皮肤黑色素瘤是全球第17大最常见的癌症。早期发现可疑皮肤病变(黑色素瘤)可使5年生存率提高20%。7点检查表(7PCL)已被广泛用于建议可能患有黑色素瘤的患者的紧急转诊。然而,7PCL方法仅考虑7个meta特征来计算风险评分,并且仅与疑似黑色素瘤患者相关.关于广泛使用患者元数据来检测所有皮肤癌亚型的研究有限。这项研究调查了人工智能(AI)模型,该模型利用由23个属性组成的患者元数据进行可疑皮肤病变检测。我们已经确定了一组新的最重要的风险因素,即“C4C危险因素”,不仅仅是黑色素瘤,而是针对所有类型的皮肤癌.将用于可疑皮肤病变检测的C4C危险因素的性能与7PCL和预测黑色素瘤终生风险的Williams危险因素的性能进行比较。我们提出的AI框架整合了五个机器学习模型,并识别了七个新的皮肤癌风险因素:病变粉红色,病变大小,病变颜色,病变发炎,病变形状,病变年龄,和自然的头发颜色,当使用从英国不同皮肤癌诊断诊所收集的53,601个皮肤病变的元数据进行评估时,其检测可疑皮肤病变的灵敏度为80.46±2.50%,特异性为62.09±1.90%,显著优于基于7PCL的方法(灵敏度68.09±2.10%,特异性61.07±0.90%)和威廉姆斯危险因素(敏感性66.32±1.90%,特异性61.71±0.6%)。此外,通过加权七个新的风险因素,我们得出了一个新的风险评分,即“C4C风险评分”,单独的灵敏度为76.09±1.20%,特异性为61.71±0.50%,显著优于基于7PCL的风险评分(灵敏度73.91±1.10%,特异性49.49±0.50%)和Williams风险评分(敏感性60.68±1.30%,特异性60.87±0.80%)。最后,将C4C危险因素与7PCL和Williams危险因素融合,取得了最佳表现,敏感性为85.24±2.20%,特异性为61.12±0.90%。我们认为,将这些新发现的风险因素和新的风险评分与图像数据融合,将进一步提高可疑皮肤病变检测的AI模型性能。因此,一组新的皮肤癌危险因素有可能被用来修改目前所有皮肤癌亚型的皮肤癌转诊指南,包括黑色素瘤.
Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for patients with a possible melanoma. However, the 7PCL method only considers seven meta-features to calculate a risk score and is only relevant for patients with suspected melanoma. There are limited studies on the extensive use of patient
metadata for the detection of all skin cancer subtypes. This study investigates artificial intelligence (AI) models that utilise patient
metadata consisting of 23 attributes for suspicious skin lesion detection. We have identified a new set of most important risk factors, namely \"C4C risk factors\", which is not just for melanoma, but for all types of skin cancer. The performance of the C4C risk factors for suspicious skin lesion detection is compared to that of the 7PCL and the Williams risk factors that predict the lifetime risk of melanoma. Our proposed AI framework ensembles five machine learning models and identifies seven new skin cancer risk factors: lesion pink, lesion size, lesion colour, lesion inflamed, lesion shape, lesion age, and natural hair colour, which achieved a sensitivity of 80.46 ± 2.50 % and a specificity of 62.09 ± 1.90 % in detecting suspicious skin lesions when evaluated using the
metadata of 53,601 skin lesions collected from different skin cancer diagnostic clinics across the UK, significantly outperforming the 7PCL-based method (sensitivity 68.09 ± 2.10 % , specificity 61.07 ± 0.90 % ) and the Williams risk factors (sensitivity 66.32 ± 1.90 % , specificity 61.71 ± 0.6 % ). Furthermore, through weighting the seven new risk factors we came up with a new risk score, namely \"C4C risk score\", which alone achieved a sensitivity of 76.09 ± 1.20 % and a specificity of 61.71 ± 0.50 % , significantly outperforming the 7PCL-based risk score (sensitivity 73.91 ± 1.10 % , specificity 49.49 ± 0.50 % ) and the Williams risk score (sensitivity 60.68 ± 1.30 % , specificity 60.87 ± 0.80 % ). Finally, fusing the C4C risk factors with the 7PCL and Williams risk factors achieved the best performance, with a sensitivity of 85.24 ± 2.20 % and a specificity of 61.12 ± 0.90 % . We believe that fusing these newly found risk factors and new risk score with image data will further boost the AI model performance for suspicious skin lesion detection. Hence, the new set of skin cancer risk factors has the potential to be used to modify current skin cancer referral guidelines for all skin cancer subtypes, including melanoma.