Diagnosis Of Nonmelanoma Skin Cancer Research Articles

A miniaturized dual band terahertz metamaterial based absorber as a biosensor for non-melanoma skin cancer diognostic

Early diagnosis of Non-melanoma skin cancers (NMSCs) is most important for successful treatment of the disease.The detection of NMSCs involves visual inspection or invasive method of detection by skin biopsy. Recently, terahertz spectroscopy has come into limelight for detection of biomarkers in low terahertz (THz) region in between 0.1 – 10 THz which will be in resonance with the biomolecules. This work entails in designing a microscale THz metamaterial absorber for discriminating the attributes between Non-melanoma skin cancerous skin and normal skin. The proposed absorber is composed of space filling curved aluminum layer over a polymide substrate. The absorber culminates its absorbance peak of 99.8 % at 0.503 THz and 99.5 % at 1.076 THz, revealing its dual band trait. Moreover, variation of refractive index of the medium from 1.3 to 1.4 shows shift in absorption peak with a sensitivity of 95.76 GHz/RIU and 100 GHz/RIU for first and second band respectively.

Impact of the COVID-19 Pandemic on the Surgical Management of Head and Neck Non-Melanoma Skin Cancers in a Maxillofacial Center of Cluj-Napoca.

Background: The COVID-19 era has been a bleak period for both cancer and non-cancer patients, with delayed non-emergency treatments, such as for non-melanoma skin cancer (NMSC). This study aimed to evaluate how the treatment of NMSC patients was influenced by the management of the COVID-19 pandemic in an Eastern European Maxillofacial Surgery center. Materials and Methods: A total of 176 patients with a histopathological diagnosis of head and neck NMSC who were surgically treated in Cluj-Napoca Emergency County Hospital between 2016 and 2022 were included in this study, and divided into two samples, pre-pandemic (2016-2019) and COVID-19 (2020-2022) periods. Results: The pandemic presented with a decrease of 46.15% in patients' hospitalization, with wealthy and educated patients being prevalent. Even if the waiting time for surgery was increased, the stage of cancer and preference method for reconstruction did not differ. Despite the lower addressability of NMSC patients during the pandemic, there were no changes in surgical treatment. Conclusions: During COVID-19, the number of patients was reduced, with a longer waiting time for surgery, but without any changes in tumor stage and treatment preferences. However, the benefit of removing a cancer tumor is higher compared to the risk of developing COVID-19 infection during hospitalization.

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Prior Nonmelanoma Skin Cancer is Associated with Fewer Fractures, More Vitamin D Sufficiency, Greater Bone Mineral Density, and Improved Bone Microarchitecture in Older Adults

BackgroundPrior nonmelanoma skin cancer (NMSC), a biomarker of cumulative lifetime sun exposure, is associated with reduced fracture risk later in life. The mechanism is unknown. MethodsProspective cohort analysis of 1099 community-dwelling adults aged 50-80 years with baseline and 10-year follow-up assessments. Histopathologically-confirmed NMSC diagnosis was established by linkage with the Tasmanian Cancer Registry. Bone mineral density (BMD) and vertebral deformity were quantified by DXA, 25-hydroxyvitamin D (25(OH)D) by radioimmunoassay, bone microarchitecture by high-resolution peripheral quantitative CT, melanin density by spectrophotometry, and skin photosensitivity and clinical fracture by questionnaire. 25(OH)D <50 nmol/L was considered deficient. ResultsParticipants with an NMSC reported prior to baseline were less likely to sustain an incident vertebral deformity over 10 years (RR = 0.74, P = .036). There were similar reductions for other fracture types but these did not reach significance. Prior NMSC was associated with baseline (RR = 1.23, P = .005) and 10-year longitudinal (RR = 5.9, P = .014) vitamin D sufficiency and greater total body BMD (β = 0.021g/cm2, P = .034), but not falls risk or muscle strength. The relationship between prior NMSC and bone microarchitecture was age-dependent (pinteraction < 0.05). In the oldest age tertile, prior NMSC was associated with greater volumetric BMD (β = 57.8-62.6, P = .002-0.01) and less porosity (β = −4.6 to −5.2, P = .002-0.009) at cortical, compact cortical and outer transitional zones. ConclusionsPrior NMSC was associated with fewer incident fractures in community-dwelling older adults. This protective association is most likely mediated by modifiable fracture risk factors associated with an outdoor lifestyle, including 25(OH)D, BMD, and bone microarchitecture.

