Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review

Abstract

Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C -index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.

Histological interpretation of spitzoid tumours: an extensive machine learning‐based concordance analysis for improving decision making

“…ML models have proved feasible in distinguishing between benign and malignant melanocytic tumours. 17 Additionally, the ANOVA analysis has proved to be a valuable tool for identifying and selecting the most significant features, enhancing the performance of ML models to achieve greater accuracy in various applications related to tumour diagnosis. [18][19][20][21] As spitzoid tumours continue to pose significant challenges in dermatopathology, this study aimed to evaluate the effectiveness of ML models in distinguishing benign from malignant tumours, as well as predicting the subclassification of the atypical intermediate category, based on 22 clinicopathological features in different cohorts diagnosed by dermatopathologists from four different countries.…”

“…ML models have proved feasible in distinguishing between benign and malignant melanocytic tumours 17 . Additionally, the ANOVA analysis has proved to be a valuable tool for identifying and selecting the most significant features, enhancing the performance of ML models to achieve greater accuracy in various applications related to tumour diagnosis 18–21 …”