Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide images

Abstract

Background

Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans.

Objective

To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions.

Methods

The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method.

Results

The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < .05).

Conclusion

This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.

Keywords:

• Melanocytic lesion
• pathological diagnosis
• whole slide image
• deep learning

Acknowledgements

We are grateful to all study participants for their contributions.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This work was supported by the Scientific Research Project of Shanghai Municipal Health Commission [grant number 202040432], the Cross Research Fund Project of Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine [grant number JYJC201903], the National Natural Science Foundation of China [grant number 81871439], the Key Research and Development Program of Shandong Province (grant number 2021SFGC0104), the Key Research and Development Program of Jiangsu Province (grant number BE2021663), the Jiangsu Province Engineering Research Center of Diagnosis and Treatment of Children's Malignant Tumor, and the Shandong Province Natural Science Foundation [grant number ZR2020QF019].