Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death

Clin Cancer Res. 2020 Mar 1;26(5):1126-1134. doi: 10.1158/1078-0432.CCR-19-1495. Epub 2019 Oct 21.


Purpose: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction.

Experimental design: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS).

Results: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001).

Conclusions: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Area Under Curve
  • Biopsy / methods
  • Deep Learning / standards*
  • Disease Progression
  • Female
  • Follow-Up Studies
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / standards*
  • Male
  • Melanoma / mortality*
  • Melanoma / pathology*
  • Middle Aged
  • Neoplasm Recurrence, Local / mortality*
  • Neoplasm Recurrence, Local / pathology*
  • Neural Networks, Computer
  • Retrospective Studies
  • Risk Factors
  • Staining and Labeling / methods*
  • Survival Rate
  • Young Adult