Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

TL;DR

This paper introduces a novel deep learning-based discovery radiomics framework using multi-column radiomic sequencers for skin cancer detection, achieving dermatologist-level sensitivity and specificity on a large clinical image dataset.

Contribution

It presents a new deep multi-column radiomic sequencer approach for high-throughput discovery of custom features tailored to skin cancer phenotypes, surpassing traditional hand-crafted methods.

Findings

Achieved 91% sensitivity in skin cancer detection.

Achieved 75% specificity, comparable to dermatologists.

Validated on over 9,000 clinical images.

Abstract

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis.