Declining Clinical Utility of Tools for Predicting Sentinel Lymph Node Biopsy Status: A Single Institution Experience from 2000 to 2021.

Introduction: Clinicopathologic data-based sentinel lymph node (SLN) prediction models are used to select patients with melanoma for sentinel lymph node biopsy (SLNB). However, the temporal performance of these models is unknown. Therefore, we investigated whether the performance and clinical utility of the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center, and Friedman et al.

Baseline dermoscopic patterns predict long-term changes in nevus diameter and in dermoscopic features.

Background: When monitoring melanocytic neoplasms, the pattern of change may distinguish nevi from melanoma. Anticipating the growth dynamics of nevi based on their dermoscopic pattern is important to make this distinction.

Objective: The primary aim was to examine the association between nevus dermoscopic pattern at baseline and diameter change during long-term monitoring.

Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.

Background: While the high accuracy of reported AI tools for melanoma detection is promising, the lack of holistic consideration of the patient is often criticized. Along with medical history, a dermatologist would also consider intra-patient nevi patterns, such that nevi that are different from others on a given patient are treated with suspicion.

Objective: To evaluate whether patient-contextual lesion-images improves diagnostic accuracy for melanoma in a dermoscopic image-based AI competition and a human reader study.