Prediction of melanoma metastasis using dermatoscopy deep features: An international multicenter cohort study.
Authors: Konstantinos Lallas, Panagiota Spyridonos, Harald Kittler, Philipp Tschandl, Giuseppe Argenziano, Renato Bakos, Ralph P Braun, Horacio Cabo, Emi Dika, Josep Malvehy, Ashfaq A. Marghoob, Susana Puig, Alon Scope, Wilhelm Stolz, Masaru Tanaka, Luc Thomas, Zoe Apalla, Efstratios Vakirlis, Iris Zalaudek, Aimilios Lallas
Journal: Journal of Clinical Oncology (2024)

9565 Background: Machine learning algorithms, and especially convolutional neural networks, demonstrated modest accuracy on the prediction of melanoma metastasis, based on histological images and clinicopathological information. Whether dermatoscopy deep features could serve as biomarker for the prediction of melanoma metastasis, remains an underexplored area in medical research. Methods: An international, multicenter, cohort study of cutaneous melanoma patients from 3 different continents was conducted. Patients with cutaneous melanoma, who had available clinical and dermatoscopic images and an adequate follow-up time for the development of metastasis (both locoregional and distant) were included. We utilized a support vector machine (SVM) classifier, to distinguish between melanomas that metastasized and those that did not. We used a pre-trained ResNet 50 network, we separated dataset into training set and testing set, stratified by TNM-stage, and to ensure robustness and guard against biased data selection, the stratified split into training and testing sets was repeated five times, resulting in five different training-test sets. The primary outcome was the comparison of the prognostic performance of deep dermatoscopy features based on SVM (model 1) to the performance of established prognostic factors of melanoma, such as Breslow thickness and ulceration (model 2) and to a combined model using deep features and histopathologic factors (model 3). A secondary aim was to examine the diagnostic performance of model 1 in stage IIB and IIC patients at diagnosis. The prognostic performance was assessed using the Area Under the Curve (AUC) and the True Positive Rate (TPR) at a True Negative Rate at 70%. Results: 712 patients were included, 465 (65.3%) non-metastatic and 247 (34.7%) metastatic, within a median follow-up of 60 months. The SVM model demonstrated mean AUC 0.84 (95% CI 0.80 – 0.87) and TPR 0.81 (95%CI 0.73 – 0.90). Similar results were shown for model 2 and model 3, and no statistically significant differences among models were detected in terms of AUC (De Long’s test, p&gt;0.05 and ANOVA Kruskal-Wallis p&gt;0.05). Regarding IIB/IIC patients and combining data from five test sets, SVM correctly classified as metastatic 21 out of 23 (91.3%) stage IIB and 21 out of 23 (91.3%) stage IIC patients, who eventually developed metastasis during follow-up. Conclusions: Our findings suggest that dermatoscopy deep features could offer an immediate, in vivo prediction of melanoma metastasis prior to excision. This advancement holds significant clinical importance, prioritizing high-risk patients for neoadjuvant treatment or guiding selection of patients who might benefit from adjuvant therapy.