A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Veronica Rotemberg,David Gutman,Noel Codella,Pascale Guitera,Harald Kittler,Katsumi Kose ,Nicholas Kurtansky,Liam J Caffery ,Steve G Langer ,Philipp Tschandl,Stephen W Dusza ,Konstantinos Lioprys,Marc Combalia,Allan C Halpern ,Alexander J Stratigos ,Jabpani Nanda,Ofer Reiter,Shenara Musthaq,Josep Malvehy,Brigid Betz‐Stablein ,Jochen Weber,George Shih,Emmanouil Chousakos,Brian Helba,H Peter Soyer

doi:10.1038/s41597-021-00815-z

Abstract

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Highlights

Background & SummaryArtificial intelligence (AI) use in medical imaging is rapidly progressing and has the potential to reduce melanoma-associated mortality, morbidity, and healthcare costs by improving access to expertise, diagnostic accuracy, and screening efficiency[1,2,3]
We present a dermatology image dataset that includes patient- and lesion-related clinical context, which can be used in studies to examine whether this additional information further improves recognition performance
Algorithms derived from the 2018 ISIC Grand Challenge have been shown to outperform over 500 clinical readers and experts in such a reader study[1]

Summary

Introduction

Background & SummaryArtificial intelligence (AI) use in medical imaging is rapidly progressing and has the potential to reduce melanoma-associated mortality, morbidity, and healthcare costs by improving access to expertise, diagnostic accuracy, and screening efficiency[1,2,3]. We present a dermatology image dataset that includes patient- and lesion-related clinical context, which can be used in studies to examine whether this additional information further improves recognition performance. This dataset is composed of 33126 images collected from 2056 patients at multiple centers around the world such as Memorial Sloan Kettering Cancer Center, New York; the Melanoma Institute Australia and the Melanoma Diagnosis Centre, Sydney; the University of Queensland, Brisbane; the Medical University of Vienna, Vienna; and Hospital Clínic de Barcelona, Barcelona.

Results

Conclusion