Pigmented nonmelanoma skin cancers of the genital area: adiagnostic and therapeutic challenge - monocentric experience and review of the literature.

Nonmelanoma skin cancers (NMSC), comprising basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are typically encountered on photo-exposed skin. Nevertheless, several cases of NMSC have been described in covered areas such as the genital region; furthermore, some of these lesions may express a variable degree of pigmentation. Due to the existence of mucosal melanoma, an accurate diagnosis is paramount. In this narrative review, we focused our attention on management and - in particular- diagnosis of pigmented NMSC (pNMSC) located in the genital region, emphasizing the features assessed by dermoscopy and reflectance confocal microscopy. As an implementation, we included data on pNMSC from the Dermatology Unit of the University of Campania Vanvitelli. BCC in the genital region represents only 1% of all BCC cases. It has been supposed that the mutation of patched 1 may lead to the development of BCC even without concomitant UV exposure. Pigmented variants on genitals have seldom been described. More prominent dermoscopic features seem to be blue-gray ovoid nests and arborizing vessels associated with whitish structureless areas. SCC and Bowen's disease (BD) - a variant of in situ SCC - may be encountered in the genital area and are sometimes associated with human papillomavirus (HPV) infection. Pigmented SCC is very rare, and most of the literature is focused on pigmented BD (pBD), which is mainly characterized by gray-brown dots in a linear fashion and glomerular vessels without evident scales. In conclusion, pNMSC is rarely encountered on genitals; evaluation with dermoscopy or other ancillary devices like RCM is important both to exclude benign lesions like seborrheic keratosis and lentigo and to rule out melanoma.

Intraoperative Flow Cytometry for the Rapid Diagnosis and Validation of Surgical Clearance of Non-Melanoma Skin Cancer: A Prospective Clinical Feasibility Study.

Non-melanoma skin cancer (NMSC) is the most prevalent cancer in humans, with a high global incidence. We present a prospective clinical feasibility study on the use of intraoperative flow cytometry (iFC) for the instant diagnosis of NMSC and its complete surgical clearance. Flow cytometry, a laser-based technique, quantifies cell features, which has applications in cancer research. This study aim is to explore the potential applicability of iFC in detecting and characterizing NMSC and its surgical margins. In total, 30 patients who underwent diagnosis for NMSC were recruited. The method demonstrated high sensitivity (95.2%) and specificity (87.1%), with an accuracy of 91.1%, as confirmed with a receiver operating characteristic curve analysis. The results also indicated that most tumors were diploid, with two cases being hypoploid. The average G0/G1 fractions for normal and tumor tissue samples were 96.03 ± 0.30% and 88.03 ± 1.29%, respectively, with the tumor index escalating from 3.89 ± 0.30% to 11.95 ± 1.29% in cancerous cells. These findings underscore iFC's capability for precise intraoperative NMSC characterization and margin evaluation, promising enhanced complete tumor excision rates. Given the technique's successful application in various other malignancies, its implementation in NMSC diagnosis and treatment holds significant promise and warrants further research in clinical trials.

Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review

Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

Frequency and features of malignant tumors clinically mimicking cutaneous cysts: A retrospective chart review.

