RESUMO
Since December 2019, the COVID-19 pandemic has profoundly affected healthcare. The real effects of the COVID-19 pandemic on skin cancer are still unclear, more than 3 years later. This study aims to summarise the pandemic's impact on skin cancer diagnosis and outcome. A systematic review and meta-analysis was conducted, selecting studies comparing skin cancer diagnosis and prognosis post-pandemic with pre-pandemic data. A total of 27 papers were reviewed including 102,263 melanomas and 271,483 keratinocyte carcinomas. During the initial pandemic months (January-July 2020), melanoma surgeries dropped by 29.7% and keratinocyte carcinomas surgeries by 50.8%. Early pandemic tumours exhibited greater thickness and stage. In a long-term period beyond the initial months, melanoma surgeries decreased by 9.3%, keratinocyte carcinomas by 16.6%. No significant differences were observed in the Breslow thickness of melanomas after the start of the pandemic (mean difference 0.06, 95% confidence interval -0.46, 0.58). Melanomas operated on post-pandemic onset had an increased risk of ulceration (odds ratio 1.35, 95% confidence interval 1.22-1.50). Keratinocyte carcinomas showed increased thickness and worsened stage post-pandemic. However, studies included were mostly retrospective and cross-sectional, reporting diverse data. This review indicates that the pandemic likely caused delays in skin cancer diagnosis and treatment, potentially impacting patient outcomes.
Assuntos
COVID-19 , Queratinócitos , Melanoma , Neoplasias Cutâneas , Humanos , COVID-19/epidemiologia , Neoplasias Cutâneas/epidemiologia , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/diagnóstico , Neoplasias Cutâneas/cirurgia , Melanoma/epidemiologia , Melanoma/cirurgia , Melanoma/diagnóstico , Queratinócitos/patologia , SARS-CoV-2 , Prognóstico , Estadiamento de NeoplasiasRESUMO
BACKGROUND: Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting. OBJECTIVES: To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence. METHODS: A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total-body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study-defined elevated CNN risk-scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0-10). RESULTS: Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI-based skin cancer screening compared to examination by AI or dermatologist alone (3D-TBP: 61.3%; 2D-TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean-SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D-TBP: 90.2%; 2D-TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI-assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN-classification more than personal malignancy suspicion (n = 3/205). CONCLUSIONS: While patients already prefer AuI with 3D-TBP for melanoma recognition, dermatologists continue to rely largely on their own decision-making despite high confidence in AI-results. TRIAL REGISTRATION: ClinicalTrials.gov (NCT04605822).