dataset
posted on 2023-03-31, 21:23 authored by Prathamesh M. Kulkarni, Eric J. Robinson, Jaya Sarin Pradhan, Robyn D. Gartrell-Corrado, Bethany R. Rohr, Megan H. Trager, Larisa J. Geskin, Harriet M. Kluger, Pok Fai Wong, Balazs Acs, Emanuelle M. Rizk, Chen Yang, Manas Mondal, Michael R. Moore, Iman Osman, Robert Phelps, Basil A. Horst, Zhe S. Chen, Tammie Ferringer, David L. Rimm, Jing Wang, Yvonne M. Saenger Supplementary Data from Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
Funding
NIH
Melanoma Research Alliance
Columbia University's Irving Institute for Clinical and Translational Research
Swim Across America
Yale SPORE in Lung Cancer
Yale Cancer Center
History
ARTICLE ABSTRACT
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.
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).
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).
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.
