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Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and
Victor Liang1,2, Parasto Shahrouki1, Daniel Nejad1
1Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Background:
Basal cell carcinoma (BCC) is the most common skin cancer, and accurate histopathologic risk stratification is essential for treatment selection. Convolutional neural networks (CNNs) demonstrate promise in image analysis, yet few studies have evaluated classification of BCC subtypes.
Objective:
To assess the diagnostic performance of a CNN for binary classification of high-versus low-risk histopathologic subtypes of BCC and compare it with dermatologists.
Methods:
In this retrospective single-center study, 1580 dermoscopic images of histopathologically confirmed BCCs were used to train a preconditioned CNN. A test set (n = 249) was evaluated by 9 dermatologists. Performance was assessed using area under the receiver operating characteristic curve (AUC ROC). Logistic regression assessed associations between predefined tumor features and histopathologic subtype.
Results:
The CNN achieved an AUC of 0.75 (95% confidence interval [CI], 0.69-0.81) comparable to the dermatologists (AUC 0.79, 95% CI: 0.73-0.84, P = .18). Bumpy surface topography, clinical and dermoscopic ulceration and ill-defined border as well as dermoscopic focused vessels and white porcelain areas were associated with high-risk BCC, whereas dermoscopic unfocused vessels, erosions, and pigmentation were associated with low-risk BCC.
Limitations:
Retrospective single-center design and limited metadata for the CNN.
Conclusion:
CNNs appear useful for binary classification of BCC histopathologic subtypes.
