The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer

OVOL1はヒト皮膚の内毛根鞘，脂腺，汗腺とその腫瘍に発現している

OVOL1 is an important transcription factor for epidermal keratinization, which suppresses proliferation and switches on the differentiation of keratinocytes. A recent genome-wide association study has revealed that OVOL1 is one of the genes associated with susceptibility to atopic dermatitis. Although it is known to be expressed in murine skin and hair follicles, no investigations have focused on its localization in human skin. In the present study, we thus immunolocalized the expression of OVOL1 in normal and diseased human skin. In normal human skin, OVOL1 was preferentially expressed in the suprabasal layer of the epidermis, inner root sheath of hair, mature sebocytes and the ductal portion of the eccrine glands. Compared to this, no remarkable change in the expression of OVOL1 was observed among inflammatory skin diseases. The expression of OVOL1 was evident in eccrine poroma and hidradenoma. Moreover, it was overexpressed in Bowen\u27s disease and sebaceous adenoma, in sharp contrast to its downregulation in their more malignant counterparts, squamous cell carcinoma and sebaceous carcinoma. OVOL1 may play an important role in human skin morphogenesis and tumorigenesis.OVOL1は表皮細胞の増殖を抑制し角化を推進させる転写因子と考えられている．最近のgenome-wide association studyでは，アトピー性皮膚炎の疾患感受性遺伝子の一つとしても注目を浴びている．マウスではOvol1は皮膚及び毛囊に発現していることが報告されているがヒトでの研究はこれまで報告されていない．本研究では，ヒト健常皮膚および皮膚疾患で, OVOL1の発現を免疫組織学的に明らかにした．健常皮膚では，OVOL1は基底層上層の表皮細胞，毛囊の内毛根鞘，成熟した脂腺細胞，エクリン汗腺の導管部に優位に発現していた．炎症性皮膚疾患のOVOL1の発現は健常皮膚に比べ変化を認めなかった．OVOL1の発現はeccrine poromaとhidradenomaで亢進していた．ボーエン病と脂腺腫ではOVOL1の発現は亢進していたが，有棘細胞癌や脂腺癌ではむしろ減少していた．OVOL1はヒト皮膚の器官形成や腫瘍発生に重要な役割をになっているのではないかと考えた