Many cutaneous lesions are clinically suspected as "cyst"; however, following histopathological examination, are found to be more significant lesions. Here, we examine the frequency and features of malignancies with cutaneous cysts in the clinical differential. A retrospective study of surgical pathology specimens at the James A. Haley Veterans' Hospital from January 2018 to December 2022 was conducted. Cutaneous specimens containing the clinical diagnosis of "cyst" were included. The clinicopathological features were summarized. Premalignant or malignant neoplasms accounted for 4.5% of all specimens submitted with cysts in the clinical differential. Most cyst-mimicking cancers were basal cell carcinoma (BCC) or squamous cell carcinoma (SCC); however, cancers with poorer prognoses, such as Merkel cell carcinoma and melanoma, also clinically masqueraded as cysts. The BCCs were predominately nodular, and the SCCs were largely well-differentiated and invasive. Many exhibited clinical signs and symptoms compatible with benign cysts, such as central punctum, pain, and rapid growth. Identified risk factors included history of prior non-melanoma skin cancer diagnosis, previous excision, and immunosuppression. Many lesions clinically concerning cutaneous cysts were found to be malignancies following histopathological review. Accordingly, following biopsy all cyst-like lesions should be examined microscopically, especially in certain clinical contexts in which the incidence of skin cancer is increased.

Clinician’s Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy

Introduction: Non-melanoma skin cancer (NMSC) is a cause of significant morbidity and mortality in high-risk individuals. Total body photography (TBP) is currently used to monitor melanocytic lesions in patients with high risk for melanoma. The authors examined if three-dimensional (3D)-TBP could be useful for diagnosis of NMSC. Methods: Patients (n = 129; 52 female, 77 male) with lesions suspicious for NMSC who had not yet had a biopsy underwent clinical examination followed by examination of each lesion with 3D-TBP Vectra^®WB360 (Canfield Scientific, Parsippany, NJ, USA) and dermoscopy. Results: The 129 patients had a total of 182 lesions. Histological examination was performed for 158 lesions; the diagnoses included basal cell carcinoma (BCC; n = 107), squamous cell carcinoma (SCC; n = 27), in-situ SCC (n = 15). Lesions were located in the head/neck region (n = 138), trunk (n = 21), and limbs (n = 23). Of the 182 lesions examined, 12 were not visible on 3D-TBP; reasons for not being visible included location under hair and on septal of nose. Two lesions appeared only as erythema in 3D-TBP but were clearly identifiable on conventional photographs. Sensitivity of 3D-TBP was lower than that of dermoscopy for BCC (73% vs. 79%, p = 0.327), higher for SCC (81% vs. 74%, p = 0.727), and lower for in-situ SCC (0% vs. 33%, p = 125). Specificity of 3D-TBP was lower than that of dermoscopy for BCC (77% vs. 82%, 0.581), lower for SCC (75% vs. 84%, p = 0.063), and higher for in-situ SCC (97% vs. 94%, p = 0.344). Diagnostic accuracy of 3D-TBP was lower than that of dermoscopy for BCC (75% vs. 80%), lower for SCC (76% vs. 82%), and lower for in-situ SCC (88% vs. 89%). Lesion location was not associated with diagnostic confidence in dermoscopy (p = 0.152) or 3D-TBP (p = 0.353). If only lesions with high confidence were included in the calculation, diagnostic accuracy increased for BCC (n = 27; sensitivity 85%, specificity 85%, diagnostic accuracy 85%), SCC (n = 10; sensitivity 90%, specificity 80%, diagnostic accuracy 83%), and for in-situ SCC (n = 2; sensitivity 0%, specificity 100%, diagnostic accuracy 95%). Conclusion: Diagnostic accuracy appears to be slightly lower for 3D-TBP in comparison to dermoscopy. However, there is no statistically significant difference in the sensitivity and specificity of 3D-TBP and dermoscopy for NMSC. Diagnostic accuracy increases, if only lesions with high confidence are included in the calculation. Further studies are necessary to determine if 3D-TBP can improve management of NMSC.

Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Carcinomas: Real-Life Data over Three Years.

