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Abstract The 7-point checklist (7PCL) is recommended by NICE to select patients with pigmented lesions and possible melanoma for urgent referral. This research investigates the potential of using machine learning (ML) models that utilize patient metadata from a teledermatology pathway including 7PCL for suspicious skin lesion detection, and compares the performance gain for each meta-feature added with the 7PCL. We analysed clinical metadata from 53 601 skin lesions in 25 105 patients who attended private skin cancer diagnosis clinics in the UK between 2015 and 2022. For each lesion, we included the following meta-features: 7PCL (change of lesion size, shape, colour, lesion 7 mm, inflamed, oozing, itching), weighted 7PCL, Williams score characteristics (patient age, patient sex, natural hair colour, arm mole count, sunburn history, prior nonmelanoma skin cancer), the overall Williams score, prior melanoma, lesion location, lesion age, and whether this was a predominantly nonpigmented pink lesion. All lesions were categorized as suspicious (10%) or nonsuspicious (90%) by skin cancer specialists during telemedicine triage (cancer detection rate = 5%). All meta-features were used as ML model inputs to classify whether each input was associated with suspicious or nonsuspicious classes. This study integrates five ML models (naive Bayes, logistics regression, support vector machine, random forest, and multilayer perceptron) to detect suspicious skin lesions based on patient metadata. Firstly, we compared the ML model performance between the original 7PCL and weighted 7PCL in correctly identifying suspicious skin lesions. We then added each meta-feature with 7PCL and tabulated the performance gain. We utilized balanced accuracy, sensitivity, specificity and area under the curve as evaluation metrics. There were no significant differences observed in model performance between the 7PCL and weighted 7PCL (sensitivity 68.1%). However, the performance of the ML model was improved significantly for each added meta-feature, with the best performance gain (sensitivity 85.2%) observed when 11 meta-features (lesion pink, Williams score, lesion age, sunburn, patient age, Williams group, patient sex, hair colour, site of the lesion on the body, freckling tendency, and mole count) were added to the 7PCL. This research has identified the optimal subset of meta-features that improve ML model performance for categorizing suspicious skin lesions during telemedicine triage, compared with 7PCL alone. Fusing these high-performing meta-features with image modalities is likely to further boost the ML model performance for skin cancer detection, and they could also be used to modify current skin cancer referral guidelines. The study is funded and supported by Innovate UK and the private teledermatology pathway provider.
Islam et al. (Fri,) studied this question.