (p < 0.0001), and non-homogeneous internal echoes (p = 0.004). We developed a risk-categorizing algorithm that differentiated aggressive HSTs from low-risk HSTs with a higher sensitivity (82.4%) and specificity (91.3%) than a combined macroscopic and dermoscopic evaluation (sensitivity: 40.1% and specificity: 73.1%). The positive and negative predictive values (PPV and NPV, respectively) for dermoscopy were 30.2% and 76.8%, respectively. In comparison, the OG-HFUS-based algorithm demonstrated a PPV of 94.7% and an NPV of 78.6%. We verified the algorithm using an independent image set, n = 15, including 12 low-risk and 3 high-risk (high-risk) with two blinded evaluators, where we found a sensitivity of 83.33% and specificity of 91.66%. CONCLUSIONS: Our study shows that OG-HFUS can identify aggressive BCC HSTs based on easily identifiable morphological parameters, supporting early therapeutic decision making.
ABSTRACT
BACKGROUND: Each brain hemisphere plays a specialized role in cognitive and behavioral processes, known as hemispheric lateralization. In chronic skin diseases, such as plaque psoriasis (Pso) and atopic dermatitis (AD), the degree of lateralization between the frontal hemispheres may provide insight into specific connections between skin diseases and the psyche. This study aims to analyze the hemispherical lateralization, neurovegetative responses, and psychometric characteristics of patients with Pso and AD. METHODS: The study included 46 patients with Pso, 56 patients with AD, and 29 healthy control (Ctrl) subjects. The participants underwent frontal electroencephalogram (EEG) measurement, heart rate variability (HRV) assessment, and psychological tests. Statistical analyses were performed using ANOVA, with Bonferroni correction applied for multiple comparisons. RESULTS: This study shows a significant right-lateralized prefrontal activity in both AD patients (p < 0.001) and Pso patients (p = 0.045) compared with Ctrl, with no significant difference between the AD and Pso groups (p = 0.633). AD patients with right-hemispheric dominant prefrontal activation exhibited increased inhibition and avoidance markers, while Pso patients showed elevated sympathetic nervous system activity. CONCLUSION: Psychophysiological and psychometric data suggest a shared prevalence of right-hemispheric dominance in both AD and Pso patient groups. However, the findings indicate distinct psychodermatological mechanisms in AD and Pso.
ABSTRACT
Melanoma is the most aggressive form of skin cancer that is known for its metastatic potential and has an increasing incidence worldwide. Breslow thickness, which determines the staging and surgical margin of the tumor, is unavailable at initial diagnosis. Novel imaging techniques for assessing Breslow thickness lack comparative data. This study evaluates optically guided high-frequency ultrasound (OG-HFUS) and multispectral imaging (MSI) for preoperative estimation of Breslow thickness and staging. We enrolled 101 patients with histologically confirmed primary melanoma and categorized them based on tumor thickness. Optically guided 33 MHz HFUS and MSI were utilized for the assessment. Our MSI-based algorithm categorized melanomas into three subgroups with a sensitivity of 62.6%, specificity of 81.3%, and fair agreement (κ = 0.440, CI: 0.298-0.583). In contrast, OG-HFUS demonstrated a sensitivity of 91.8%, specificity of 96.0%, and almost perfect agreement (κ = 0.858, CI: 0.763-0.952). OG-HFUS performed better than MSI in estimating Breslow thickness, emphasizing its potential as a valuable tool for melanoma diagnosis and patient management. OG-HFUS holds promise for enhancing preoperative staging and treatment decision-making in melanoma.
ABSTRACT
Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s' could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1-2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58-0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39-0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.