Line-field confocal optical coherence tomography (LC-OCT) can help the clinical diagnosis of skin diseases. The present study aimed to evaluate the sensitivity, specificity, and diagnostic accuracy of LC-OCT for the diagnosis of the most frequent non-melanoma skin cancers (NMSCs), i.e., basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Comparing LC-OCT diagnostic performances with those of dermoscopy, histopathological examination was used as a gold standard. For every study endpoint, the diagnostic ability of LC-OCT revealed superiority over the dermoscopic examination. In particular, a significant increase in specificity was observed. Sensitivity, specificity, and diagnostic accuracy of dermoscopy and LC-OCT for the diagnosis of malignancy were, respectively, 0.97 (CI 0.94-0.99), 0.43 (CI 0.36-0.51), and 0.77 (CI 0.72-0.81) for dermoscopy and 0.99 (CI 0.97-1.00), 0.90 (CI 0.84-0.94), and 0.96 (CI 0.93-0.97) for LC-OCT. The positive predictive value (PPV) resulted in 0.74 (CI 0.69-0.78) for dermoscopy and 0.94 (CI 0.91-0.97) for LC-OCT, and the negative predictive value (NPV) was 0.89 (CI 0.81-0.95) for dermoscopy and 0.98 (CI 0.95-1.00) for LC-OCT. Finally, our real-life study showed a potentially important role of LC-OCT in the non-invasive diagnosis of NMSCs, especially BCC. The real-time imaging technique could spare unnecessary biopsies with an increased sensitivity, a much higher specificity, and better accuracy than clinical assessment with dermoscopy alone.

BI31 Hereditary haemochromatosis: a risk factor for nonmelanoma skin cancer?

Abstract Hereditary haemochromatosis (HH) is an autosomal recessive disease resulting in excessive levels of iron. In a recent study, in a US population of 448 patients diagnosed with HH, a positive association between HH and nonmelanoma skin cancers (NMSCs) was observed (Pan CX, Yang K, Lau CB et al. Non-melanoma skin cancer in patients with hereditary hemochromatosis: a case–control study. J Am Acad Dermatol 2023; 88:692–4). The authors also reported that, among those who required more frequent venesection, the risk of basal cell carcinoma (BCC) was particularly elevated. In terms of the possible pathomechanism, the authors noted that oxidative stress represents a key component in the pathogenesis of both HH and BCC. Ireland has the highest prevalence of HH in the world, with research suggesting approximately 1 in 83 people is predisposed. For this reason, a study was undertaken to determine if a similar association could be observed in an Irish population. A single-centre retrospective cross-sectional analysis was undertaken on a database of patients with haemochromatosis attending the venesection department of a tertiary referral centre over 25 years (1997–2022). All histologically diagnosed NMSCs within this patient group were then recorded. Ethical approval was granted by the local hospital ethics committee. There were a total 1480 patients in the database, 65% Male (n = 956). Five were excluded due to insufficient data. Thus, 1475 patients were analysed, 64% male (n = 949). The mean current age was 60 years (range: 19–100). Only 52/1475 patients (3.5%) had a histologically diagnosed NMSC, 52% male (n = 27). HH genetic mutation: C282Y homozygous (n = 29); H63D homozygous (n = 5); C282Y/H63D compound heterozygous (n = 11); and data not recorded (n = 7). Mean ferritin at HH diagnosis 575 (range: 0–8413). Forty-three patients were diagnosed with a BCC, 58% male (n = 25), with a total of 94 BCCs. Mean age was 67 years at first NMSC diagnosis. Histological classification: nodular (n = 48); superficial (n = 19); unclassified (n = 18); infiltrative (n = 5); micronodular (n = 3); and basosquamous (n = 1). Eleven patients were diagnosed with a squamous cell carcinoma (SCC), with a total of 21 SCCs. This study had limitations. It was a single-centre, retrospective study analysing only histologically confirmed NMSCs. It is acknowledged that a small proportion of NMSCs may be diagnosed clinically, specifically superficial BCCs without histological confirmation. Only records with histology available in our institution were recorded, and it is possible that NMSCs were excised in other institutions, potentially leading to an under-representation of NMSC incidence. The proportion of patients diagnosed with NMSCs in this cohort was relatively low, in contrast to the findings reported by Pan et al. This study indicates that HH is unlikely to represent an independent risk factor.

