Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning

Abstract Background There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)‐stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly‐supervised deep‐learning approach, SmartProg‐MEL, to predict survival outcomes in stages I to III melanoma patients from HE‐stained whole slide image (WSI). Methods We designed a deep neural network that extracts morphological features from WSI to predict 5‐y overall survival (OS), and assign a survival risk score to each patient. The model was trained and validated on a discovery cohort of primary cutaneous melanomas (IHP‐MEL‐1, n = 342). Performance was tested on two external and independent datasets (IHP‐MEL‐2, n = 161; and TCGA cohort n = 63). It was compared with well‐established prognostic factors. Concordance index (c‐index) was used as a metric. Results On the discovery cohort, the SmartProg‐MEL predicts the 5‐y OS with a c‐index of 0.78 on the cross‐validation data and of 0.72 on the cross‐testing series. In the external cohorts, the model achieved a c‐index of 0.71 and 0.69 for the IHP‐MEL‐2 and TCGA dataset respectively. Furthermore, SmartProg‐MEL was an independent and the most powerful prognostic factor in multivariate analysis (HR = 1.84, p ‐value < 0.005). Finally, the model was able to dichotomize patients in two groups—a low and a high‐risk group—each associated with a significantly different 5‐y OS ( p ‐value < 0.001 for IHP‐MEL‐1 and p ‐value = 0.01 for IHP‐MEL‐2). Conclusion The performance of our fully automated SmartProg‐MEL model outperforms the current clinicopathological factors in terms of prediction of 5‐y OS and risk stratification of cutaneous melanoma patients. Incorporation of SmartProg‐MEL in the clinical workflow could guide the decision‐making process by improving the identification of patients that may benefit from adjuvant therapy.