Fig. 3: Short-term lesion change detection and metastasis prognosis results.

a, SDDI1 dataset (n = 585 dermoscopic images) statistics: ratio of changed lesions, ratio of changed malignant lesions during follow-up, and follow-up time distribution. b, Ratio of changed lesions in the SDDI2 dataset (n = 458 dermoscopic images). c, Ablation study on preprocessing methods using SDDI1 and SDDI2 ‘Default’ (direct input), ‘With warp’ (registration only), ‘With mask’ (lesion segmentation) and ‘With whole pipeline’ (complete preprocessing as in Extended Data Fig. 3). For change detection in SDDI1 and SDDI2, all models were evaluated using the whole preprocessing pipeline. d, Performance of binary metastasis prediction (control versus metastasis) in ComBineMel (n = 680 dermoscopic images) by AUROC. e, Scheme of PanDerm for melanoma metastasis and prognosis prediction. MS, metastasis. f, Distribution of metastasis types in the ComBineMel dataset (n = 680 dermoscopic images). g, Kaplan–Meier curves for the RFI in invasive melanoma patients (ComBineMel (n = 305 patients)), stratified by PanDerm prediction scores. h, Forest plot of HRs for PanDerm; stratified groups in invasive melanoma patients. i, Time-dependent AUC of PanDerm versus clinical variable score combinations in ComBineMel. j, Time-dependent AUC comparison of PanDerm and other pretrained models in ComBineMel. The error bars in c, d, i and j and error bands in g show 95% CIs; the bar centers indicate means. All estimates were derived from fivefold cross-validation. P values in d were derived from two-sided t-tests and those in h from Wald tests within Cox proportional hazards models. Icons in e from Flaticon.com.