P63 Effectiveness of an image-analysing artificial intelligence-based digital health technology to diagnose nonmelanoma skin cancer and benign skin lesions

Abstract Squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) are common types of nonmelanoma skin cancer (NMSC). The DERM-003 study was a prospective, multicentre, single-arm, masked study that aimed to demonstrate the effectiveness of an artificial intelligence-based digital health technology (AI-DHT) to identify SCC, BCC and premalignant conditions in dermoscopic images of suspicious skin lesions. Patients with at least one suspicious skin lesion that was suitable for photography were eligible. Each lesion was photographed with three smartphone cameras (iPhone 6S, iPhone 11 and Samsung 10) with a dermoscopic lens attached. Each image was assessed by the AI-DHT. A clinical diagnosis was made by the dermatologist, and histopathology results were obtained for biopsied lesions. The AI-DHT output was compared with the histopathology diagnosis, and the area under the receiver operating characteristic curve (AUROC) was calculated. Secondary endpoints included other diagnostic measures and assessment of premalignant lesions. Altogether, 572 patients (49.5% female, mean age 68.5 years, 96.9% Fitzpatrick skin types I–III) were recruited from four UK National Health Service trusts, providing images of 611 suspicious lesions. There were no exclusion criteria relating to skin type. In total, 592 lesions had images from all three cameras available; only one lesion had no images available. Altogether, 395 lesions had a histopathology result. Forty-seven biopsied lesions were diagnosed as SCC and 184 as BCC. The AUROCs for images taken by the iPhone 6S were 0.88 [95% confidence interval (CI) 0.83–0.93] for SCC and 0.87 (95% CI 0.84–0.91) for BCC. For Samsung 10, the AUROCs were 0.85 (95% CI 0.79–0.90) and 0.87 (95% CI 0.83–0.90), and for iPhone 11, they were 0.88 (95% CI 0.84–0.93) and 0.89 (95% CI 0.86–0.92) for SCC and BCC, respectively. Using images taken on iPhone 6S of biopsied-only lesions, the sensitivity rates in detecting SCC and BCC were 98% (95% CI 88–100) and 94% (95% CI 90–97), respectively; the specificity rates were 38% (95% CI 33–44) and 28% (95% CI 21–35), respectively; the positive predictive values were 17% (95% CI 16–19) and 59% (95% CI 56–61), respectively; and the NPVs were 99% (95% CI 95–100) and 81% (95% CI 70–89), respectively. All 12 lesions diagnosed as Bowen disease were classified correctly by the AI-DHT. Of the 61 lesions diagnosed as actinic keratosis, 52 were correctly classified of the 22 lesions diagnosed as dysplastic and 18 were correctly classified by the AI-DHT. All 16 lesions diagnosed as melanoma were classified as such by the AI-DHT. The AI-DHT has the potential to support the diagnosis of NMSC and premalignant lesions.

BT03 (P63) Effectiveness of an image-analysing artificial intelligence-based digital health technology to diagnose nonmelanoma skin cancer and benign skin lesions

