Precision Diagnosis Of Melanoma And Other Skin Lesions From Digital Images

Melanoma will affect an estimated 73,000 new cases this year and result in 9,000 deaths, yet precise diagnosis remains a serious problem. Without early detection and preventative care, melanoma can quickly spread to become fatal (Stage IV 5-year survival rate is 20-10%) from a once localized skin lesion (Stage IA 5- year survival rate is 97%). There is no biomarker for melanoma in clinical use, and the current diagnostic criteria for skin lesions remains subjective and imprecise. Accurate diagnosis of melanoma relies on a histopathologic gold standard; thus, aggressive excision of melanocytic skin lesions has been the mainstay of treatment. It is estimated that 36 biopsies are performed for every melanoma confirmed by pathology among excised lesions. There is significant morbidity in misdiagnosing melanoma such as progression of the disease for a false negative prediction vs the risks of unnecessary surgery for a false positive prediction. Every year, poor diagnostic precision adds an estimated $673 million in overall cost to manage the disease. Currently, manual dermatoscopic imaging is the standard of care in selecting atypical skin lesions for biopsy, and at best it achieves 90% sensitivity but only 59% specificity when performed by an expert dermatologist. Many computer vision (CV) algorithms perform better than dermatologists in classifying skin lesions although not significantly so in clinical practice. Meanwhile, open source deep learning (DL) techniques in CV have been gaining dominance since 2012 for image classification, and today DL can outperform humans in classifying millions of digital images with less than 5% error rates. Moreover, DL algorithms are readily run on commoditized hardware and have a strong online community of developers supporting their rapid adoption. In this work, we performed a successful pilot study to show proof of concept to DL skin pathology from images. However, DL algorithms must be trained on very large labelled datasets of images to achieve high accuracy. Here, we begin to assemble a large imageset of skin lesions from the UCSF and the San Francisco Veterans Affairs Medical Center (VAMC) dermatology clinics that are well characterized by their underlying pathology, on which to train DL algorithms. If trained on sufficient data, we hypothesize that our approach will significantly outperform general dermatologists in predicting skin lesion pathology. We posit that our work will allow for precision diagnosis of melanoma from widely available digital photography, which may optimize the management of the disease by decreasing unnecessary office visits and the significant morbidity and cost of melanoma misdiagnosis.