Deep learning reveals prognostic information beyond AJCC stage in routine histopathology of early-stage melanoma: the population-based D-ESMEL study

Of all melanoma patients who develop distant metastasis over time, 41% were initially diagnosed with stage I/II. More accurate prognostic models are needed to select patients for potential (neo)adjuvant therapy. We hypothesise that histopathological features in whole slide images (WSIs) from primary early-stage melanomas provide additional information beyond American Joint Committee on Cancer (AJCC) stage. We utilised data from the population-based Dutch Early-Stage Melanoma (D-ESMEL) study. This included a discovery set (n=328) of case-control sets matched on follow-up time and known prognost

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