Abstract Squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) are common types of nonmelanoma skin cancer (NMSC). The DERM-003 study was a prospective multicentre single-arm masked study that aimed to demonstrate the effectiveness of an artificial intelligence-based digital health technology (AI-DHT) to identify SCC, BCC and premalignant conditions in dermoscopic images of suspicious skin lesions. Patients with at least one suspicious skin lesion that was suitable for photography were eligible. Each lesion was photographed with three smartphone cameras (iPhone 6S, iPhone 11 and Samsung 10) with a dermoscopic lens attached. Each image was assessed by the AI-DHT. A clinical diagnosis was made by the dermatologist, and histopathology results were obtained for biopsied lesions. The AI-DHT output was compared with the histopathology diagnosis, and the area under the receiver operating characteristic curve (AUROC) was calculated. Secondary endpoints included other diagnostic measures and assessment of premalignant lesions. Altogether, 572 patients (49.5% women, mean age 68.5 years, 96.9% Fitzpatrick skin types I–III) were recruited from four UK National Health Service trusts, providing images of 611 suspicious lesions. There were no exclusion criteria relating to skin type. In total, 592 lesions had images from all three cameras available; only one lesion had no images available. Altogether, 395 lesions had a histopathology result. Forty-seven biopsied lesions were diagnosed as SCC and 184 as BCC. The AUROCs for images taken by the iPhone 6S was 0.88 [95% confidence interval (CI) 0.83–0.93] for SCC and 0.87 (95% CI 0.84–0.91) for BCC. For the Samsung 10, the AUROCs were 0.85 (95% CI 0.79–0.90) and 0.87 (95% CI 0.83–0.90), and for the iPhone 11, they were 0.88 (95% CI 0.84–0.93) and 0.89 (95% CI 0.86–0.92) for SCC and BCC, respectively. Using images taken on the iPhone 6S of biopsied only lesions, the sensitivity in detecting SCC and BCC was 98% (95% CI 88–100) and 94% (95% CI 90–97), respectively; the specificity was 38% (95% CI 33–44) and 28% (95% CI 21–35), respectively; the positive predictive value was 17% (95% CI 16–19) and 59% (95% CI 56–61), respectively; and the NPV was 99% (95% CI 95–100) and 81% (95% CI 70–89), respectively. All 12 lesions diagnosed as Bowen disease were classified correctly by the AI-DHT. Of the 61 lesions diagnosed as actinic keratosis, 52 were correctly classified of the 22 lesions diagnosed as dysplastic, and 18 were correctly classified by the AI-DHT. All 16 lesions diagnosed as melanoma were classified as such by the AI-DHT. The AI-DHT has the potential to support the diagnosis of NMSC and premalignant lesions.

Abstract 24: Comparison Between the White Skin Color Distribution on the Brazil and Diagnoses of Nonmelanoma Skin Cancer

Abstract Purpose: According to the National Institute of Cancer (INCA), people with lighter skin tone have a higher chance of being stricken by Nonmelanoma skin cancer. Still, according to the same institute, this cancer is the most frequent tumor for both genders in Brazil. Therefore, it’s important to measure how impactful this risk factor is in Brazil. Methods: In this analysis, the data about the diagnosis of nonmelanoma skin cancer between the years of 2013 to 2021 in the different regions of Brazil was captured using the DATASUS Oncology Panel as data source, through the TABNET system. Also the data about the resident population of each region by color and race uncovered in the last census realized by the Brazilian Institute of Geography and Statistic (IBGE) in 2010 has been collected. This information has been correlated with the statistics about the tumor, using the Pearson coefficient as the method to measure the relation between these two variables. Results: The region that has the highest number of white people is the southeast region, with 44.152.518 people, followed by the south, with 21.456.204. The region with the lowest number of white habitants is the north region, with 3.686.144 individuals. The number of diagnoses of nonmelanoma skin cancer between the years of 2013-2021 follows a similar logic, with the highest number of cases in the southeast and south region (88.851 and 70.046 respectively). In this way, correlating both variables it has been found a determination coefficient (r2) of 0.943 and a p-value= 0.016, that demonstrate the significance of the relation. Conclusion: It’s evident that a direct relation between the number of nonmelanoma skin cancer diagnoses and the population of white skin per location in Brazil exists, as indicated by relevant value of the determination coefficient founded (r2 = 0.943), as well as the p-value &amp;lt; 0.05. So, the data show that, in Brazil, the lighter skin tone is an important risk factor associated with the tumor referred to, being this information important to subsidize future campaigns to prevent the disease. Citation Format: Arthur Minas Alberti, Lucas Kieling, Pedro Antonio Paludo Menna Barreto, Pedro Arthur Zimmer Silveira, Rafaela de Andrade, Fernando Azambuja, Luiza Seixas de Sá Beltramo, Gabriele Eckerdt Lech, Laura Tibola Marques da Silva, Maria Eduarda Corrêa Pereira. Comparison Between the White Skin Color Distribution on the Brazil and Diagnoses of Nonmelanoma Skin Cancer [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 24.

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer.

Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon is due in part to domain shift, wherein differences in test-site pre-analytical variables (e.g., slide scanner, staining procedure) result in WSI with notably different visual presentations compared to training data. To ameliorate pre-analytic variances, approaches such as CycleGAN can be used to calibrate visual properties of images between sites, with the intent of improving DL classifier generalizability. In this work, we present a new approach termed Multi-Site Cross-Organ Calibration based Deep Learning (MuSClD) that employs WSIs of an off-target organ for calibration created at the same site as the on-target organ, based off the assumption that cross-organ slides are subjected to a common set of pre-analytical sources of variance. We demonstrate that by using an off-target organ from the test site to calibrate training data, the domain shift between training and testing data can be mitigated. Importantly, this strategy uniquely guards against potential data leakage introduced during calibration, wherein information only available in the testing data is imparted on the training data. We evaluate MuSClD in the context of the automated diagnosis of non-melanoma skin cancer (NMSC). Specifically, we evaluated MuSClD for identifying and distinguishing (a) basal cell carcinoma (BCC), (b) in-situ squamous cell carcinomas (SCC-In Situ), and (c) invasive squamous cell carcinomas (SCC-Invasive), using an Australian (training, n=85) and a Swiss (held-out testing, n=352) cohort. Our experiments reveal that MuSCID reduces the Wasserstein distances between sites in terms of color, contrast, and brightness metrics, without imparting noticeable artifacts to training data. The NMSC-subtyping performance is statistically improved as a result of MuSCID in terms of one-vs. rest AUC: BCC (0.92vs 0.87, p=0.01), SCC-In Situ (0.87vs 0.73, p=0.15) and SCC-Invasive (0.92vs 0.82, p=1e-5). Compared to baseline NMSC-subtyping with no calibration, the internal validation results of MuSClD (BCC (0.98), SCC-In Situ (0.92), and SCC-Invasive (0.97)) suggest that while domain shift indeed degrades classification performance, our on-target calibration using off-target tissue can safely compensate for pre-analytical variabilities, while improving the robustness of the model.

Same Day Biopsy and Treatment of Non-Melanoma Skin Cancer in Patients with Field Cancerization: A Retrospective Chart Study.

Patients with field cancerization will develop numerous superficial non-melanoma skin cancers (NMSCs). Treating patients with field cancerization can be challenging and burdensome due to the numerous non-melanoma skin cancers (NMSCs) they develop and the frequent dermatology visits required for biopsy and treatment. The success rate of diagnosing and treating lesions suspicious for NMSCs on the same day is measured, immediately after biopsy. We retrospectively reviewed records of patients with same day lesion diagnosis and curettage treatment to determine diagnostic accuracy, treatment failure, and number needed to treat to reduce a follow-up treatment. A total of 237 lesions underwent same day biopsy and treatment, of which the majority were NMSC (66%) or actinic keratosis (23%). Patients had at least 3 months and a median of 17 months follow-up. A total of 20 lesions either recurred or were deemed to require additional treatment. The number needed to treat (NNT) to prevent one follow-up treatment was 1.3. Limitations: sample size limited ability to determine risk factors for treatment failure. Simultaneous diagnosis and treatment of superficial NMSCs is a successful way of improving efficiency and patient satisfaction.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy.

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

High Prevalence of Beta-Human Papilloma Virus in Patients with Concurrent Chronic Lymphocytic Leukemia and Non-Melanoma Skin Cancer

