RESUMEN
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.
Asunto(s)
COVID-19 , Queratinocitos , Melanoma , Neoplasias Cutáneas , Humanos , COVID-19/epidemiología , Neoplasias Cutáneas/epidemiología , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/cirugía , Melanoma/epidemiología , Melanoma/cirugía , Melanoma/diagnóstico , Queratinocitos/patología , SARS-CoV-2 , Pronóstico , Estadificación de NeoplasiasRESUMEN
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).
RESUMEN
With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.
Asunto(s)
Melanoma , Nevo Pigmentado , Neoplasias Cutáneas , Humanos , Melanoma/diagnóstico por imagen , Melanoma/clasificación , Melanoma/patología , Melanoma/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/clasificación , Neoplasias Cutáneas/diagnóstico , Femenino , Nevo Pigmentado/diagnóstico por imagen , Nevo Pigmentado/diagnóstico , Nevo Pigmentado/clasificación , Nevo Pigmentado/patología , Masculino , Persona de Mediana Edad , Adulto , Proyectos Piloto , Anciano , Melanocitos/patología , Iluminación/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Sensibilidad y EspecificidadRESUMEN
AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.