Introduction Patients with chronic lymphocytic leukemia (CLL) are at least twice as likely to develop a second malignancy as their age-matched peers, which is likely secondary chronic CLL-related immunosuppression (Royle 2011). Non-melanoma skin cancers (NMSC) are the most common type of second cancer and those with CLL are seven times more likely to have recurrent NMSC than those without CLL (Mehrany 2005). The etiology of NMSC is likely multifactorial, but we hypothesized that viral load of beta-subtype of human papillomavirus (B-HPV) is correlated with likelihood of NMSC in patients with CLL. Virtually all of the currently available commercial HPV testing targets alpha-subtypes of HPV, but B-HPV has recently been associated with NMSC's (Rollison 2021). The prevalence and viral load of B-HPV in patients with CLL has not been studied. Methods We collected blood samples from patients with CLL and non-CLL controls (patients with solid malignancies) through the ORIEN AVATAR program and performed whole exome sequencing. We used Kraken2 metagenomic software to identify co-purifying HPV DNA using a custom-built viral database composed of unique sequences from alpha and beta HPV reference genomes. Comparison of viral load between patients with and without CLL, and CLL patients with and without NMSC was performed by a 2 sample t-test. Statistic significance was set as p<0.05. Results We measured B-HPV viral loads in the peripheral blood of 333 CLL patients and 69 non-CLL control patients. We determined that the average B-HPV viral load in the peripheral blood of CLL patients was double the load in our non-CLL controls (2724.9 versus 1315.0 viral counts; p<0.001). When we classified the B-HPV subtypes, we found higher viral loads of the subtypes 8, 15, 110, 143, 150 in CLL patients than non-CLL controls (p<0.001 for all CLL vs control comparisons). B-HPV subtype 8 (B-HPV8) was the subtype with the highest viral load as determined by exact matches to our Kraken2 HPV database. We identified 29 CLL patients with known diagnosis of NMSC. In this population, the viral load of B-HPV8 was >6-fold higher than that found in the non-CLL control patients (1009.8 vs 149.6 viral sequences; p<0.001; 2-sample t-test). Conclusions These preliminary data support our hypothesis that B-HPV infection is associated with NMSC in CLL patients. At minimum, it may serve as a biomarker for level of immunosuppression, which predisposes to NMSC. We are currently enrolling a prospective clinical trial for CLL patients with recurrent NMSC (NCT04844528). We plan to sequence both the peripheral blood and the skin cancer samples from these patients to determine whether there is a correlation between B-HPV subtype and NMSC occurrence. We will also evaluate components of the immune system such as T-cell function in an attempt to quantify and correlate immune dysfunction in these patients.

107 DermTech smart stickers can non-invasively detect RNAs that are associated with non-melanoma skin cancer

The most common types of skin cancers are non-melanoma skin cancers (NMSC), including both: basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). NMSC’s primarily form on sun exposed skin areas and are responsible for >15,000 deaths each year in the US alone. Current diagnosis of NMSC relies on an in-depth visual assessment of suspicious lesions followed by a surgical skin biopsy for histopathologic review. This study investigated whether non-invasive collection of skin tissue and subsequent genomic analysis could properly classify NMSC.

TGF-β/VEGF-A Genetic Variants Interplay in Genetic Susceptibility to Non-Melanocytic Skin Cancer.

Differential genetically determined expression of transforming growth factor-β (TGF-β pathway and of vascular endothelial growth factor-A (VEGF-A) might modulate the molecular “milieu” involved in the etio-pathogenesis of non-melanoma skin cancer (NMSC). We have evaluated the frequency of some functionally relevant SNPs of TGF-β and VEGF-A genes in 70 NMSC patients and 161 healthy controls, typed for TGF-β1 rs1800471, TGF-β2 rs900, TGF-βR1 rs334348 and rs334349, TGF-βR2 rs4522809 and VEGF-A rs3025039 SNPs. TGF-βR2 rs1800629G allele and related genotypes were found to be associated with a possible protective role against NMSC, whereas VEGF-A rs3025039T was associated with an increased risk. To evaluate the effect of genotype combinations on NMSC susceptibility, we determined the frequencies of 31 pseudo-haplotypes due to non-random linkage among alleles of loci not lying on the same chromosome. Two pseudo-haplotypes that imply a minor allele of TGF-βR2 or minor allele of VEGF-A SNPs combined with major alleles of the other SNPs were, respectively, associated with a protective effect, and susceptibility to NMSC. In addition, a pseudo-haplotype involving minor alleles of TGF-β2 rs900, TGF-βR1 rs334348 and rs4522809 SNPs might be a susceptibility marker for NMSC. In conclusion, our data suggest that a complex interplay among the genetic polymorphisms of TGF-β, TGF-β receptors and VEGF-A genes might influence the net effect of genetic background of the patients on NMSC development. This might be relevant in the risk evaluation, diagnosis and treatment of NMSC.